South African Language Identification Hack 2022.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# South African Language Identification Hack 2022"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "South Africa is a multiethnic country known for its extensive language variety. The power of language to advance democracy and improve the social, cultural, intellectual, economic, and political spheres of South African society makes it an essential weapon.\n",
    "\n",
    "There are 11 official languages in the nation, all of which are given equal recognition. The majority of South Africans speak two or more of the official languages and are multilingual."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " <a id=\"one\"></a>\n",
    "## 1. Import Necessary Libraries\n",
    "<a href=#cont>Back to Table of Contents</a>\n",
    "\n",
    "---\n",
    "    \n",
    "| ⚡ Description: Import necessary libraries ⚡ |\n",
    "| :--------------------------- |\n",
    "| We'd be importing all of the necessary libraries for the notebook to run smoothly..|\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore', category=FutureWarning)\n",
    "warnings.filterwarnings('ignore', category=DeprecationWarning)\n",
    "from nltk.tokenize import  word_tokenize\n",
    "\n",
    "import warnings\n",
    "warnings.simplefilter(\"ignore\")\n",
    "# imports for data visualisation\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "sns.set()\n",
    "from textblob import TextBlob\n",
    "from nltk.probability import FreqDist\n",
    "from wordcloud import WordCloud #Pip install wordcloud\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "\n",
    "# imports Checking Acuracy\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import  accuracy_score\n",
    "from nltk.corpus import stopwords\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn import metrics\n",
    "\n",
    "\n",
    "# Models\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.linear_model import LogisticRegression, RidgeClassifier\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.model_selection import GridSearchCV, StratifiedKFold, KFold\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"Two\"></a>\n",
    "## 2. Loading the Data\n",
    "<a class=\"anchor\" id=\"1.1\"></a>\n",
    "<a href=#cont>Back to Table of Contents</a>\n",
    "\n",
    "---\n",
    "    \n",
    "| ⚡ Description: Loading the data ⚡ |\n",
    "| :--------------------------- |\n",
    "| To start importing our data, we make sure the raw data and notebook file are both in the same folder on our local system. Using the code below, we'll load the train data sets into our notebook. If the files aren't in the same folder, we'll have to point to the directory on our machine . To check that the data has loaded correctly, it is a good practice to call up the loaded data after it has been loaded.\n",
    "The information would be saved in two different data frames, one for training and testing and the other for our EDA.|\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the data sets\n",
    "train = pd.read_csv('train_set.csv')\n",
    "test = pd.read_csv('test_set.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lang_id</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>xho</td>\n",
       "      <td>umgaqo-siseko wenza amalungiselelo kumaziko axhasa ulawulo lwesininzi kunye nokuthath inxaxheba kwabafazi ezi ziquka phakathi kwezinye zazo ikomishoni yokulingana ngokwesini ikomishoni yamalungelo oluntu lomzantsi afrika</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>xho</td>\n",
       "      <td>i-dha iya kuba nobulumko bokubeka umsebenzi naphi na kwisebe ngokusekwe kwiimfuno zokusebenza zalo emva kokubonana nomsebenzi kunye okanye imanyano yakhe ukuba ulandulo lomntu onjalo alufanelekanga i-dha mayibize uncedo olufanelekileyo elungelweni layo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>eng</td>\n",
       "      <td>the province of kwazulu-natal department of transport invites tenders from established contractors experienced in bridge construction for the construction of the kwajolwayo tugela river pedestrian bridge near tugela ferry the duration of the project will be months</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>nso</td>\n",
       "      <td>o netefatša gore o ba file dilo ka moka tše le dumelelanego ka tšona mohlala maleri a magolo a a šomišwago go fihlelela meagong e metelele scaffolds a a bolokegilego lefelo la maleba la go šomela go phela gabotse bjbj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ven</td>\n",
       "      <td>khomishini ya ndinganyiso ya mbeu yo ewa maana u ya nga mulayo wa khomishini ya ndinganyiso ya mbeu u thetshelesa mbilaelo dzine dza tshimbilelana na tshialula u ya nga mbeu nahone i ivhea sa foramu ya thungo u ya nga mulayo wa ndinganyiso</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  lang_id  \\\n",
       "0     xho   \n",
       "1     xho   \n",
       "2     eng   \n",
       "3     nso   \n",
       "4     ven   \n",
       "\n",
       "                                                                                                                                                                                                                                                                       text  \n",
       "0                                              umgaqo-siseko wenza amalungiselelo kumaziko axhasa ulawulo lwesininzi kunye nokuthath inxaxheba kwabafazi ezi ziquka phakathi kwezinye zazo ikomishoni yokulingana ngokwesini ikomishoni yamalungelo oluntu lomzantsi afrika  \n",
       "1              i-dha iya kuba nobulumko bokubeka umsebenzi naphi na kwisebe ngokusekwe kwiimfuno zokusebenza zalo emva kokubonana nomsebenzi kunye okanye imanyano yakhe ukuba ulandulo lomntu onjalo alufanelekanga i-dha mayibize uncedo olufanelekileyo elungelweni layo  \n",
       "2  the province of kwazulu-natal department of transport invites tenders from established contractors experienced in bridge construction for the construction of the kwajolwayo tugela river pedestrian bridge near tugela ferry the duration of the project will be months  \n",
       "3                                                 o netefatša gore o ba file dilo ka moka tše le dumelelanego ka tšona mohlala maleri a magolo a a šomišwago go fihlelela meagong e metelele scaffolds a a bolokegilego lefelo la maleba la go šomela go phela gabotse bjbj  \n",
       "4                           khomishini ya ndinganyiso ya mbeu yo ewa maana u ya nga mulayo wa khomishini ya ndinganyiso ya mbeu u thetshelesa mbilaelo dzine dza tshimbilelana na tshialula u ya nga mbeu nahone i ivhea sa foramu ya thungo u ya nga mulayo wa ndinganyiso  "
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
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       "      <th>index</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Mmasepala, fa maemo a a kgethegileng a letlelela kgato eo.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Uzakwaziswa ngokufaneleko nakungafuneka eminye imitlolo engezelelako ukuqedelela ukutloliswa kwesibawo sakho.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Tshivhumbeo tshi fana na ngano dza vhathu.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Kube inja nelikati betingevakala kutsi titsini naticocisana.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Winste op buitelandse valuta.</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "   index  \\\n",
       "0      1   \n",
       "1      2   \n",
       "2      3   \n",
       "3      4   \n",
       "4      5   \n",
       "\n",
       "                                                                                                            text  \n",
       "0                                                     Mmasepala, fa maemo a a kgethegileng a letlelela kgato eo.  \n",
       "1  Uzakwaziswa ngokufaneleko nakungafuneka eminye imitlolo engezelelako ukuqedelela ukutloliswa kwesibawo sakho.  \n",
       "2                                                                     Tshivhumbeo tshi fana na ngano dza vhathu.  \n",
       "3                                                   Kube inja nelikati betingevakala kutsi titsini naticocisana.  \n",
       "4                                                                                  Winste op buitelandse valuta.  "
      ]
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     "execution_count": 121,
