sexClassifier-ngrams1-3-submit.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/em-chiu/intersection_project/blob/main/EC%20Classifier.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DSgFvHzhSPNK",
    "outputId": "fe6a4603-759f-483e-b192-e891f58f83e0"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     /Users/emilychiu/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package punkt to /Users/emilychiu/nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
      "[nltk_data]     /Users/emilychiu/nltk_data...\n",
      "[nltk_data]   Package averaged_perceptron_tagger is already up-to-\n",
      "[nltk_data]       date!\n"
     ]
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download('stopwords')\n",
    "nltk.download('punkt')\n",
    "import urllib.request\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import TweetTokenizer\n",
    "import re\n",
    "import numpy as np\n",
    "from collections import Counter\n",
    "from sklearn import preprocessing\n",
    "import string\n",
    "nltk.download('averaged_perceptron_tagger') # to solve pos feature extract issue\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "import pandas as pd # data processing\n",
    "import csv\n",
    "import sys\n",
    "import unidecode\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the data to df, try1 works\n",
    "df = pd.read_csv('sexistinfo.csv', header=None, encoding = \"ISO-8859-1\").drop(0, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>text</td>\n",
       "      <td>label</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>@MirkoPlsll @IanisRondeau ouais.. un chef d'oe...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>@MamzelleMNa Une trÃ¨s humble femme! #Ironie</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BLOGUE - Â«TsÃ©, la paritÃ© homme-femme...Â» a...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Je suis une femme matÃ©rialiste et superficiel...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   1      2\n",
       "0                                               text  label\n",
       "1  @MirkoPlsll @IanisRondeau ouais.. un chef d'oe...      0\n",
       "2       @MamzelleMNa Une trÃ¨s humble femme! #Ironie      0\n",
       "3  BLOGUE - Â«TsÃ©, la paritÃ© homme-femme...Â» a...      0\n",
       "4  Je suis une femme matÃ©rialiste et superficiel...      0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# preview data\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop first row \n",
    "# by selecting all rows from first row onwards\n",
    "df = df.iloc[1: , :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9341, 2)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# view dimensions of dataset\n",
    "df.shape\n",
    "# 9341 instances and 2 attributes in dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['tweet', 'label'], dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rename column names\n",
    "col_names = ['tweet', 'label']\n",
    "df.columns = col_names\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>@MirkoPlsll @IanisRondeau ouais.. un chef d'oe...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>@MamzelleMNa Une trÃ¨s humble femme! #Ironie</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BLOGUE - Â«TsÃ©, la paritÃ© homme-femme...Â» a...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Je suis une femme matÃ©rialiste et superficiel...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mise en ligne de mon article sur @PayeTaShnek ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               tweet label\n",
       "1  @MirkoPlsll @IanisRondeau ouais.. un chef d'oe...     0\n",
       "2       @MamzelleMNa Une trÃ¨s humble femme! #Ironie     0\n",
       "3  BLOGUE - Â«TsÃ©, la paritÃ© homme-femme...Â» a...     0\n",
       "4  Je suis une femme matÃ©rialiste et superficiel...     0\n",
       "5  Mise en ligne de mon article sur @PayeTaShnek ...     0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# preview after renaming\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9341 entries, 1 to 9341\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   tweet   9340 non-null   object\n",
      " 1   label   9330 non-null   object\n",
      "dtypes: object(2)\n",
      "memory usage: 146.1+ KB\n"
     ]
    }
   ],
   "source": [
    "# summary of data\n",
    "df.info()\n",
    "# no missing data\n",
    "# Categorical variables have data type object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 2 categorical variables\n",
      "\n",
      "The categorical variables are :\n",
      "\n",
      " ['tweet', 'label']\n"
     ]
    }
   ],
   "source": [
    "# find categorical variables\n",
