Kaggle compet.ipynb

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    "Introduction\n",
    "Language Identification (LID) systems from voice are classification models that predict the spoken language from a given audio recording. The LID systems can facilitate the process of any speech processing system such as speech recognition (ASR) or speech translation systems. In speech-based assistant systems, LID works as a first step by selecting the appropriate grammar from a list of available languages for further semantic analysis. Also, these models can be employed in call centers in order to redirect an international user to an operator who is fluent in that identified language.\n",
    "\n",
    "Objective\n",
    "The objective of this project is to use machine learning methods for constructing a LID model which can discriminate 4 languages; English, French, Arabic, Japanese. There are 2 expected phases. The first phase is constructing a classifier. It is expected to compare the performance of different models, optimize the hyperparameters, and practice of finding the best model. The second phase is the evaluation of the model in a simulated situation of real life deployment. The objective is to understand the challenge of generalization. It's also expected to analyze the result of the model’s performance and make hypothesis about the weak and strong aspect of models. The competition among models' accuracy can provide a better understanding of performance\n",
    "\n",
    "Data\n",
    "The provided dataset has been collected from TEDx talks YouTube for the Language Identification task from audio. The samples are recorded audio of speaker speech from available TEDx talks videos. In order to have a standard sample's type, they follow below convention. The length of recorded audio files should be around 5 seconds (5.00 - 5.99 seconds). The format of audio files should be *.wav. The sample rate of recording files should be 16 kHz (in mono format).\n",
    "\n",
    "Dataset\n",
    "A repository contains recording files in the standard format (.wav, 16kHz, mono, 5-6 seconds) and a .txt file with 4 information (separated by , ) for each recorded file (one file per line) has been provided. The 1st column is the name of *.wav file The 2nd column is the URL address of YouTube video The 3rd column is the starting time of recording from YouTube video The 4th column is the label (language) of recorded speech (EN, FR, AR, JP)\n",
    "\n",
    "Evaluation set\n",
    "A repository contains recording files in the standard format (.wav, 16kHz, mono, 5-6 seconds) and a .txt file with 2 columns as the file names and a the predicted label by your classification model."
   ]
  },
  {
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   "id": "176c9865",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting librosa\n",
      "  Downloading librosa-0.10.0.post2-py3-none-any.whl (253 kB)\n",
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      "Building wheels for collected packages: audioread\n",
      "  Building wheel for audioread (setup.py): started\n",
      "  Building wheel for audioread (setup.py): finished with status 'done'\n",
      "  Created wheel for audioread: filename=audioread-3.0.0-py3-none-any.whl size=23736 sha256=be0c78596b3a440c053fd2ddc52bb1ab3402856a55bab8c78a50a1e5f86c06b5\n",
      "  Stored in directory: c:\\users\\natha\\appdata\\local\\pip\\cache\\wheels\\7a\\6a\\b0\\92760a6d6bc2bff5464970af910c0b0b921390993f3199cdf7\n",
      "Successfully built audioread\n",
      "Installing collected packages: numpy, lazy-loader, audioread, soxr, soundfile, pooch, librosa\n",
      "  Attempting uninstall: numpy\n",
      "    Found existing installation: numpy 1.24.2\n",
      "    Uninstalling numpy-1.24.2:\n",
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      "Successfully installed audioread-3.0.0 lazy-loader-0.2 librosa-0.10.0.post2 numpy-1.21.6 pooch-1.6.0 soundfile-0.12.1 soxr-0.3.4\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
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    {
     "name": "stderr",
     "output_type": "stream",
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      "ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "daal4py 2021.6.0 requires daal==2021.4.0, which is not installed.\n",