     "metadata": {},
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   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=three1></a>\n",
    "\n",
    "#### 2.1 Set Pandas to enable viewing of all columns\n",
    "Due to the length of th content of the message column, pandas cannot display all of them at once by default. While doing EDA and data cleansing, we will need to see all of the columns. When the dataframe is presented, the code below allows us to see the whole set of columns in our data collection. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lang_id</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>xho</td>\n",
       "      <td>umgaqo-siseko wenza amalungiselelo kumaziko axhasa ulawulo lwesininzi kunye nokuthath inxaxheba kwabafazi ezi ziquka phakathi kwezinye zazo ikomishoni yokulingana ngokwesini ikomishoni yamalungelo oluntu lomzantsi afrika</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>xho</td>\n",
       "      <td>i-dha iya kuba nobulumko bokubeka umsebenzi naphi na kwisebe ngokusekwe kwiimfuno zokusebenza zalo emva kokubonana nomsebenzi kunye okanye imanyano yakhe ukuba ulandulo lomntu onjalo alufanelekanga i-dha mayibize uncedo olufanelekileyo elungelweni layo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>eng</td>\n",
       "      <td>the province of kwazulu-natal department of transport invites tenders from established contractors experienced in bridge construction for the construction of the kwajolwayo tugela river pedestrian bridge near tugela ferry the duration of the project will be months</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>nso</td>\n",
       "      <td>o netefatša gore o ba file dilo ka moka tše le dumelelanego ka tšona mohlala maleri a magolo a a šomišwago go fihlelela meagong e metelele scaffolds a a bolokegilego lefelo la maleba la go šomela go phela gabotse bjbj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ven</td>\n",
       "      <td>khomishini ya ndinganyiso ya mbeu yo ewa maana u ya nga mulayo wa khomishini ya ndinganyiso ya mbeu u thetshelesa mbilaelo dzine dza tshimbilelana na tshialula u ya nga mbeu nahone i ivhea sa foramu ya thungo u ya nga mulayo wa ndinganyiso</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  lang_id  \\\n",
       "0     xho   \n",
       "1     xho   \n",
       "2     eng   \n",
       "3     nso   \n",
       "4     ven   \n",
       "\n",
       "                                                                                                                                                                                                                                                                       text  \n",
       "0                                              umgaqo-siseko wenza amalungiselelo kumaziko axhasa ulawulo lwesininzi kunye nokuthath inxaxheba kwabafazi ezi ziquka phakathi kwezinye zazo ikomishoni yokulingana ngokwesini ikomishoni yamalungelo oluntu lomzantsi afrika  \n",
       "1              i-dha iya kuba nobulumko bokubeka umsebenzi naphi na kwisebe ngokusekwe kwiimfuno zokusebenza zalo emva kokubonana nomsebenzi kunye okanye imanyano yakhe ukuba ulandulo lomntu onjalo alufanelekanga i-dha mayibize uncedo olufanelekileyo elungelweni layo  \n",
       "2  the province of kwazulu-natal department of transport invites tenders from established contractors experienced in bridge construction for the construction of the kwajolwayo tugela river pedestrian bridge near tugela ferry the duration of the project will be months  \n",
       "3                                                 o netefatša gore o ba file dilo ka moka tše le dumelelanego ka tšona mohlala maleri a magolo a a šomišwago go fihlelela meagong e metelele scaffolds a a bolokegilego lefelo la maleba la go šomela go phela gabotse bjbj  \n",
       "4                           khomishini ya ndinganyiso ya mbeu yo ewa maana u ya nga mulayo wa khomishini ya ndinganyiso ya mbeu u thetshelesa mbilaelo dzine dza tshimbilelana na tshialula u ya nga mbeu nahone i ivhea sa foramu ya thungo u ya nga mulayo wa ndinganyiso  "
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set option to display all columns\n",
    "pd.set_option('display.max_colwidth', None)\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=three2></a>\n",
    "\n",
    "#### 2.2 Check the \"Shape\" of the data-sets\n",
    "As demonstrated by the shape of both datasets, the data has been separated into two sets. The form also shows that the training data set has three columns, but the test data set has just two. Our model is designed to forecast the column that is not present in the test set. We can look for that specific item by looking for the missing entity (Column) in the test data set. After looking at both datasets, the column may be identified as the sentiment column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((33000, 2), (5682, 2))"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape, test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=three21></a>\n",
    "\n",
    "#### 2.3 Use the \".column\" function to view the columns in our data set\n",
    "The number of columns and rows in our data set were revealed by the shape method, but the.columns function provides the names of all the columns in the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Index(['lang_id', 'text'], dtype='object'),\n",
       " Index(['index', 'text'], dtype='object'))"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns, test.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.4 Statistics\n",
    "For text and lang id, there are 33000 features total in the dataset. There are 29948 unique characteristics in the text compared to 11 unique features in the lang id. The lang id occurs 3000and the text features 17 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>text</th>\n",
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       "      <td>33000</td>\n",
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       "      <th>top</th>\n",
       "      <td>xho</td>\n",
       "      <td>ngokwesekhtjheni yomthetho ophathelene nalokhu unelungelo lokudlulisela isililo sakho kusomkhandlu wezehlalakuhle ngokutlola incwadi uyithumele e-adresini elandelako kungakapheli amalanga amatjhumi alithoba ukusukela mhlazana uthola incwadi le</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>3000</td>\n",
       "      <td>17</td>\n",
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      "text/plain": [
       "       lang_id  \\\n",
       "count    33000   \n",
       "unique      11   \n",
       "top        xho   \n",
       "freq      3000   \n",
       "\n",
       "                                                                                                                                                                                                                                                       text  \n",
       "count                                                                                                                                                                                                                                                 33000  \n",
       "unique                                                                                                                                                                                                                                                29948  \n",
       "top     ngokwesekhtjheni yomthetho ophathelene nalokhu unelungelo lokudlulisela isililo sakho kusomkhandlu wezehlalakuhle ngokutlola incwadi uyithumele e-adresini elandelako kungakapheli amalanga amatjhumi alithoba ukusukela mhlazana uthola incwadi le  \n",
       "freq                                                                                                                                                                                                                                                     17  "
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"three\"></a>\n",
    "## 3. Data Preprocessing (Cleaning)\n",
    "<a class=\"anchor\" id=\"1.1\"></a>\n",
    "<a href=#cont>Back to Table of Contents</a>\n",
    "\n",
    "| ⚡ Description: Data Cleaning ⚡ |\n",
    "| :--------------------------- |\n",
    "| In this phase, we'll convert the data into a readable and desired format, as well as filter out the most relevant information.. |\n",