    "categorical = [var for var in df.columns if df[var].dtype=='O']\n",
    "\n",
    "print('There are {} categorical variables\\n'.format(len(categorical)))\n",
    "\n",
    "print('The categorical variables are :\\n\\n', categorical)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>tweet</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>@MirkoPlsll @IanisRondeau ouais.. un chef d'oe...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>@MamzelleMNa Une trÃ¨s humble femme! #Ironie</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BLOGUE - Â«TsÃ©, la paritÃ© homme-femme...Â» a...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Je suis une femme matÃ©rialiste et superficiel...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mise en ligne de mon article sur @PayeTaShnek ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               tweet label\n",
       "1  @MirkoPlsll @IanisRondeau ouais.. un chef d'oe...     0\n",
       "2       @MamzelleMNa Une trÃ¨s humble femme! #Ironie     0\n",
       "3  BLOGUE - Â«TsÃ©, la paritÃ© homme-femme...Â» a...     0\n",
       "4  Je suis une femme matÃ©rialiste et superficiel...     0\n",
       "5  Mise en ligne de mon article sur @PayeTaShnek ...     0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# view the categorical variables\n",
    "df[categorical].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tweet     1\n",
       "label    11\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check missing values in categorical variables\n",
    "df[categorical].isnull().sum()\n",
    "\n",
    "# drop values (in processing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace = True)\n",
    "# drop a rows whose all data is missing or contain null values(NaN) \n",
    "# in place changes df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 9330 entries, 1 to 9341\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   tweet   9330 non-null   object\n",
      " 1   label   9330 non-null   object\n",
      "dtypes: object(2)\n",
      "memory usage: 218.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()\n",
    "# after dropping rows w/ nulls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Le 25 janvier se tiendra la premiÃ¨re JournÃ©e contre le sexisme #EnsembleContreLeSexisme https://t.co/AeT5xrm83W via @latribune                                                                                                                                                                                          8\n",
      "@frhaz @partisocialiste @RachidTemal Le test du sondage par le tweet inutile aprÃ¨s 10j de silence est echec Freddy ! #Degage #BalanceTonPorc                                                                                                                                                                             6\n",
      "#journeedesdroitsdesfemmes \\nVoici le flot d immondices que reÃ§oit @Simonnet2 sur Twitter \\nÃ gerber ð¤¢\\n#Balancetonporc https://t.co/7tm8VsLICh                                                                                                                                                                      4\n",
      "ð´@Monier_eric qui dirige aujourd'hui la rÃ©daction de @LCI est accusÃ© par 14 journalistes d'harcÃ¨lement sexuel. â¶ï¸https://t.co/8MGOq5rC2v https://t.co/zxR4HEpdjb                                                                                                                                                4\n",
      "Eric Monier, directeur de la rÃ©daction de #LCI, attaquÃ© en justice pour harcÃ¨lement sexuel et moral. #BalanceTonPorc https://t.co/NbpkeLG0U2                                                                                                                                                                           4\n",
      "                                                                                                                                                                                                                                                                                                                         ..\n",
      "j'adhÃ¨re, excellent article.\\n#sexisme #balancetonporc #feminisme https://t.co/I7gEZD9S54                                                                                                                                                                                                                                1\n",
      "Eh bas moi je trouve au contraire que #Friends est en fait... rÃ©aliste.\\nOn dÃ©nonce aujrd des situations problÃ©matiques (homo/transphobie, #sexisme, #grossophobie...).\\nOn aurait pas Ã  les dÃ©noncer si elles n'existaient pas, si elles n'avaient pas existÃ©s par expl : dans les 90's https://t.co/pWImBM4Id6    1\n",