      "cvae 0.0.3 requires tensorflow<2,>=1, but you have tensorflow 2.11.0 which is incompatible.\n",
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     ]
    }
   ],
   "source": [
    "pip install --upgrade librosa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f6500930",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting catboost\n",
      "  Using cached catboost-1.1.1-cp39-none-win_amd64.whl (74.0 MB)\n",
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      "Installing collected packages: graphviz, catboost\n",
      "Successfully installed catboost-1.1.1 graphviz-0.20.1\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Ignoring invalid distribution -atplotlib (c:\\users\\natha\\anaconda3\\lib\\site-packages)\n",
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      "WARNING: Ignoring invalid distribution -atplotlib (c:\\users\\natha\\anaconda3\\lib\\site-packages)\n"
     ]
    }
   ],
   "source": [
    "pip install catboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cf75e7b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import librosa\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import IPython.display as ipd\n",
    "import matplotlib.pyplot as plt \n",
    "import random\n",
    "import csv\n",
    "import os\n",
    "\n",
    "import sklearn\n",
    "#from sklearn.cross_validation import train_test_split\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.metrics import confusion_matrix \n",
    "from sklearn import metrics\n",
    "\n",
    "from catboost import CatBoostClassifier, Pool, cv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "71f5cf10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\Natha\\\\emlyon-dsaib\\\\Python tutorship\\\\Projet'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path_to_repo = os.path.dirname(os.path.dirname(os.path.realpath('__file__')))\n",
    "path_to_repo"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94cf9735",
   "metadata": {},
   "source": [
    "# Feature extractor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "01de457f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The duration of FR_001.wav in seconds: 5.494\n"
     ]
    }
   ],
   "source": [
    "# sr should be set to your recording sample rate (16k)\n",
    "# x,freq = librosa.load(\"[your_wav_files_directory]/FR_01.wav\",sr=16000)\n",
    "x,freq = librosa.load('C:\\\\Users\\\\Natha\\\\emlyon-dsaib\\\\Python tutorship\\\\Projet\\\\language-identification-lid2\\\\dataset\\\\partition7\\\\0000.wav',sr=16000)\n",
    "# The load function will return a time series value (x) and\n",
    "#   the input sample rate (freq) which is 16000\n",
    "print(\"The duration of FR_001.wav in seconds:\",len(x)/freq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5c1fb0c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(40, 172)\n"
     ]
    }
   ],
   "source": [
    "# This function will return n_mfcc number of MFCC per\n",
    "#     a window of time in audio time series\n",
    "x_mfcc=librosa.feature.mfcc(y=x,sr=freq, n_mfcc=40)\n",
    "print(x_mfcc.shape)\n",
    "# x_mfcc is an array with 40 values for a window of time\n",
    "# The len(x_mfcc) is a proportion of wav file duration (5-6 seconds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "743ad35f",
   "metadata": {},
   "source": [
    "### Method 1: Computing statistics from time series:¶"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5faa733",
   "metadata": {},
   "source": [
    "By computing mean, variance, median, etc. it is possible to summarize and convert a list of values with variable length to one (or certain number of) value. In this method, the information related to the time will be lost."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "67b4238d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def feature_extractor_1(audio_file_dir):\n",
    "\n",
    "    #load the audio files\n",
    "    x,freq = librosa.load(audio_file_dir,sr=16000)\n",
    "    #extract 20 MFCCs\n",
    "    mfcc=librosa.feature.mfcc(y=x,sr=freq,n_mfcc=20)\n",
    "    #calculate the mean and variance of each MFFC \n",
    "    mean_mfccs=np.mean(mfcc,axis=1)\n",
    "    var_mfccs=np.var(mfcc,axis=1)\n",
    "    #return mean and variance as the audio file feature \n",