    "# "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=four1></a>\n",
    "\n",
    "#### 3.1 Identifying Missing Values\n",
    "\n",
    "It is important to identify the columns that have null entries as null values can affect the performance of our model. The \"isnull\" function shows the number of null values that are contained in each column of the dataset. This data set is relatively clean as this function shows that only the column \"Valencia_pressure\" features null values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "lang_id    0\n",
       "text       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Cheching if there are missing values in the Train dataset\n",
    "train.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "index    0\n",
       "text     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Cheching if there are missing values in the Train dataset\n",
    "test.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Cheching for unique values\n",
    "train[\"lang_id\"].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5682"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test[\"index\"].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=four2></a>\n",
    "### 3.2 Preprocessing the Dataset\n",
    "\n",
    "#### Due to the presence of a non numerical column in our datasets, some preprocessing processes must be performed, including:\n",
    "\n",
    "\n",
    "* letter casing :Converting all letters to upper case or lower case is called letter casing.\n",
    "\n",
    "* Noise removal: Unwanted characters such as HTML tags, punctuation marks, special characters, white spaces, and so on are removed.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "#removing links\n",
    "train['text'] = train['text'].str.replace('http\\S+|www.\\S+', '', case=False)\n",
    "train['text'] = train['text'].str.replace(r's*https?://S+(s+|$)', ' ',case=False).str.strip()\n",
    "#removing links for test data\n",
    "test['text'] = test['text'].str.replace('http\\S+|www.\\S+', '', case=False)\n",
    "test['text'] = test['text'].str.replace(r's*https?://S+(s+|$)', ' ',case=False).str.strip()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "stopwords_list = stopwords.words('english')\n",
    "\n",
    "def preprocess(text):\n",
    "    \"\"\"This function takes in pandas dataframe, removes punctuation noises,contractions and lemmatize the text.\"\"\"\n",
    "    cleanText = re.sub(r'[-]',' ',text)\n",
    "    cleanText = re.sub(r'[_]', ' ', cleanText)\n",
    "    cleanText = re.sub(r'[^\\w\\s]','',cleanText)\n",
    "    cleanText = re.sub('[0-9]+', '', cleanText)\n",
    "    cleanText = re.sub(r'[^\\x00-\\x7f]',r'', cleanText)   \n",
    "    cleanText = re.sub(r'@[a-zA-Z0-9\\_\\w]+', '', cleanText)#Remove @mentions\n",
    "    cleanText = re.sub(r'#[a-zA-Z0-9]+', '', cleanText) #Remove '#' symbols\n",
    "    cleanText = cleanText.lower() #Lowering case\n",
    "    return cleanText"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lang_id</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>xho</td>\n",
       "      <td>umgaqo siseko wenza amalungiselelo kumaziko axhasa ulawulo lwesininzi kunye nokuthath inxaxheba kwabafazi ezi ziquka phakathi kwezinye zazo ikomishoni yokulingana ngokwesini ikomishoni yamalungelo oluntu lomzantsi afrika</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>xho</td>\n",
       "      <td>i dha iya kuba nobulumko bokubeka umsebenzi naphi na kwisebe ngokusekwe kwiimfuno zokusebenza zalo emva kokubonana nomsebenzi kunye okanye imanyano yakhe ukuba ulandulo lomntu onjalo alufanelekanga i dha mayibize uncedo olufanelekileyo elungelweni layo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>eng</td>\n",
       "      <td>the province of kwazulu natal department of transport invites tenders from established contractors experienced in bridge construction for the construction of the kwajolwayo tugela river pedestrian bridge near tugela ferry the duration of the project will be months</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>nso</td>\n",
       "      <td>o netefata gore o ba file dilo ka moka te le dumelelanego ka tona mohlala maleri a magolo a a omiwago go fihlelela meagong e metelele scaffolds a a bolokegilego lefelo la maleba la go omela go phela gabotse bjbj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ven</td>\n",
       "      <td>khomishini ya ndinganyiso ya mbeu yo ewa maana u ya nga mulayo wa khomishini ya ndinganyiso ya mbeu u thetshelesa mbilaelo dzine dza tshimbilelana na tshialula u ya nga mbeu nahone i ivhea sa foramu ya thungo u ya nga mulayo wa ndinganyiso</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  lang_id  \\\n",
       "0     xho   \n",
       "1     xho   \n",
       "2     eng   \n",
       "3     nso   \n",
       "4     ven   \n",
       "\n",
       "                                                                                                                                                                                                                                                                       text  \n",
       "0                                              umgaqo siseko wenza amalungiselelo kumaziko axhasa ulawulo lwesininzi kunye nokuthath inxaxheba kwabafazi ezi ziquka phakathi kwezinye zazo ikomishoni yokulingana ngokwesini ikomishoni yamalungelo oluntu lomzantsi afrika  \n",
       "1              i dha iya kuba nobulumko bokubeka umsebenzi naphi na kwisebe ngokusekwe kwiimfuno zokusebenza zalo emva kokubonana nomsebenzi kunye okanye imanyano yakhe ukuba ulandulo lomntu onjalo alufanelekanga i dha mayibize uncedo olufanelekileyo elungelweni layo  \n",
       "2  the province of kwazulu natal department of transport invites tenders from established contractors experienced in bridge construction for the construction of the kwajolwayo tugela river pedestrian bridge near tugela ferry the duration of the project will be months  \n",
       "3                                                       o netefata gore o ba file dilo ka moka te le dumelelanego ka tona mohlala maleri a magolo a a omiwago go fihlelela meagong e metelele scaffolds a a bolokegilego lefelo la maleba la go omela go phela gabotse bjbj  \n",
       "4                           khomishini ya ndinganyiso ya mbeu yo ewa maana u ya nga mulayo wa khomishini ya ndinganyiso ya mbeu u thetshelesa mbilaelo dzine dza tshimbilelana na tshialula u ya nga mbeu nahone i ivhea sa foramu ya thungo u ya nga mulayo wa ndinganyiso  "
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[\"text\"]=train[\"text\"].apply(preprocess)\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>mmasepala fa maemo a a kgethegileng a letlelela kgato eo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>uzakwaziswa ngokufaneleko nakungafuneka eminye imitlolo engezelelako ukuqedelela ukutloliswa kwesibawo sakho</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>tshivhumbeo tshi fana na ngano dza vhathu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>kube inja nelikati betingevakala kutsi titsini naticocisana</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>winste op buitelandse valuta</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  \\\n",
       "0      1   \n",
       "1      2   \n",
       "2      3   \n",
       "3      4   \n",
       "4      5   \n",
       "\n",
       "                                                                                                           text  \n",
       "0                                                      mmasepala fa maemo a a kgethegileng a letlelela kgato eo  \n",
       "1  uzakwaziswa ngokufaneleko nakungafuneka eminye imitlolo engezelelako ukuqedelela ukutloliswa kwesibawo sakho  \n",
       "2                                                                     tshivhumbeo tshi fana na ngano dza vhathu  \n",
       "3                                                   kube inja nelikati betingevakala kutsi titsini naticocisana  \n",
       "4                                                                                  winste op buitelandse valuta  "
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test[\"text\"]=test[\"text\"].apply(preprocess)\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "removing stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lang_id</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>xho</td>\n",