      "Nouveau â¡ Guide #Ã©ducation Ã  la sexualitÃ© par #UNESCO pr conseils, informations auprÃ¨s des jeunes ðð pr favoriser #libertÃ© #EgaliteFH #santÃ© #Ã©mancipation ðºð¹ et pr combattre #violences liÃ©es au #sexisme #genre #harcÃ¨lement #VIH...\\n\\nhttps://t.co/zJM4m3g4ed https://t.co/BUmQjUqvxW          1\n",
      "Ai croisÃ© une femme d'une cinquante d'annÃ©es dans le mÃ©tro. Elle Ã©tait d'un indicible charme avec ses cheveux poivre et sel. Quelques sourires partagÃ©s. J'ai osÃ© un \"bonne journÃ©e\" au sortir du mÃ©tro. Sourires. Tt n'est pas perdu dans ce climat hystÃ©rique #sexisme #harcellement                           1\n",
      "@Admir_fdlrc @cardoso_lisa @nadine__morano au bout dâun moment nvm. vous Ãªtes lÃ  vous gonflez les gens h37 oh. quand câest des bonnes soeurs qui se voilent vous lâouvrez pas votre bouche. chaque femme est libre de se vetir comme elle veut putain. avec votre racisme mal cachÃ© bande de haineux.            1\n",
      "Name: tweet, Length: 8587, dtype: int64\n",
      "0    6330\n",
      "1    3000\n",
      "Name: label, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# view frequency counts of values in categorical variables\n",
    "for var in categorical: \n",
    "    print(df[var].value_counts())\n",
    "\n",
    "#0    6330\n",
    "#1    3000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Le 25 janvier se tiendra la premiÃ¨re JournÃ©e contre le sexisme #EnsembleContreLeSexisme https://t.co/AeT5xrm83W via @latribune                                                                                                                                                                                          0.000857\n",
      "@frhaz @partisocialiste @RachidTemal Le test du sondage par le tweet inutile aprÃ¨s 10j de silence est echec Freddy ! #Degage #BalanceTonPorc                                                                                                                                                                             0.000643\n",
      "#journeedesdroitsdesfemmes \\nVoici le flot d immondices que reÃ§oit @Simonnet2 sur Twitter \\nÃ gerber ð¤¢\\n#Balancetonporc https://t.co/7tm8VsLICh                                                                                                                                                                      0.000429\n",
      "ð´@Monier_eric qui dirige aujourd'hui la rÃ©daction de @LCI est accusÃ© par 14 journalistes d'harcÃ¨lement sexuel. â¶ï¸https://t.co/8MGOq5rC2v https://t.co/zxR4HEpdjb                                                                                                                                                0.000429\n",
      "Eric Monier, directeur de la rÃ©daction de #LCI, attaquÃ© en justice pour harcÃ¨lement sexuel et moral. #BalanceTonPorc https://t.co/NbpkeLG0U2                                                                                                                                                                           0.000429\n",
      "                                                                                                                                                                                                                                                                                                                            ...   \n",
      "j'adhÃ¨re, excellent article.\\n#sexisme #balancetonporc #feminisme https://t.co/I7gEZD9S54                                                                                                                                                                                                                                0.000107\n",
      "Eh bas moi je trouve au contraire que #Friends est en fait... rÃ©aliste.\\nOn dÃ©nonce aujrd des situations problÃ©matiques (homo/transphobie, #sexisme, #grossophobie...).\\nOn aurait pas Ã  les dÃ©noncer si elles n'existaient pas, si elles n'avaient pas existÃ©s par expl : dans les 90's https://t.co/pWImBM4Id6    0.000107\n",
      "Nouveau â¡ Guide #Ã©ducation Ã  la sexualitÃ© par #UNESCO pr conseils, informations auprÃ¨s des jeunes ðð pr favoriser #libertÃ© #EgaliteFH #santÃ© #Ã©mancipation ðºð¹ et pr combattre #violences liÃ©es au #sexisme #genre #harcÃ¨lement #VIH...\\n\\nhttps://t.co/zJM4m3g4ed https://t.co/BUmQjUqvxW          0.000107\n",
      "Ai croisÃ© une femme d'une cinquante d'annÃ©es dans le mÃ©tro. Elle Ã©tait d'un indicible charme avec ses cheveux poivre et sel. Quelques sourires partagÃ©s. J'ai osÃ© un \"bonne journÃ©e\" au sortir du mÃ©tro. Sourires. Tt n'est pas perdu dans ce climat hystÃ©rique #sexisme #harcellement                           0.000107\n",