    "    return list(mean_mfccs)+list(var_mfccs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd344919",
   "metadata": {},
   "source": [
    "### Method 2 : Converting sample to a fixed length:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87581588",
   "metadata": {},
   "source": [
    "The audio time series data can be cut to a fix length (such as minimum length of 5.00 seconds) which called \\textit{Sequence Truncation}. Another method to have a fix length sequence is padding to a maximum length (for example 6.00 seconds) by concatenating a constant value such as 0 to the end (or beginning) of the sequence. These two model will keep the information related to the time in the time series data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6276d811",
   "metadata": {},
   "outputs": [],
   "source": [
    "def feature_extractor_2(audio_file_dir):\n",
    "\n",
    "    #load the audio files\n",
    "    x,freq = librosa.load(audio_file_dir,sr=16000)\n",
    "    # trim the first 5 seconds (Sequence Truncation)\n",
    "    length_of_5seconds=5*16000\n",
    "    x_5sec=x[:length_of_5seconds]\n",
    "    # extract 20 MFCCs\n",
    "    mfccs_5sec=librosa.feature.mfcc(x_5sec,sr=freq,n_mfcc=20)\n",
    "    # return mfcc of the first 5 sec as the audio file feature\n",
    "    return mfccs_5sec"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "512d3f23",
   "metadata": {},
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "606fb8aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "#set data_dir to the directory of your data files\n",
    "data_dir= \"C:\\\\Users\\\\Natha\\\\emlyon-dsaib\\\\Python tutorship\\\\Projet\\\\language-identification-lid2\\\\dataset\\\\partition7\\\\info.txt\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0f1f0910",
   "metadata": {},
   "outputs": [],
   "source": [
    "file_list=[]\n",
    "label_list=[]\n",
    "\n",
    "\n",
    "with open(data_dir, 'r') as f:\n",
    "    for line in f:\n",
    "        parts = line.strip().split(',')\n",
    "        filename = parts[0]\n",
    "        label = parts[-1]\n",
    "        file_list.append(filename)\n",
    "        label_list.append(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "03927d17",
   "metadata": {},
   "outputs": [
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       " 'JP',\n",
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       " 'JP',\n",
       " 'JP',\n",
       " 'JP',\n",
       " 'JP',\n",
       " 'JP',\n",
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       " 'JP',\n",
       " 'JP',\n",
       " 'JP',\n",
       " 'JP',\n",
       " 'JP',\n",
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       " 'JP',\n",
       " 'JP',\n",
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       " 'JP',\n",
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       " 'JP',\n",
       " 'JP',\n",
       " 'JP',\n",
       " 'JP']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "59aae1c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a dictionary for labels\n",
    "lang_dic={'EN':0,'FR':1,'AR':2,'JP':3}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "74f27e57",
   "metadata": {},
   "outputs": [],
   "source": [
    "#set data_dir to the directory of your data files\n",
    "audio_dir= \"C:\\\\Users\\\\Natha\\\\emlyon-dsaib\\\\Python tutorship\\\\Projet\\\\language-identification-lid2\\\\dataset\\\\partition7\\\\\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a4bcafeb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a list of extracted feature (MFCC) for files\n",
    "x_data=[]\n",
    "\n",
    "for filename in file_list:\n",
    "    # Load audio file\n",
    "    file_path = audio_dir + filename\n",
    "    y, sr = librosa.load(file_path, sr=None)\n",
    "    \n",
    "    # Extract MFCC features and their mean and variance\n",
    "    mfcc_mean_var = feature_extractor_1(file_path)\n",
    "    \n",
    "    # Append to x_data\n",
    "    x_data.append(mfcc_mean_var)\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e1dbd022",
   "metadata": {},
   "outputs": [
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     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "26155170",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40,)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "np.array(x_data)[1].shape"