       "      <td>[umgaqo, siseko, wenza, amalungiselelo, kumaziko, axhasa, ulawulo, lwesininzi, kunye, nokuthath, inxaxheba, kwabafazi, ezi, ziquka, phakathi, kwezinye, zazo, ikomishoni, yokulingana, ngokwesini, ikomishoni, yamalungelo, oluntu, lomzantsi, afrika]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>xho</td>\n",
       "      <td>[dha, iya, kuba, nobulumko, bokubeka, umsebenzi, naphi, na, kwisebe, ngokusekwe, kwiimfuno, zokusebenza, zalo, emva, kokubonana, nomsebenzi, kunye, okanye, imanyano, yakhe, ukuba, ulandulo, lomntu, onjalo, alufanelekanga, dha, mayibize, uncedo, olufanelekileyo, elungelweni, layo]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>eng</td>\n",
       "      <td>[province, kwazulu, natal, department, transport, invites, tenders, established, contractors, experienced, bridge, construction, construction, kwajolwayo, tugela, river, pedestrian, bridge, near, tugela, ferry, duration, project, months]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>nso</td>\n",
       "      <td>[netefata, gore, ba, file, dilo, ka, moka, te, le, dumelelanego, ka, tona, mohlala, maleri, magolo, omiwago, go, fihlelela, meagong, e, metelele, scaffolds, bolokegilego, lefelo, la, maleba, la, go, omela, go, phela, gabotse, bjbj]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ven</td>\n",
       "      <td>[khomishini, ya, ndinganyiso, ya, mbeu, yo, ewa, maana, u, ya, nga, mulayo, wa, khomishini, ya, ndinganyiso, ya, mbeu, u, thetshelesa, mbilaelo, dzine, dza, tshimbilelana, na, tshialula, u, ya, nga, mbeu, nahone, ivhea, sa, foramu, ya, thungo, u, ya, nga, mulayo, wa, ndinganyiso]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  lang_id  \\\n",
       "0     xho   \n",
       "1     xho   \n",
       "2     eng   \n",
       "3     nso   \n",
       "4     ven   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                       text  \n",
       "0                                    [umgaqo, siseko, wenza, amalungiselelo, kumaziko, axhasa, ulawulo, lwesininzi, kunye, nokuthath, inxaxheba, kwabafazi, ezi, ziquka, phakathi, kwezinye, zazo, ikomishoni, yokulingana, ngokwesini, ikomishoni, yamalungelo, oluntu, lomzantsi, afrika]  \n",
       "1  [dha, iya, kuba, nobulumko, bokubeka, umsebenzi, naphi, na, kwisebe, ngokusekwe, kwiimfuno, zokusebenza, zalo, emva, kokubonana, nomsebenzi, kunye, okanye, imanyano, yakhe, ukuba, ulandulo, lomntu, onjalo, alufanelekanga, dha, mayibize, uncedo, olufanelekileyo, elungelweni, layo]  \n",
       "2                                             [province, kwazulu, natal, department, transport, invites, tenders, established, contractors, experienced, bridge, construction, construction, kwajolwayo, tugela, river, pedestrian, bridge, near, tugela, ferry, duration, project, months]  \n",
       "3                                                   [netefata, gore, ba, file, dilo, ka, moka, te, le, dumelelanego, ka, tona, mohlala, maleri, magolo, omiwago, go, fihlelela, meagong, e, metelele, scaffolds, bolokegilego, lefelo, la, maleba, la, go, omela, go, phela, gabotse, bjbj]  \n",
       "4  [khomishini, ya, ndinganyiso, ya, mbeu, yo, ewa, maana, u, ya, nga, mulayo, wa, khomishini, ya, ndinganyiso, ya, mbeu, u, thetshelesa, mbilaelo, dzine, dza, tshimbilelana, na, tshialula, u, ya, nga, mbeu, nahone, ivhea, sa, foramu, ya, thungo, u, ya, nga, mulayo, wa, ndinganyiso]  "
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stop_words = set(stopwords.words('english'))\n",
    "train['text'] = train['text'].map(lambda x: list(map(str, filter(None, x.split(' '))))) ## Convert the cleaned message from string to list\n",
    "train['text'] = train['text'].apply(lambda x: [word for word in x if word not in stop_words])\n",
    "train.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>[mmasepala, fa, maemo, kgethegileng, letlelela, kgato, eo]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>[uzakwaziswa, ngokufaneleko, nakungafuneka, eminye, imitlolo, engezelelako, ukuqedelela, ukutloliswa, kwesibawo, sakho]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>[tshivhumbeo, tshi, fana, na, ngano, dza, vhathu]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>[kube, inja, nelikati, betingevakala, kutsi, titsini, naticocisana]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>[winste, op, buitelandse, valuta]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  \\\n",
       "0      1   \n",
       "1      2   \n",
       "2      3   \n",
       "3      4   \n",
       "4      5   \n",
       "\n",
       "                                                                                                                      text  \n",
       "0                                                               [mmasepala, fa, maemo, kgethegileng, letlelela, kgato, eo]  \n",
       "1  [uzakwaziswa, ngokufaneleko, nakungafuneka, eminye, imitlolo, engezelelako, ukuqedelela, ukutloliswa, kwesibawo, sakho]  \n",
       "2                                                                        [tshivhumbeo, tshi, fana, na, ngano, dza, vhathu]  \n",
       "3                                                      [kube, inja, nelikati, betingevakala, kutsi, titsini, naticocisana]  \n",
       "4                                                                                        [winste, op, buitelandse, valuta]  "
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stop_words = set(stopwords.words('english'))\n",
    "test['text'] = test['text'].map(lambda x: list(map(str, filter(None, x.split(' '))))) ## Convert the cleaned message from string to list\n",
    "test['text'] = test['text'].apply(lambda x: [word for word in x if word not in stop_words])\n",
    "test.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "def stri(cleanText):\n",
    "    cleanText = ' '.join(cleanText)\n",
    "    return cleanText"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=four3></a>\n",
    "\n",
    "#### 3.3 Most Frequent Words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('ya', 28245), ('le', 21204), ('ka', 18190), ('go', 17107), ('e', 15989)]"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train['text'] = train['text'].map(lambda x: list(map(str, filter(None, x.split(' '))))) ## Convert the cleaned message from string to list\n",
    "from collections import Counter\n",
    "cnt = Counter()\n",
    "for message in train['text'].values:\n",
    "    for word in message:\n",
    "        cnt[word] += 1\n",
    "        \n",
    "cnt.most_common(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "See distribution of messages per sentiment : \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_9b37b_row0_col1, #T_9b37b_row1_col1, #T_9b37b_row2_col1, #T_9b37b_row3_col1, #T_9b37b_row4_col1, #T_9b37b_row5_col1, #T_9b37b_row6_col1, #T_9b37b_row7_col1, #T_9b37b_row8_col1, #T_9b37b_row9_col1, #T_9b37b_row10_col1 {\n",
       "  background-color: #fcfbfd;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_9b37b\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_9b37b_level0_col0\" class=\"col_heading level0 col0\" >lang_id</th>\n",
       "      <th id=\"T_9b37b_level0_col1\" class=\"col_heading level0 col1\" >text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_9b37b_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_9b37b_row0_col0\" class=\"data row0 col0\" >afr</td>\n",
       "      <td id=\"T_9b37b_row0_col1\" class=\"data row0 col1\" >3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9b37b_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_9b37b_row1_col0\" class=\"data row1 col0\" >eng</td>\n",
       "      <td id=\"T_9b37b_row1_col1\" class=\"data row1 col1\" >3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9b37b_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_9b37b_row2_col0\" class=\"data row2 col0\" >nbl</td>\n",
       "      <td id=\"T_9b37b_row2_col1\" class=\"data row2 col1\" >3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9b37b_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_9b37b_row3_col0\" class=\"data row3 col0\" >nso</td>\n",
       "      <td id=\"T_9b37b_row3_col1\" class=\"data row3 col1\" >3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9b37b_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_9b37b_row4_col0\" class=\"data row4 col0\" >sot</td>\n",
       "      <td id=\"T_9b37b_row4_col1\" class=\"data row4 col1\" >3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9b37b_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_9b37b_row5_col0\" class=\"data row5 col0\" >ssw</td>\n",