      "@Admir_fdlrc @cardoso_lisa @nadine__morano au bout dâun moment nvm. vous Ãªtes lÃ  vous gonflez les gens h37 oh. quand câest des bonnes soeurs qui se voilent vous lâouvrez pas votre bouche. chaque femme est libre de se vetir comme elle veut putain. avec votre racisme mal cachÃ© bande de haineux.            0.000107\n",
      "Name: tweet, Length: 8587, dtype: float64\n",
      "0    0.678457\n",
      "1    0.321543\n",
      "Name: label, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# view frequency distribution of categorical variables\n",
    "for var in categorical:   \n",
    "    print(df[var].value_counts()/float(len(df)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    6330\n",
       "1    3000\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # check frequency distribution of values in label variable\n",
    "df.label.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1       @mirkoplsll @ianisrondeau ouais.. un chef d'oe...\n",
       "2            @mamzellemna une trã¨s humble femme! #ironie\n",
       "3       blogue - â«tsã©, la paritã© homme-femme...â» a...\n",
       "4       je suis une femme matã©rialiste et superficiel...\n",
       "5       mise en ligne de mon article sur @payetashnek ...\n",
       "                              ...                        \n",
       "9337    #sã©golã¨neroyal a participã© activement au dã...\n",
       "9338    depuis l'affaire dsk, les fã©ministes ne veule...\n",
       "9339    analogie. câest comme dire ã  une femme, vou...\n",
       "9340    si t'as une bite a la place du coeur, t'ã©tonn...\n",
       "9341    @admir_fdlrc @cardoso_lisa @nadine__morano au ...\n",
       "Name: lowered_tweet, Length: 9330, dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lowercase all words\n",
    "def make_lower(a_string):\n",
    "    return a_string.lower()\n",
    "\n",
    "df['lowered_tweet'] = df['tweet'].apply(make_lower)\n",
    "df['lowered_tweet']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>tweet</th>\n",
       "      <th>label</th>\n",
       "      <th>lowered_tweet</th>\n",
       "      <th>tokenized_tweet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>@MirkoPlsll @IanisRondeau ouais.. un chef d'oe...</td>\n",
       "      <td>0</td>\n",
       "      <td>@mirkoplsll @ianisrondeau ouais.. un chef d'oe...</td>\n",
       "      <td>[@mirkoplsll, @ianisrondeau, ouais, .., un, ch...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>@MamzelleMNa Une trÃ¨s humble femme! #Ironie</td>\n",
       "      <td>0</td>\n",
       "      <td>@mamzellemna une trã¨s humble femme! #ironie</td>\n",
       "      <td>[@mamzellemna, une, trã, ¨, s, humble, femme, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BLOGUE - Â«TsÃ©, la paritÃ© homme-femme...Â» a...</td>\n",
       "      <td>0</td>\n",
       "      <td>blogue - â«tsã©, la paritã© homme-femme...â» a...</td>\n",
       "      <td>[blogue, -, â, «, tsã, ©, ,, la, paritã, ©, ho...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Je suis une femme matÃ©rialiste et superficiel...</td>\n",
       "      <td>0</td>\n",
       "      <td>je suis une femme matã©rialiste et superficiel...</td>\n",
       "      <td>[je, suis, une, femme, matã, ©, rialiste, et, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mise en ligne de mon article sur @PayeTaShnek ...</td>\n",
       "      <td>0</td>\n",
       "      <td>mise en ligne de mon article sur @payetashnek ...</td>\n",
       "      <td>[mise, en, ligne, de, mon, article, sur, @paye...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               tweet label  \\\n",
       "1  @MirkoPlsll @IanisRondeau ouais.. un chef d'oe...     0   \n",
       "2       @MamzelleMNa Une trÃ¨s humble femme! #Ironie     0   \n",
       "3  BLOGUE - Â«TsÃ©, la paritÃ© homme-femme...Â» a...     0   \n",
       "4  Je suis une femme matÃ©rialiste et superficiel...     0   \n",
       "5  Mise en ligne de mon article sur @PayeTaShnek ...     0   \n",
       "\n",
       "                                       lowered_tweet  \\\n",
       "1  @mirkoplsll @ianisrondeau ouais.. un chef d'oe...   \n",
       "2       @mamzellemna une trã¨s humble femme! #ironie   \n",
       "3  blogue - â«tsã©, la paritã© homme-femme...â» a...   \n",
       "4  je suis une femme matã©rialiste et superficiel...   \n",
       "5  mise en ligne de mon article sur @payetashnek ...   \n",
       "\n",
       "                                     tokenized_tweet  \n",
       "1  [@mirkoplsll, @ianisrondeau, ouais, .., un, ch...  \n",
       "2  [@mamzellemna, une, trã, ¨, s, humble, femme, ...  \n",
       "3  [blogue, -, â, «, tsã, ©, ,, la, paritã, ©, ho...  \n",