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  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "38f6aa83",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a list of labels for files\n",
    "y_data = []\n",
    "\n",
    "for label in label_list:\n",
    "    # Split label into its components\n",
    "    label_parts = label.split(',')\n",
    "\n",
    "    # Extract language label and convert to numerical value\n",
    "    lang_label = label_parts[-1].strip()\n",
    "    lang_code = lang_dic[lang_label]\n",
    "\n",
    "    # Append to y_data\n",
    "    y_data.append(lang_code)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a0dc1458",
   "metadata": {},
   "outputs": [
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       " 3,\n",
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       " 3,\n",
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       " 3,\n",
       " 3,\n",
       " 3,\n",
       " 3,\n",
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       " 3,\n",
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       " 3,\n",
       " 3,\n",
       " 3,\n",
       " 3,\n",
       " 3,\n",
       " 3,\n",
       " 3,\n",
       " 3,\n",
       " 3,\n",
       " 3,\n",
       " 3,\n",
       " 3,\n",
       " 3,\n",
       " 3,\n",
       " 3,\n",
       " 3,\n",
       " 3,\n",
       " 3]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7af4060f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# shuffle two lists\n",
    "# Hint : make a list of zip (x_data, y_data) and then use random.shuffle \n",
    "# Create a list of tuples by zipping x_data and y_data together\n",
    "data = list(zip(x_data, y_data))\n",
    "\n",
    "# Shuffle the list of tuples\n",
    "random.shuffle(data)\n",
    "\n",
    "# Unzip the list of tuples to get back the two original lists in shuffled order\n",
    "x_data, y_data = zip(*data)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ba1c24c",
   "metadata": {},
   "source": [
    "# DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5692d3d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1e91f3f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(x_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d3a76fa4",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "      <th>31</th>\n",
       "      <th>32</th>\n",
       "      <th>33</th>\n",
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       "      <th>36</th>\n",
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       "      <td>76.826630</td>\n",
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       "      <td>74.518936</td>\n",
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       "      <th>1</th>\n",
       "      <td>-227.963181</td>\n",
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       "      <td>25.436537</td>\n",
       "      <td>-13.943529</td>\n",
       "      <td>-11.942792</td>\n",
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       "      <td>2.439936</td>\n",
       "      <td>-4.780290</td>\n",
       "      <td>-1.518001</td>\n",
       "      <td>...</td>\n",
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       "      <td>90.275749</td>\n",
       "      <td>157.819168</td>\n",
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       "      <td>84.768921</td>\n",
       "      <td>-14.442950</td>\n",
       "      <td>17.228878</td>\n",
       "      <td>-14.266505</td>\n",
       "      <td>-21.237408</td>\n",
       "      <td>-6.853325</td>\n",
       "      <td>-17.390331</td>\n",
       "      <td>-7.362807</td>\n",
       "      <td>-7.884434</td>\n",
       "      <td>...</td>\n",
       "      <td>73.392982</td>\n",
       "      <td>75.081322</td>\n",
       "      <td>88.733971</td>\n",
       "      <td>43.143719</td>\n",
       "      <td>53.145477</td>\n",
       "      <td>44.339607</td>\n",
       "      <td>41.940868</td>\n",
       "      <td>45.784637</td>\n",
       "      <td>58.414825</td>\n",
       "      <td>33.092609</td>\n",
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       "      <th>3</th>\n",
       "      <td>-408.614471</td>\n",
       "      <td>65.062874</td>\n",
       "      <td>-3.461067</td>\n",
       "      <td>23.298765</td>\n",