       "      <td id=\"T_9b37b_row5_col1\" class=\"data row5 col1\" >3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9b37b_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_9b37b_row6_col0\" class=\"data row6 col0\" >tsn</td>\n",
       "      <td id=\"T_9b37b_row6_col1\" class=\"data row6 col1\" >3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9b37b_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_9b37b_row7_col0\" class=\"data row7 col0\" >tso</td>\n",
       "      <td id=\"T_9b37b_row7_col1\" class=\"data row7 col1\" >3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9b37b_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_9b37b_row8_col0\" class=\"data row8 col0\" >ven</td>\n",
       "      <td id=\"T_9b37b_row8_col1\" class=\"data row8 col1\" >3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9b37b_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_9b37b_row9_col0\" class=\"data row9 col0\" >xho</td>\n",
       "      <td id=\"T_9b37b_row9_col1\" class=\"data row9 col1\" >3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9b37b_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_9b37b_row10_col0\" class=\"data row10 col0\" >zul</td>\n",
       "      <td id=\"T_9b37b_row10_col1\" class=\"data row10 col1\" >3000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1f9f6163fa0>"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Cheching the value cont of the language id\n",
    "print(\"See distribution of messages per sentiment : \")\n",
    "count = train.groupby(\"lang_id\").count()[\"text\"].reset_index().sort_values(by=\"text\", ascending=False)\n",
    "count.style.background_gradient(cmap=\"Purples\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"five\"></a>\n",
    "## 4. Exploratory Data Analysis (EDA)\n",
    "<a class=\"anchor\" id=\"1.1\"></a>\n",
    "<a href=#cont>Back to Table of Contents</a>\n",
    "\n",
    "---\n",
    "    \n",
    "| ⚡ Description: Exploratory data analysis ⚡ |\n",
    "| :--------------------------- |\n",
    "| We'll use a range of strategies in this part to maximize specific insights into our dataset, uncover underlying structure, extract relevant variables, find outliers and anomalies, test assumptions, and establish the optimum estimation parameters. In other words, we want to go deeper into our dataset in order to learn more about its behavior! |\n",
    "\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=five1></a>\n",
    "\n",
    "### 5.1 Explore the Target Variable\n",
    "\n",
    "For each of our categories, we want to know how our target variable behaves....let's dig in ⛏!\n",
    "\n",
    "#### Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage contribution:\n",
      " xho    9.090909\n",
      "eng    9.090909\n",
      "nso    9.090909\n",
      "ven    9.090909\n",
      "tsn    9.090909\n",
      "nbl    9.090909\n",
      "zul    9.090909\n",
      "ssw    9.090909\n",
      "tso    9.090909\n",
      "sot    9.090909\n",
      "afr    9.090909\n",
      "Name: lang_id, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display target distribution\n",
    "\n",
    "print(\"Percentage contribution:\\n\",train['lang_id'].value_counts(normalize=True)*100)\n",
    "\n",
    "fig, axes = plt.subplots(ncols=2, \n",
    "                         nrows=1, \n",
    "                         figsize=(15, 10), \n",
    "                         dpi=100)\n",
    "\n",
    "sns.countplot(train['lang_id'], ax=axes[0])\n",
    "\n",
    "code_labels=['afr', 'eng', 'nbl', 'nso',\"sot\",\"ssw\",\"tsn\",\"tso\",\"ven\",\"xho\",\"zul\"]\n",
    "axes[1].pie(train['lang_id'].value_counts(),\n",
    "            labels= code_labels,\n",
    "            autopct='%1.0f%%',\n",
    "            startangle=90,\n",
    "            explode = (0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1))\n",
    "\n",
    "fig.suptitle('Dispersion of the target variable', fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=five22></a>\n",
    "\n",
    "#### 5.2.2 Visualize Tweet Length for each Class "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the length of each message for each class\n",
    "train[\"text\"] = train[\"text\"].apply(stri)\n",
    "train['text_length'] = train['text'].apply(lambda x: len(x))\n",
    "sns.boxplot(x=train['lang_id'], y=train['text_length'], data=train, width = 0.9, color = 'indigo')\n",
    "sns.set(rc={'figure.figsize':(10,5)})\n",
    "plt.ylabel('Length of the message')\n",
    "plt.xlabel('Sentiment class')\n",
    "plt.title('Message length for each lang_id')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=five4></a>\n",
    "\n",
    "#### 5.4 Investigative  Text Analysis\n",
    "From here, we'll delve further into our text, looking into each term and its frequency of occurrence individually.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['text'] = train['text'].map(lambda x: list(map(str, filter(None, x.split(' '))))) ## Convert the cleaned message from string to list\n",
    "words = train['text']\n",
    "allwords = []\n",
    "for wordlist in words:\n",
    "    allwords += wordlist\n",
    "    \n",
    "mostcommon = FreqDist(allwords).most_common(10000)\n",
    "wordcloud = WordCloud(width=700, height=700, background_color=\n",
    "                      'black').generate(str(mostcommon))\n",
    "fig = plt.figure(figsize=(30,10), facecolor='white')\n",
    "plt.imshow(wordcloud, interpolation=\"bilinear\")\n",
    "plt.axis('off')\n",
    "plt.tight_layout(pad=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"seven\"></a>\n",
    "## 6. Data or Feature Engineering\n",
    "<a class=\"anchor\" id=\"1.1\"></a>\n",
    "<a href=#cont>Back to Table of Contents</a>\n",
    "\n",
    "---\n",
    "    \n",
    "| ⚡ Description: Data engineering ⚡ |\n",
    "| :--------------------------- |\n",
    "| In this section we would be looking at one of feature selection in text data namely the `tfidfVectorizer`,split the data and also prepare it for training. |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>lang_id</th>\n",
       "      <th>text</th>\n",
       "      <th>text_length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>xho</td>\n",
       "      <td>umgaqo siseko wenza amalungiselelo kumaziko axhasa ulawulo lwesininzi kunye nokuthath inxaxheba kwabafazi ezi ziquka phakathi kwezinye zazo ikomishoni yokulingana ngokwesini ikomishoni yamalungelo oluntu lomzantsi afrika</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>xho</td>\n",
       "      <td>dha iya kuba nobulumko bokubeka umsebenzi naphi na kwisebe ngokusekwe kwiimfuno zokusebenza zalo emva kokubonana nomsebenzi kunye okanye imanyano yakhe ukuba ulandulo lomntu onjalo alufanelekanga dha mayibize uncedo olufanelekileyo elungelweni layo</td>\n",
       "      <td>248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>eng</td>\n",
       "      <td>province kwazulu natal department transport invites tenders established contractors experienced bridge construction construction kwajolwayo tugela river pedestrian bridge near tugela ferry duration project months</td>\n",
       "      <td>212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>nso</td>\n",
       "      <td>netefata gore ba file dilo ka moka te le dumelelanego ka tona mohlala maleri magolo omiwago go fihlelela meagong e metelele scaffolds bolokegilego lefelo la maleba la go omela go phela gabotse bjbj</td>\n",
       "      <td>197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ven</td>\n",
       "      <td>khomishini ya ndinganyiso ya mbeu yo ewa maana u ya nga mulayo wa khomishini ya ndinganyiso ya mbeu u thetshelesa mbilaelo dzine dza tshimbilelana na tshialula u ya nga mbeu nahone ivhea sa foramu ya thungo u ya nga mulayo wa ndinganyiso</td>\n",
       "      <td>237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32995</th>\n",
       "      <td>tsn</td>\n",
       "      <td>popo ya dipolateforomo tse ke go tlisa boeteledipele ka mo saenseng ya lefatse ka mo go rona mme se ke kgato e kgolo ya go ya pele ka poponthwa ya thekeniki ya biothekeniki ga bua motsamaisi wa thekenoloji ya biotekeniki wa lefapha la saense le thekenoloji mor ben durham</td>\n",
       "      <td>271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32996</th>\n",
       "      <td>sot</td>\n",