       "4  [je, suis, une, femme, matã, ©, rialiste, et, ...  \n",
       "5  [mise, en, ligne, de, mon, article, sur, @paye...  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer = nltk.TweetTokenizer()\n",
    "# keeps hashtags together, subset of word_tokenize\n",
    "\n",
    "df['tokenized_tweet'] = df['lowered_tweet'].apply(tokenizer.tokenize)\n",
    "df.head()\n",
    "\n",
    "# https://stackoverflow.com/questions/48363461/passing-a-pandas-dataframe-column-to-an-nltk-tokenizer\n",
    "#df['tweet'] = df['tweet'].apply(nltk.TweetTokenizer)\n",
    "# this overwrote what i did"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df['tweet'] = df['tweet'].astype(pd.StringDtype())\n",
    "\n",
    "# df['tokenized_tweet_joined'] = df['tokenized_tweet'].astype(str)\n",
    "# # must feed str into vectorizer\n",
    "\n",
    "def rejoin_string(a_list):\n",
    "    return ' '.join(a_list)\n",
    "\n",
    "\n",
    "df['tokenized_tweet_joined'] = df['tokenized_tweet'].apply(rejoin_string)\n",
    "# rejoins into string, that's been tokenized\n",
    "# holds spaces between tokens but now altogether"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>tweet</th>\n",
       "      <th>label</th>\n",
       "      <th>lowered_tweet</th>\n",
       "      <th>tokenized_tweet</th>\n",
       "      <th>tokenized_tweet_joined</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>@MirkoPlsll @IanisRondeau ouais.. un chef d'oe...</td>\n",
       "      <td>0</td>\n",
       "      <td>@mirkoplsll @ianisrondeau ouais.. un chef d'oe...</td>\n",
       "      <td>[@mirkoplsll, @ianisrondeau, ouais, .., un, ch...</td>\n",
       "      <td>@mirkoplsll @ianisrondeau ouais .. un chef d'o...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>@MamzelleMNa Une trÃ¨s humble femme! #Ironie</td>\n",
       "      <td>0</td>\n",
       "      <td>@mamzellemna une trã¨s humble femme! #ironie</td>\n",
       "      <td>[@mamzellemna, une, trã, ¨, s, humble, femme, ...</td>\n",
       "      <td>@mamzellemna une trã ¨ s humble femme ! #ironie</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BLOGUE - Â«TsÃ©, la paritÃ© homme-femme...Â» a...</td>\n",
       "      <td>0</td>\n",
       "      <td>blogue - â«tsã©, la paritã© homme-femme...â» a...</td>\n",
       "      <td>[blogue, -, â, «, tsã, ©, ,, la, paritã, ©, ho...</td>\n",
       "      <td>blogue - â « tsã © , la paritã © homme-femme ....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Je suis une femme matÃ©rialiste et superficiel...</td>\n",
       "      <td>0</td>\n",
       "      <td>je suis une femme matã©rialiste et superficiel...</td>\n",
       "      <td>[je, suis, une, femme, matã, ©, rialiste, et, ...</td>\n",
       "      <td>je suis une femme matã © rialiste et superfici...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mise en ligne de mon article sur @PayeTaShnek ...</td>\n",
       "      <td>0</td>\n",
       "      <td>mise en ligne de mon article sur @payetashnek ...</td>\n",
       "      <td>[mise, en, ligne, de, mon, article, sur, @paye...</td>\n",
       "      <td>mise en ligne de mon article sur @payetashnek ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               tweet label  \\\n",
       "1  @MirkoPlsll @IanisRondeau ouais.. un chef d'oe...     0   \n",
       "2       @MamzelleMNa Une trÃ¨s humble femme! #Ironie     0   \n",
       "3  BLOGUE - Â«TsÃ©, la paritÃ© homme-femme...Â» a...     0   \n",
       "4  Je suis une femme matÃ©rialiste et superficiel...     0   \n",
       "5  Mise en ligne de mon article sur @PayeTaShnek ...     0   \n",
       "\n",
       "                                       lowered_tweet  \\\n",
       "1  @mirkoplsll @ianisrondeau ouais.. un chef d'oe...   \n",
       "2       @mamzellemna une trã¨s humble femme! #ironie   \n",
       "3  blogue - â«tsã©, la paritã© homme-femme...â» a...   \n",
       "4  je suis une femme matã©rialiste et superficiel...   \n",
       "5  mise en ligne de mon article sur @payetashnek ...   \n",
       "\n",
       "                                     tokenized_tweet  \\\n",
       "1  [@mirkoplsll, @ianisrondeau, ouais, .., un, ch...   \n",
       "2  [@mamzellemna, une, trã, ¨, s, humble, femme, ...   \n",
       "3  [blogue, -, â, «, tsã, ©, ,, la, paritã, ©, ho...   \n",
       "4  [je, suis, une, femme, matã, ©, rialiste, et, ...   \n",
       "5  [mise, en, ligne, de, mon, article, sur, @paye...   \n",
       "\n",
       "                              tokenized_tweet_joined  \n",
       "1  @mirkoplsll @ianisrondeau ouais .. un chef d'o...  \n",
       "2    @mamzellemna une trã ¨ s humble femme ! #ironie  \n",
       "3  blogue - â « tsã © , la paritã © homme-femme ....  \n",