       "      <td>-5.167718</td>\n",
       "      <td>-6.868882</td>\n",
       "      <td>-10.238270</td>\n",
       "      <td>-10.013874</td>\n",
       "      <td>-3.846821</td>\n",
       "      <td>-8.435979</td>\n",
       "      <td>...</td>\n",
       "      <td>105.423798</td>\n",
       "      <td>113.685074</td>\n",
       "      <td>205.002029</td>\n",
       "      <td>292.279968</td>\n",
       "      <td>314.066010</td>\n",
       "      <td>213.689926</td>\n",
       "      <td>331.186981</td>\n",
       "      <td>560.553467</td>\n",
       "      <td>452.160522</td>\n",
       "      <td>394.947571</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-282.728638</td>\n",
       "      <td>133.492111</td>\n",
       "      <td>5.505677</td>\n",
       "      <td>-13.809141</td>\n",
       "      <td>6.786129</td>\n",
       "      <td>11.818027</td>\n",
       "      <td>-5.320152</td>\n",
       "      <td>-13.286053</td>\n",
       "      <td>-6.614403</td>\n",
       "      <td>1.260433</td>\n",
       "      <td>...</td>\n",
       "      <td>104.905647</td>\n",
       "      <td>82.350685</td>\n",
       "      <td>76.866112</td>\n",
       "      <td>82.891014</td>\n",
       "      <td>58.766121</td>\n",
       "      <td>54.383972</td>\n",
       "      <td>49.545364</td>\n",
       "      <td>71.772606</td>\n",
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       "      <th>584</th>\n",
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       "      <td>-4.024202</td>\n",
       "      <td>-6.837125</td>\n",
       "      <td>-7.856150</td>\n",
       "      <td>...</td>\n",
       "      <td>61.145725</td>\n",
       "      <td>101.442505</td>\n",
       "      <td>40.355484</td>\n",
       "      <td>32.903236</td>\n",
       "      <td>46.642784</td>\n",
       "      <td>31.679495</td>\n",
       "      <td>36.315010</td>\n",
       "      <td>29.811001</td>\n",
       "      <td>32.518738</td>\n",
       "      <td>37.335407</td>\n",
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       "    <tr>\n",
       "      <th>585</th>\n",
       "      <td>-123.547653</td>\n",
       "      <td>82.733696</td>\n",
       "      <td>-16.028028</td>\n",
       "      <td>15.209191</td>\n",
       "      <td>-17.696552</td>\n",
       "      <td>-15.167856</td>\n",
       "      <td>-19.630817</td>\n",
       "      <td>-10.001487</td>\n",
       "      <td>-3.730552</td>\n",
       "      <td>-2.771024</td>\n",
       "      <td>...</td>\n",
       "      <td>86.325241</td>\n",
       "      <td>97.338722</td>\n",
       "      <td>80.465233</td>\n",
       "      <td>109.561638</td>\n",
       "      <td>50.503128</td>\n",
       "      <td>66.590225</td>\n",
       "      <td>144.432068</td>\n",
       "      <td>201.716476</td>\n",
       "      <td>156.727737</td>\n",
       "      <td>138.671890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586</th>\n",
       "      <td>-355.408539</td>\n",
       "      <td>113.639374</td>\n",
       "      <td>-8.238214</td>\n",
       "      <td>-6.050410</td>\n",
       "      <td>-25.043859</td>\n",
       "      <td>-32.435711</td>\n",
       "      <td>-2.681955</td>\n",
       "      <td>9.396270</td>\n",
       "      <td>-4.411253</td>\n",
       "      <td>9.460797</td>\n",
       "      <td>...</td>\n",
       "      <td>52.955227</td>\n",
       "      <td>42.968689</td>\n",
       "      <td>40.768856</td>\n",
       "      <td>35.323669</td>\n",
       "      <td>27.976421</td>\n",
       "      <td>36.866512</td>\n",
       "      <td>51.115898</td>\n",
       "      <td>61.877457</td>\n",
       "      <td>56.489166</td>\n",
       "      <td>78.596199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>587</th>\n",
       "      <td>-310.173096</td>\n",
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       "      <td>-11.730412</td>\n",
       "      <td>34.159313</td>\n",
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       "      <td>19.629356</td>\n",
       "      <td>1.311089</td>\n",
       "      <td>14.075998</td>\n",
       "      <td>1.686203</td>\n",
       "      <td>5.130901</td>\n",
       "      <td>...</td>\n",
       "      <td>58.052486</td>\n",
       "      <td>43.503632</td>\n",