       "      <td>modise mosadi na ntse sa utlwe hore thabang rata ho iketsa sengangele jwang hona jwale ke mona se bolela ka moo batlang ho lokollelwa ngwana wa hae ka teng wena jwale batla ho jarisa nna melato ya hae jwalo ka ha eka ke nna ya mo rommeng hore mathakake le banana ba motse oo kaofela</td>\n",
       "      <td>282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32997</th>\n",
       "      <td>eng</td>\n",
       "      <td>closing date submission completed tenders august h late submissions considered submissions must enclosed sealed envelope addressed chief executive officer address</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32998</th>\n",
       "      <td>xho</td>\n",
       "      <td>nawuphina umntu ofunyenwe enetyala phantsi kwalo mthetho ujongene ujungene nesohlwayo sokudliwa imali okanye ukuvalelwa ixesha elingadluliyo kwiinyanga ezintandathu okanye kuzo zombini isohlwayo sokudliwa imali okanye ukuvalelwa</td>\n",
       "      <td>228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32999</th>\n",
       "      <td>sot</td>\n",
       "      <td>mafapha mang le ona lokela ho etsa ditlaleho tsa ona ho latela dipehelo tsa nema merero yohle e lokela ho hlahlojwa ke komiti ya ditaba tsa tikoloho eo e kenyeleditseng motsamaisi kakaretso wa lefapha</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>33000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      lang_id  \\\n",
       "0         xho   \n",
       "1         xho   \n",
       "2         eng   \n",
       "3         nso   \n",
       "4         ven   \n",
       "...       ...   \n",
       "32995     tsn   \n",
       "32996     sot   \n",
       "32997     eng   \n",
       "32998     xho   \n",
       "32999     sot   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                             text  \\\n",
       "0                                                                    umgaqo siseko wenza amalungiselelo kumaziko axhasa ulawulo lwesininzi kunye nokuthath inxaxheba kwabafazi ezi ziquka phakathi kwezinye zazo ikomishoni yokulingana ngokwesini ikomishoni yamalungelo oluntu lomzantsi afrika   \n",
       "1                                        dha iya kuba nobulumko bokubeka umsebenzi naphi na kwisebe ngokusekwe kwiimfuno zokusebenza zalo emva kokubonana nomsebenzi kunye okanye imanyano yakhe ukuba ulandulo lomntu onjalo alufanelekanga dha mayibize uncedo olufanelekileyo elungelweni layo   \n",
       "2                                                                            province kwazulu natal department transport invites tenders established contractors experienced bridge construction construction kwajolwayo tugela river pedestrian bridge near tugela ferry duration project months   \n",
       "3                                                                                           netefata gore ba file dilo ka moka te le dumelelanego ka tona mohlala maleri magolo omiwago go fihlelela meagong e metelele scaffolds bolokegilego lefelo la maleba la go omela go phela gabotse bjbj   \n",
       "4                                                   khomishini ya ndinganyiso ya mbeu yo ewa maana u ya nga mulayo wa khomishini ya ndinganyiso ya mbeu u thetshelesa mbilaelo dzine dza tshimbilelana na tshialula u ya nga mbeu nahone ivhea sa foramu ya thungo u ya nga mulayo wa ndinganyiso   \n",
       "...                                                                                                                                                                                                                                                                                           ...   \n",
       "32995             popo ya dipolateforomo tse ke go tlisa boeteledipele ka mo saenseng ya lefatse ka mo go rona mme se ke kgato e kgolo ya go ya pele ka poponthwa ya thekeniki ya biothekeniki ga bua motsamaisi wa thekenoloji ya biotekeniki wa lefapha la saense le thekenoloji mor ben durham   \n",
       "32996  modise mosadi na ntse sa utlwe hore thabang rata ho iketsa sengangele jwang hona jwale ke mona se bolela ka moo batlang ho lokollelwa ngwana wa hae ka teng wena jwale batla ho jarisa nna melato ya hae jwalo ka ha eka ke nna ya mo rommeng hore mathakake le banana ba motse oo kaofela   \n",
       "32997                                                                                                                          closing date submission completed tenders august h late submissions considered submissions must enclosed sealed envelope addressed chief executive officer address   \n",
       "32998                                                        nawuphina umntu ofunyenwe enetyala phantsi kwalo mthetho ujongene ujungene nesohlwayo sokudliwa imali okanye ukuvalelwa ixesha elingadluliyo kwiinyanga ezintandathu okanye kuzo zombini isohlwayo sokudliwa imali okanye ukuvalelwa   \n",
       "32999                                                                                    mafapha mang le ona lokela ho etsa ditlaleho tsa ona ho latela dipehelo tsa nema merero yohle e lokela ho hlahlojwa ke komiti ya ditaba tsa tikoloho eo e kenyeleditseng motsamaisi kakaretso wa lefapha   \n",
       "\n",
       "       text_length  \n",
       "0              220  \n",
       "1              248  \n",
       "2              212  \n",
       "3              197  \n",
       "4              237  \n",
       "...            ...  \n",
       "32995          271  \n",
       "32996          282  \n",
       "32997          162  \n",
       "32998          228  \n",
       "32999          200  \n",
       "\n",
       "[33000 rows x 3 columns]"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[\"text\"] = train[\"text\"].apply(stri)\n",
    "train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6.1 Creating our X and y Metrics\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X_tra = train_real.drop(['lang_id','target'], axis=1)\n",
    "X = train[\"text\"]\n",
    "y = train[\"lang_id\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6.2 Splitting data into train and validation sets\n",
    "\n",
    "Separating data into training and validation sets is an important part of evaluating our models. \n",
    "In our case we will randomly split the train data into 80% train and 20% validation. \n",
    "After our model is trained with the train data we then use it to make predictions for the target using the validation set,Because the data in the validation set already contains known values for the target variable this will make it easy  for us to asses our model's accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train , X_test , y_train , y_test = train_test_split(X , y, stratify=y,\n",
    "                                                       test_size =0.2, \n",
    "                                                       random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "#The several models that will be used on the dataset and compared for performance\n",
    "algo = [LogisticRegression(random_state =42 , max_iter=5000), \n",
    "       MultinomialNB(), \n",
    "       LinearSVC(random_state=42), \n",
    "       RidgeClassifier(random_state=42)\n",
    "       \n",
    "       ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Make tokenizer callable\n",
    "def wordd_tokenizer(text):\n",
    "    return word_tokenize(text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='seven3'></a>\n",
    "\n",
    "#### 7.3 Training Models\n",
    "\n",
    "Each model is trained via a distinct pipeline that accepts raw text input, transforms it into numerical data, and establishes the classifier's default parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _performace_assesment(*args , **kwargs):\n",
    "  model_stats = {}\n",
    "  for clf in algo:\n",
    "    model = Pipeline([('tfidf', TfidfVectorizer(stop_words = 'english', tokenizer=wordd_tokenizer,max_df=0.9, ngram_range=(1, 5), analyzer= 'char')),\n",
    "                      ('clf' , clf)\n",
    "                      ])\n",
    "    \n",
    "    model.fit(X_train, y_train) #Training\n",
    "    model_pred = model.predict(X_test) #Testing\n",
    "\n",
    "    # Dictionary of Models Performances\n",
    "    model_stats[clf.__class__.__name__] = {\n",
    "        'F1-Macro':metrics.f1_score(y_test, model_pred, average='macro'),\n",
    "        'F1-Accuracy':metrics.f1_score(y_test, model_pred, average='micro'),\n",