       "4  je suis une femme matã © rialiste et superfici...  \n",
       "5  mise en ligne de mon article sur @payetashnek ...  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df['tokenized_tweet_joined'] # list of tokenized tweets\n",
    "y = df['label'] # list of labels of tweets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split X and y into training and testing sets\n",
    "# randomizes data in splitting\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)\n",
    "# indicating random state ensures data split will be same\n",
    "#https://stackoverflow.com/questions/28064634/random-state-pseudo-random-number-in-scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((7464,), (1866,))"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the shape of X_train and X_test\n",
    "\n",
    "X_train.shape, X_test.shape\n",
    "# ((7464,), (1866,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((7464,), (1866,))"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape, y_test.shape\n",
    "#((7464,), (1866,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check data types in X_train\n",
    "\n",
    "X_train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# stopwords_fr = nltk.corpus.stopwords.words(\"french\")\n",
    "# https://stackoverflow.com/questions/57359982/remove-stopwords-in-french-and-english-in-tfidfvectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "# Initialize our vectorizer (featurizer, turning text to numbers)\n",
    "#only looking at numbers produced, creating features\n",
    "# takes each tweet turning into feature vector, each column corresponds to word\n",
    "vectorizer = TfidfVectorizer(ngram_range=(1,3))\n",
    "\n",
    "# fit vectorizer w/ train data (training data)\n",
    "# This makes your vocab matrix, learns words in vocab\n",
    "vectorizer.fit(X_train)\n",
    "\n",
    "# This transforms your documents into vectors (numbers).\n",
    "X_train = vectorizer.transform(X_train)\n",
    "X_test = vectorizer.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to test and evaluate models. \n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import accuracy_score, f1_score\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "# For plotting\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def build_and_eval_model(model, X, y):\n",
    "    print(\"Running report for model:\", model) \n",
    "    \n",
    "    # Fit our model.\n",
    "    model.fit(X_train, y_train)\n",
    "    \n",
    "    \n",
    "    # predicting results based on testing data\n",
    "    y_pred = model.predict(X_test)\n",
    "    # Make predicted probabilites of our testing data\n",
    "#     y_pred_proba = model.predict_proba(X_test)\n",
    "# https://stackoverflow.com/questions/15111408/how-does-sklearn-svm-svcs-function-predict-proba-work-internally\n",
    "\n",
    "    # Get the evaluation metrics \n",
    "    print('Model accuracy score: {0:0.4f}'. format(accuracy_score(y_test, y_pred)))\n",
    "    # y_test are true class labels and y_pred are predicted class labels in test-set, don't use train data\n",
    "    \n",
    "    # f1-score is always lower than accuracy measures, they embed precision and recall into their computation\n",
    "#     print('F1 Score: %.4f' % f1_score(y_test, y_pred, pos_label='0'))\n",
    "    # https://datascience.stackexchange.com/questions/54436/valueerror-pos-label-1-is-not-a-valid-label-arrayn-y-dtype-u1\n",
    "    # data not much, low f1score; cross fold validation to generate data articificially\n",
    "    f1score = f1_score(y_test, y_pred, average='macro')\n",
    "    print('macro F-1 score : {}'.format(np.round(f1score,5)))\n",
    "    # macro avg f1score = unweighted mean of all the per-class F1 scores, treats all classes equally important\n",
    "    # weighted-averaged F1 score is calculated by taking the mean of all per-class F1 scores while considering each class’s support\n",
    "    ## ‘weight’ refers to proportion of each class’s support relative to the sum of all support values\n",
    "    # https://towardsdatascience.com/micro-macro-weighted-averages-of-f1-score-clearly-explained-b603420b292f\n",
    "    \n",
    "    # compare the train-set and test-set accuracy to check for overfitting \n",
    "    y_pred_train = model.predict(X_train) \n",
    "    # overlearned training data = training accuracy higher (but not over so b/c not 99)\n",
    "    ## close enough to \"possibly\" overfit but not dramatic difference --> trust model accuracy score (minor point)\n",
    "    print('Training set accuracy score: {0:0.4f}'. format(accuracy_score(y_train, y_pred_train)))\n",
    "    print('Test set accuracy score: {:.4f}'.format(model.score(X_test, y_test)))\n",