       "      <td>54.277916</td>\n",
       "      <td>86.208717</td>\n",
       "      <td>62.405777</td>\n",
       "      <td>56.055653</td>\n",
       "      <td>47.341019</td>\n",
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       "      <td>48.265728</td>\n",
       "      <td>57.144554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>-338.266266</td>\n",
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       "      <td>-5.471134</td>\n",
       "      <td>-1.290421</td>\n",
       "      <td>...</td>\n",
       "      <td>105.716637</td>\n",
       "      <td>131.848801</td>\n",
       "      <td>91.873077</td>\n",
       "      <td>50.522659</td>\n",
       "      <td>68.674652</td>\n",
       "      <td>65.790802</td>\n",
       "      <td>50.790550</td>\n",
       "      <td>66.571907</td>\n",
       "      <td>113.074432</td>\n",
       "      <td>36.910999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>589 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             0           1          2          3          4          5   \\\n",
       "0   -248.128159   61.303684 -19.975313  19.798573  -4.117367  -4.895715   \n",
       "1   -227.963181   29.403967 -92.605980  25.436537 -13.943529 -11.942792   \n",
       "2   -367.714355   84.768921 -14.442950  17.228878 -14.266505 -21.237408   \n",
       "3   -408.614471   65.062874  -3.461067  23.298765  -5.167718  -6.868882   \n",
       "4   -282.728638  133.492111   5.505677 -13.809141   6.786129  11.818027   \n",
       "..          ...         ...        ...        ...        ...        ...   \n",
       "584 -347.344604   76.998276  -7.739465  15.402439   0.422429   0.546733   \n",
       "585 -123.547653   82.733696 -16.028028  15.209191 -17.696552 -15.167856   \n",
       "586 -355.408539  113.639374  -8.238214  -6.050410 -25.043859 -32.435711   \n",
       "587 -310.173096   90.196533 -11.730412  34.159313   0.865455  19.629356   \n",
       "588 -338.266266   82.783638  -7.855293  17.250938 -20.955805   1.619784   \n",
       "\n",
       "            6          7         8          9   ...          30          31  \\\n",
       "0   -17.358507 -11.942326 -0.887034 -13.839718  ...   73.389374   76.826630   \n",
       "1   -23.799694   2.439936 -4.780290  -1.518001  ...  159.218857  179.757599   \n",
       "2    -6.853325 -17.390331 -7.362807  -7.884434  ...   73.392982   75.081322   \n",
       "3   -10.238270 -10.013874 -3.846821  -8.435979  ...  105.423798  113.685074   \n",
       "4    -5.320152 -13.286053 -6.614403   1.260433  ...  104.905647   82.350685   \n",
       "..         ...        ...       ...        ...  ...         ...         ...   \n",
       "584  -5.391399  -4.024202 -6.837125  -7.856150  ...   61.145725  101.442505   \n",
       "585 -19.630817 -10.001487 -3.730552  -2.771024  ...   86.325241   97.338722   \n",
       "586  -2.681955   9.396270 -4.411253   9.460797  ...   52.955227   42.968689   \n",
       "587   1.311089  14.075998  1.686203   5.130901  ...   58.052486   43.503632   \n",
       "588  -4.997383  -3.794005 -5.471134  -1.290421  ...  105.716637  131.848801   \n",
       "\n",
       "             32          33          34          35          36          37  \\\n",
       "0     76.895599   87.658760   74.518936   63.381691   63.509331   88.355316   \n",
       "1    128.857239  100.173042  123.832306  172.844879  147.451279   90.275749   \n",
       "2     88.733971   43.143719   53.145477   44.339607   41.940868   45.784637   \n",
       "3    205.002029  292.279968  314.066010  213.689926  331.186981  560.553467   \n",
       "4     76.866112   82.891014   58.766121   54.383972   49.545364   71.772606   \n",
       "..          ...         ...         ...         ...         ...         ...   \n",
       "584   40.355484   32.903236   46.642784   31.679495   36.315010   29.811001   \n",
       "585   80.465233  109.561638   50.503128   66.590225  144.432068  201.716476   \n",
       "586   40.768856   35.323669   27.976421   36.866512   51.115898   61.877457   \n",
       "587   54.277916   86.208717   62.405777   56.055653   47.341019   50.427452   \n",
       "588   91.873077   50.522659   68.674652   65.790802   50.790550   66.571907   \n",
       "\n",
       "             38          39  \n",
       "0    143.243225  224.491592  \n",