    "        'F1-Weighted':metrics.f1_score(y_test, model_pred, average='weighted')}\n",
    "  return pd.DataFrame.from_dict(model_stats, orient='index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "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>F1-Macro</th>\n",
       "      <th>F1-Accuracy</th>\n",
       "      <th>F1-Weighted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MultinomialNB</th>\n",
       "      <td>0.999394</td>\n",
       "      <td>0.999394</td>\n",
       "      <td>0.999394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearSVC</th>\n",
       "      <td>0.999394</td>\n",
       "      <td>0.999394</td>\n",
       "      <td>0.999394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RidgeClassifier</th>\n",
       "      <td>0.999243</td>\n",
       "      <td>0.999242</td>\n",
       "      <td>0.999243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.998485</td>\n",
       "      <td>0.998485</td>\n",
       "      <td>0.998485</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    F1-Macro  F1-Accuracy  F1-Weighted\n",
       "MultinomialNB       0.999394     0.999394     0.999394\n",
       "LinearSVC           0.999394     0.999394     0.999394\n",
       "RidgeClassifier     0.999243     0.999242     0.999243\n",
       "LogisticRegression  0.998485     0.998485     0.998485"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "performance = _performace_assesment(algo , X_train , X_test , y_train , y_test)\n",
    "performance.to_csv('performance.csv')\n",
    "dataframe = pd.read_csv('performance.csv', index_col = 0)\n",
    "dataframe.sort_values('F1-Weighted', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperparameter Tuning and Model Validation\n",
    "\n",
    "The process of selecting a group of ideal hyperparameters for a learning algorithm is known as hyperparameter tuning. On the other hand, a hyperparameter is a variable or input whose value is predetermined before the training process starts. When a tuple of hyperparameters are tuned, the result is an ideal model that minimizes a predetermined loss function on input data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _param_tuning(*args , **kwargs):\n",
    "  best_params = {}\n",
    "\n",
    "  for clf in algo:\n",
    "    model = Pipeline([('tfidf', TfidfVectorizer(stop_words = 'english', max_df=0.9, ngram_range=(1, 5), analyzer= 'char')),\n",
    "                      ('clf' , clf)])\n",
    "    model.fit(X_train, y_train) #Training\n",
    "    \n",
    "    #Get models performing parameters\n",
    "    params = model.get_params()\n",
    "    model_name = clf.__class__.__name__ \n",
    "    model_name = {}\n",
    "    for key in params:\n",
    "      if key.startswith(\"clf\"):\n",
    "        if len(key) < 5:\n",
    "          model_name['model'] = params[key]\n",
    "        else:\n",
    "            model_name[key[5:]] = params[key]\n",
    "    best_params[clf.__class__.__name__] = model_name\n",
    "  return best_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_params = _param_tuning(algo, X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'LogisticRegression': {'model': LogisticRegression(max_iter=5000, random_state=42),\n",
       "  'C': 1.0,\n",
       "  'class_weight': None,\n",
       "  'dual': False,\n",
       "  'fit_intercept': True,\n",
       "  'intercept_scaling': 1,\n",
       "  'l1_ratio': None,\n",
       "  'max_iter': 5000,\n",
       "  'multi_class': 'auto',\n",
       "  'n_jobs': None,\n",
       "  'penalty': 'l2',\n",
       "  'random_state': 42,\n",
       "  'solver': 'lbfgs',\n",
       "  'tol': 0.0001,\n",
       "  'verbose': 0,\n",
       "  'warm_start': False},\n",
       " 'MultinomialNB': {'model': MultinomialNB(),\n",
       "  'alpha': 1.0,\n",
       "  'class_prior': None,\n",
       "  'fit_prior': True},\n",
       " 'LinearSVC': {'model': LinearSVC(random_state=42),\n",
       "  'C': 1.0,\n",
       "  'class_weight': None,\n",
       "  'dual': True,\n",
       "  'fit_intercept': True,\n",
       "  'intercept_scaling': 1,\n",
       "  'loss': 'squared_hinge',\n",
       "  'max_iter': 1000,\n",
       "  'multi_class': 'ovr',\n",
       "  'penalty': 'l2',\n",
       "  'random_state': 42,\n",
       "  'tol': 0.0001,\n",
       "  'verbose': 0},\n",
       " 'RidgeClassifier': {'model': RidgeClassifier(random_state=42),\n",
       "  'alpha': 1.0,\n",
       "  'class_weight': None,\n",
       "  'copy_X': True,\n",
       "  'fit_intercept': True,\n",
       "  'max_iter': None,\n",
       "  'normalize': 'deprecated',\n",
       "  'positive': False,\n",
       "  'random_state': 42,\n",
       "  'solver': 'auto',\n",
       "  'tol': 0.001}}"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Best parameters\n",
    "best_params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"eight\"></a>\n",
    "## 8. Modelling and Evaluation\n",
    "<a class=\"anchor\" id=\"1.1\"></a>\n",
    "<a href=#cont>Back to Table of Contents</a>\n",
    "\n",
    "---\n",
    "    \n",
    "| ⚡ Description: Modelling and Evaluation ⚡ |\n",
    "| :--------------------------- |\n",
    "| In this section we would be:we would be evaluating all the models. |\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Multinomial Naive Bayes Classifier was the first modeling technique employed. With discrete features, it is appropriate for categorization (e.g., word counts for text classification)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "#model\n",
    "model1 = MultinomialNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "Vectorize = TfidfVectorizer(stop_words = 'english', max_df=0.9, ngram_range=(1, 5), analyzer= 'char')\n",
    "X_train = Vectorize.fit_transform(X_train)\n",
    "X_test = Vectorize.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "stratified_kfold = StratifiedKFold(n_splits=5, shuffle=True,\n",
    "                                   random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Stratified K-Folds cross-validator.**\n",
    "\n",
    "To divide data into train and test sets, Stratified K-Folds cross-validator offers train/test indices.\n",
    "\n",
    "A KFold variant that returns stratified folds is this cross-validation object. The folds are created by keeping track of the sample percentages for each class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'model': MultinomialNB(),\n",
       " 'alpha': 1.0,\n",
       " 'class_prior': None,\n",
       " 'fit_prior': True}"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# selecting the  best_params\n",
    "best_params[algo[1].__class__.__name__]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "alpha = list(np.linspace(0.1,0.02,4))\n",
    "param_grid = dict(alpha=alpha)\n",
    "grid_search = GridSearchCV(estimator= model1,\n",
    "                           param_grid=param_grid,\n",
    "                           scoring='f1_weighted',\n",
    "                           cv=stratified_kfold,\n",
    "                           error_score=0,\n",
    "                           n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search.fit(X_train, y_train)\n",
    "prediction = grid_search.predict(X_test)\n",
    "cv_score = grid_search.best_score_\n",
    "test_score = grid_search.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-validation score: 0.9998484846840803\n",
      "Test score: 0.9993938128155686\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MultinomialNB(alpha=0.1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MultinomialNB</label><div class=\"sk-toggleable__content\"><pre>MultinomialNB(alpha=0.1)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "MultinomialNB(alpha=0.1)"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(f'Cross-validation score: {cv_score}')\n",
    "print(f'Test score: {test_score}')\n",
    "grid_search.best_params_    \n",
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "#model\n",
    "model2 = RidgeClassifier()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The second model applied to the dataset was **Ridge Classifier**\n",
    "\n",
    "This classifier turns the target values into the values \"-1, 1\" before treating the issue as a regression issue (multi-output regression in the multiclass case)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'model': RidgeClassifier(random_state=42),\n",
       " 'alpha': 1.0,\n",
       " 'class_weight': None,\n",
       " 'copy_X': True,\n",