    "\n",
    "    # Print the classification report. \n",
    "    print(metrics.classification_report(y_test, y_pred))\n",
    "    \n",
    "        \n",
    "    # Plot the confusion matrix\n",
    "    fig, ax = plt.subplots(figsize=(13,13))\n",
    "    \n",
    "    metrics.plot_confusion_matrix(estimator=model, \n",
    "                                  X=X_test, \n",
    "                                  y_true=y_test, \n",
    "                                  ax=ax)\n",
    "    \n",
    "    cm = confusion_matrix(y_test, y_pred)\n",
    "    print('Confusion matrix\\n\\n', cm)\n",
    "    print('\\nTrue Positives(TP) = ', cm[0,0])\n",
    "    print('\\nTrue Negatives(TN) = ', cm[1,1])\n",
    "    print('\\nFalse Positives(FP) = ', cm[0,1])\n",
    "    print('\\nFalse Negatives(FN) = ', cm[1,0])\n",
    "\n",
    "#     # visualize confusion matrix with seaborn heatmap\n",
    "#     cm_matrix = pd.DataFrame(data=cm, columns=['Actual Positive:1', 'Actual Negative:0'], \n",
    "#                                  index=['Predict Positive:1', 'Predict Negative:0'])\n",
    "#     sns.heatmap(cm_matrix, annot=True, fmt='d', cmap='YlGnBu')\n",
    "    \n",
    "    \n",
    "    # print classification accuracy, same as test set score & model accuracy score\n",
    "    classification_accuracy = (cm[0,0] + cm[1,1]) / float(cm[0,0] + cm[1,1] + cm[0,1] + cm[1,0])\n",
    "    print('Classification accuracy : {0:0.4f}'.format(classification_accuracy))\n",
    "    \n",
    "    # print classification error\n",
    "    classification_error = (cm[0,1] + cm[1,0]) / float(cm[0,0] + cm[1,1] + cm[0,1] + cm[1,0])\n",
    "    print('Classification error : {0:0.4f}'.format(classification_error))\n",
    "\n",
    "    # Return the fitted model. \n",
    "    return model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running report for model: LogisticRegression()\n",
      "Model accuracy score: 0.7728\n",
      "macro F-1 score : 0.71293\n",
      "Training set accuracy score: 0.8792\n",
      "Test set accuracy score: 0.7728\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.90      0.84      1278\n",
      "           1       0.69      0.50      0.58       588\n",
      "\n",
      "    accuracy                           0.77      1866\n",
      "   macro avg       0.74      0.70      0.71      1866\n",
      "weighted avg       0.76      0.77      0.76      1866\n",
      "\n",
      "Confusion matrix\n",
      "\n",
      " [[1147  131]\n",
      " [ 293  295]]\n",
      "\n",
      "True Positives(TP) =  1147\n",
      "\n",
      "True Negatives(TN) =  295\n",
      "\n",
      "False Positives(FP) =  131\n",
      "\n",
      "False Negatives(FN) =  293\n",
      "Classification accuracy : 0.7728\n",
      "Classification error : 0.2272\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 936x936 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "lr = LogisticRegression()\n",
    "build_and_eval_model(lr, X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running report for model: LinearSVC()\n",
      "Model accuracy score: 0.7964\n",
      "macro F-1 score : 0.76823\n",
      "Training set accuracy score: 0.9918\n",
      "Test set accuracy score: 0.7964\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.84      0.85      1278\n",
      "           1       0.67      0.71      0.69       588\n",
      "\n",
      "    accuracy                           0.80      1866\n",
      "   macro avg       0.76      0.77      0.77      1866\n",
      "weighted avg       0.80      0.80      0.80      1866\n",
      "\n",
      "Confusion matrix\n",
      "\n",
      " [[1068  210]\n",
      " [ 170  418]]\n",
      "\n",
      "True Positives(TP) =  1068\n",
      "\n",
      "True Negatives(TN) =  418\n",
      "\n",
      "False Positives(FP) =  210\n",
      "\n",
      "False Negatives(FN) =  170\n",
      "Classification accuracy : 0.7964\n",
      "Classification error : 0.2036\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LinearSVC()"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 936x936 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "\n",
    "lsvc = svm.LinearSVC()\n",
    "build_and_eval_model(lsvc, X, y)\n",
    "# class is imbalanced but not a lot of data is impacting 0 detection better"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running report for model: MultinomialNB(alpha=0.05)\n",
      "Model accuracy score: 0.7792\n",
      "macro F-1 score : 0.73418\n",
      "Training set accuracy score: 0.9910\n",
      "Test set accuracy score: 0.7792\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.87      0.84      1278\n",
      "           1       0.67      0.58      0.62       588\n",