       "1    157.819168  122.855034  \n",
       "2     58.414825   33.092609  \n",
       "3    452.160522  394.947571  \n",
       "4     54.621117   37.779202  \n",
       "..          ...         ...  \n",
       "584   32.518738   37.335407  \n",
       "585  156.727737  138.671890  \n",
       "586   56.489166   78.596199  \n",
       "587   48.265728   57.144554  \n",
       "588  113.074432   36.910999  \n",
       "\n",
       "[589 rows x 40 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c7e4feb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tuple"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(y_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "48f67289",
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = pd.Series(y_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "19c709a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[40] = labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9efa9266",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns={ 40: 'Labels'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "6c55fb7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>29.403967</td>\n",
       "      <td>-92.605980</td>\n",
       "      <td>25.436537</td>\n",
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       "      <td>-4.780290</td>\n",
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       "      <td>17.228878</td>\n",
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       "      <td>-21.237408</td>\n",
       "      <td>-6.853325</td>\n",
       "      <td>-17.390331</td>\n",
       "      <td>-7.362807</td>\n",
       "      <td>-7.884434</td>\n",
       "      <td>...</td>\n",
       "      <td>75.081322</td>\n",
       "      <td>88.733971</td>\n",
       "      <td>43.143719</td>\n",
       "      <td>53.145477</td>\n",
       "      <td>44.339607</td>\n",
       "      <td>41.940868</td>\n",
       "      <td>45.784637</td>\n",
       "      <td>58.414825</td>\n",
       "      <td>33.092609</td>\n",
       "      <td>2</td>\n",
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       "      <th>3</th>\n",
       "      <td>-408.614471</td>\n",
       "      <td>65.062874</td>\n",
       "      <td>-3.461067</td>\n",
       "      <td>23.298765</td>\n",
       "      <td>-5.167718</td>\n",
       "      <td>-6.868882</td>\n",
       "      <td>-10.238270</td>\n",
       "      <td>-10.013874</td>\n",
       "      <td>-3.846821</td>\n",
       "      <td>-8.435979</td>\n",
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       "      <td>113.685074</td>\n",
       "      <td>205.002029</td>\n",
       "      <td>292.279968</td>\n",
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       "      <td>560.553467</td>\n",
       "      <td>452.160522</td>\n",
       "      <td>394.947571</td>\n",
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       "      <td>6.786129</td>\n",
       "      <td>11.818027</td>\n",
       "      <td>-5.320152</td>\n",
       "      <td>-13.286053</td>\n",
       "      <td>-6.614403</td>\n",
       "      <td>1.260433</td>\n",
       "      <td>...</td>\n",
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       "      <td>76.866112</td>\n",
       "      <td>82.891014</td>\n",
       "      <td>58.766121</td>\n",
       "      <td>54.383972</td>\n",
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       "      <td>71.772606</td>\n",
       "      <td>54.621117</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>584</th>\n",
       "      <td>-347.344604</td>\n",
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       "      <td>-4.024202</td>\n",
       "      <td>-6.837125</td>\n",
       "      <td>-7.856150</td>\n",
       "      <td>...</td>\n",
       "      <td>101.442505</td>\n",
       "      <td>40.355484</td>\n",
       "      <td>32.903236</td>\n",
       "      <td>46.642784</td>\n",
       "      <td>31.679495</td>\n",
       "      <td>36.315010</td>\n",
       "      <td>29.811001</td>\n",
       "      <td>32.518738</td>\n",
       "      <td>37.335407</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>585</th>\n",
       "      <td>-123.547653</td>\n",
       "      <td>82.733696</td>\n",
       "      <td>-16.028028</td>\n",
       "      <td>15.209191</td>\n",