       " 'fit_intercept': True,\n",
       " 'max_iter': None,\n",
       " 'normalize': 'deprecated',\n",
       " 'positive': False,\n",
       " 'random_state': 42,\n",
       " 'solver': 'auto',\n",
       " 'tol': 0.001}"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_params[algo[3].__class__.__name__]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "alpha = list(np.linspace(0.15,0.4, 5))\n",
    "param_grid = dict(alpha=alpha)\n",
    "grid_search = GridSearchCV(estimator= model2,\n",
    "                           param_grid=param_grid,\n",
    "                           scoring='f1_weighted',\n",
    "                           cv=stratified_kfold,\n",
    "                           error_score=0,\n",
    "                           n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search.fit(X_train, y_train)\n",
    "prediction = grid_search.predict(X_test)\n",
    "cv_score = grid_search.best_score_\n",
    "test_score = grid_search.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-validation score: 0.9993560591687981\n",
      "Test score: 0.9990906558284217\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RidgeClassifier(alpha=0.2125)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RidgeClassifier</label><div class=\"sk-toggleable__content\"><pre>RidgeClassifier(alpha=0.2125)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RidgeClassifier(alpha=0.2125)"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(f'Cross-validation score: {cv_score}')\n",
    "print(f'Test score: {test_score}')\n",
    "grid_search.best_params_    \n",
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predictions\n",
    "\n",
    "The output of each individual estimator is stacked in stacked generalization, and a classifier is used to calculate the final prediction. Using each estimator's output as the input for a final estimator enables stacking, which lets us take use of each estimate's strengths.\n",
    "\n",
    "Consider that although final estimator is trained using cross-validated predictions of the base estimators using cross val predict, estimators_ are fitted to the whole X."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import StackingClassifier\n",
    "from sklearn.pipeline import make_pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train , X_test , y_train , y_test = train_test_split(X, y,  stratify=y, test_size=0.4, random_state =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "vect = TfidfVectorizer(stop_words = 'english', max_df=0.9, ngram_range=(2, 6), analyzer= 'char')\n",
    "X_train = vect.fit_transform(X_train)\n",
    "X_test = vect.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "multiNB1 = MultinomialNB(alpha=0.1)\n",
    "multiNB2 = MultinomialNB(alpha=0.1)\n",
    "\n",
    "estimators = [('multiNB1', multiNB1), ('multiNB2', multiNB2)]\n",
    "final_est = RidgeClassifier(alpha=0.2125)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "stacking_NB2 = StackingClassifier(estimators = estimators,\n",
    "                           final_estimator = final_est,\n",
    "                           passthrough = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StackingClassifier(estimators=[(&#x27;multiNB1&#x27;, MultinomialNB(alpha=0.1)),\n",
       "                               (&#x27;multiNB2&#x27;, MultinomialNB(alpha=0.1))],\n",
       "                   final_estimator=RidgeClassifier(alpha=0.2125),\n",
       "                   passthrough=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StackingClassifier</label><div class=\"sk-toggleable__content\"><pre>StackingClassifier(estimators=[(&#x27;multiNB1&#x27;, MultinomialNB(alpha=0.1)),\n",
       "                               (&#x27;multiNB2&#x27;, MultinomialNB(alpha=0.1))],\n",
       "                   final_estimator=RidgeClassifier(alpha=0.2125),\n",
       "                   passthrough=True)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>multiNB1</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MultinomialNB</label><div class=\"sk-toggleable__content\"><pre>MultinomialNB(alpha=0.1)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>multiNB2</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MultinomialNB</label><div class=\"sk-toggleable__content\"><pre>MultinomialNB(alpha=0.1)</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>final_estimator</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RidgeClassifier</label><div class=\"sk-toggleable__content\"><pre>RidgeClassifier(alpha=0.2125)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "StackingClassifier(estimators=[('multiNB1', MultinomialNB(alpha=0.1)),\n",
       "                               ('multiNB2', MultinomialNB(alpha=0.1))],\n",
       "                   final_estimator=RidgeClassifier(alpha=0.2125),\n",
       "                   passthrough=True)"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stacking_NB2.fit(X_train , y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = stacking_NB2.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F1-Macro</th>\n",
       "      <th>F1-Accuracy</th>\n",
       "      <th>F1-Weighted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>StackingClassifier</th>\n",
       "      <td>0.999848</td>\n",
       "      <td>0.999848</td>\n",
       "      <td>0.999848</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "                    F1-Macro  F1-Accuracy  F1-Weighted\n",
       "StackingClassifier  0.999848     0.999848     0.999848"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_stats = {}\n",
    "model_stats[stacking_NB2.__class__.__name__] = {\n",
    "        'F1-Macro':metrics.f1_score(y_test, pred, average='macro'),\n",
    "        'F1-Accuracy':metrics.f1_score(y_test, pred, average='micro'),\n",
    "        'F1-Weighted':metrics.f1_score(y_test, pred, average='weighted')}\n",
    "pd.DataFrame.from_dict(model_stats, orient='index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "count_vec = CountVectorizer(ngram_range=(3,7), analyzer= 'char')\n",
    "X_train , X_test , y_train , y_test = train_test_split(X, y, stratify=y,test_size=0.05, random_state =1)\n",
    "X_train = count_vec.fit_transform(X_train)\n",
    "X_test = count_vec.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['text'] = test['text'].apply(stri)\n",
    "X = test['text']\n",
    "Vectorize = vect.transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['lang_id'] = stacking_NB2.predict(Vectorize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "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",
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       "        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>index</th>\n",
       "      <th>lang_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>tsn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>nbl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>ven</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>ssw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>afr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5677</th>\n",
       "      <td>5678</td>\n",
       "      <td>eng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5678</th>\n",
       "      <td>5679</td>\n",
       "      <td>nso</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5679</th>\n",
       "      <td>5680</td>\n",
       "      <td>sot</td>\n",
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       "    <tr>\n",
       "      <th>5680</th>\n",
       "      <td>5681</td>\n",
       "      <td>sot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5681</th>\n",
       "      <td>5682</td>\n",
       "      <td>nbl</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5682 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      index lang_id\n",
       "0         1     tsn\n",
       "1         2     nbl\n",
       "2         3     ven\n",
       "3         4     ssw\n",
       "4         5     afr\n",
       "...     ...     ...\n",
       "5677   5678     eng\n",
       "5678   5679     nso\n",
       "5679   5680     sot\n",
       "5680   5681     sot\n",
       "5681   5682     nbl\n",
       "\n",
       "[5682 rows x 2 columns]"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission = test[['index', 'lang_id']]\n",
    "submission.to_csv('silas2.csv',index=False)\n",
    "submission"
   ]
  }
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