      "\n",
      "    accuracy                           0.78      1866\n",
      "   macro avg       0.75      0.73      0.73      1866\n",
      "weighted avg       0.77      0.78      0.77      1866\n",
      "\n",
      "Confusion matrix\n",
      "\n",
      " [[1111  167]\n",
      " [ 245  343]]\n",
      "\n",
      "True Positives(TP) =  1111\n",
      "\n",
      "True Negatives(TN) =  343\n",
      "\n",
      "False Positives(FP) =  167\n",
      "\n",
      "False Negatives(FN) =  245\n",
      "Classification accuracy : 0.7792\n",
      "Classification error : 0.2208\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "MultinomialNB(alpha=0.05)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 936x936 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mnb = MultinomialNB(alpha=.05)\n",
    "#https://stackoverflow.com/questions/52319703/naive-bayes-accuracy-increasing-as-increasing-in-the-alpha-value\n",
    "build_and_eval_model(mnb, X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running report for model: SVC()\n",
      "Model accuracy score: 0.7808\n",
      "macro F-1 score : 0.7194\n",
      "Training set accuracy score: 0.9902\n",
      "Test set accuracy score: 0.7808\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.91      0.85      1278\n",
      "           1       0.72      0.50      0.59       588\n",
      "\n",
      "    accuracy                           0.78      1866\n",
      "   macro avg       0.76      0.70      0.72      1866\n",
      "weighted avg       0.77      0.78      0.77      1866\n",
      "\n",
      "Confusion matrix\n",
      "\n",
      " [[1165  113]\n",
      " [ 296  292]]\n",
      "\n",
      "True Positives(TP) =  1165\n",
      "\n",
      "True Negatives(TN) =  292\n",
      "\n",
      "False Positives(FP) =  113\n",
      "\n",
      "False Negatives(FN) =  296\n",
      "Classification accuracy : 0.7808\n",
      "Classification error : 0.2192\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 936x936 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "svc = svm.SVC()\n",
    "build_and_eval_model(svc, X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running report for model: RandomForestClassifier()\n",
      "Model accuracy score: 0.7481\n",
      "macro F-1 score : 0.62812\n",
      "Training set accuracy score: 0.9918\n",
      "Test set accuracy score: 0.7481\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.75      0.96      0.84      1278\n",
      "           1       0.77      0.29      0.42       588\n",
      "\n",
      "    accuracy                           0.75      1866\n",
      "   macro avg       0.76      0.62      0.63      1866\n",
      "weighted avg       0.75      0.75      0.71      1866\n",
      "\n",
      "Confusion matrix\n",
      "\n",
      " [[1228   50]\n",
      " [ 420  168]]\n",
      "\n",
      "True Positives(TP) =  1228\n",
      "\n",
      "True Negatives(TN) =  168\n",
      "\n",
      "False Positives(FP) =  50\n",
      "\n",
      "False Negatives(FN) =  420\n",
      "Classification accuracy : 0.7481\n",
      "Classification error : 0.2519\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 936x936 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf = RandomForestClassifier()\n",
    "build_and_eval_model(rf, X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running report for model: DecisionTreeClassifier(random_state=0)\n",
      "Model accuracy score: 0.7235\n",
      "macro F-1 score : 0.66743\n",
      "Training set accuracy score: 0.9918\n",
      "Test set accuracy score: 0.7235\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.83      0.80      1278\n",
      "           1       0.57      0.50      0.53       588\n",
      "\n",
      "    accuracy                           0.72      1866\n",
      "   macro avg       0.68      0.66      0.67      1866\n",
      "weighted avg       0.71      0.72      0.72      1866\n",
      "\n",
      "Confusion matrix\n",
      "\n",
      " [[1058  220]\n",
      " [ 296  292]]\n",
      "\n",
      "True Positives(TP) =  1058\n",
      "\n",
      "True Negatives(TN) =  292\n",
      "\n",
      "False Positives(FP) =  220\n",
      "\n",
      "False Negatives(FN) =  296\n",
      "Classification accuracy : 0.7235\n",
      "Classification error : 0.2765\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(random_state=0)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 936x936 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dt = DecisionTreeClassifier(random_state=0)\n",
    "build_and_eval_model(dt, X, y)"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "include_colab_link": true,
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}