       "      <td>-17.696552</td>\n",
       "      <td>-15.167856</td>\n",
       "      <td>-19.630817</td>\n",
       "      <td>-10.001487</td>\n",
       "      <td>-3.730552</td>\n",
       "      <td>-2.771024</td>\n",
       "      <td>...</td>\n",
       "      <td>97.338722</td>\n",
       "      <td>80.465233</td>\n",
       "      <td>109.561638</td>\n",
       "      <td>50.503128</td>\n",
       "      <td>66.590225</td>\n",
       "      <td>144.432068</td>\n",
       "      <td>201.716476</td>\n",
       "      <td>156.727737</td>\n",
       "      <td>138.671890</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>586</th>\n",
       "      <td>-355.408539</td>\n",
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       "      <td>-8.238214</td>\n",
       "      <td>-6.050410</td>\n",
       "      <td>-25.043859</td>\n",
       "      <td>-32.435711</td>\n",
       "      <td>-2.681955</td>\n",
       "      <td>9.396270</td>\n",
       "      <td>-4.411253</td>\n",
       "      <td>9.460797</td>\n",
       "      <td>...</td>\n",
       "      <td>42.968689</td>\n",
       "      <td>40.768856</td>\n",
       "      <td>35.323669</td>\n",
       "      <td>27.976421</td>\n",
       "      <td>36.866512</td>\n",
       "      <td>51.115898</td>\n",
       "      <td>61.877457</td>\n",
       "      <td>56.489166</td>\n",
       "      <td>78.596199</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>587</th>\n",
       "      <td>-310.173096</td>\n",
       "      <td>90.196533</td>\n",
       "      <td>-11.730412</td>\n",
       "      <td>34.159313</td>\n",
       "      <td>0.865455</td>\n",
       "      <td>19.629356</td>\n",
       "      <td>1.311089</td>\n",
       "      <td>14.075998</td>\n",
       "      <td>1.686203</td>\n",
       "      <td>5.130901</td>\n",
       "      <td>...</td>\n",
       "      <td>43.503632</td>\n",
       "      <td>54.277916</td>\n",
       "      <td>86.208717</td>\n",
       "      <td>62.405777</td>\n",
       "      <td>56.055653</td>\n",
       "      <td>47.341019</td>\n",
       "      <td>50.427452</td>\n",
       "      <td>48.265728</td>\n",
       "      <td>57.144554</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>-338.266266</td>\n",
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       "      <td>-7.855293</td>\n",
       "      <td>17.250938</td>\n",
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       "      <td>1.619784</td>\n",
       "      <td>-4.997383</td>\n",
       "      <td>-3.794005</td>\n",
       "      <td>-5.471134</td>\n",
       "      <td>-1.290421</td>\n",
       "      <td>...</td>\n",
       "      <td>131.848801</td>\n",
       "      <td>91.873077</td>\n",
       "      <td>50.522659</td>\n",
       "      <td>68.674652</td>\n",
       "      <td>65.790802</td>\n",
       "      <td>50.790550</td>\n",
       "      <td>66.571907</td>\n",
       "      <td>113.074432</td>\n",
       "      <td>36.910999</td>\n",
       "      <td>3</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>589 rows × 41 columns</p>\n",
       "</div>"
      ],
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       "4   -282.728638  133.492111   5.505677 -13.809141   6.786129  11.818027   \n",
       "..          ...         ...        ...        ...        ...        ...   \n",
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       "\n",
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       "4    -5.320152 -13.286053 -6.614403   1.260433  ...   82.350685   76.866112   \n",
       "..         ...        ...       ...        ...  ...         ...         ...   \n",
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       "\n",
       "             33          34          35          36          37          38  \\\n",
       "0     87.658760   74.518936   63.381691   63.509331   88.355316  143.243225   \n",
       "1    100.173042  123.832306  172.844879  147.451279   90.275749  157.819168   \n",
       "2     43.143719   53.145477   44.339607   41.940868   45.784637   58.414825   \n",
       "3    292.279968  314.066010  213.689926  331.186981  560.553467  452.160522   \n",
       "4     82.891014   58.766121   54.383972   49.545364   71.772606   54.621117   \n",
       "..          ...         ...         ...         ...         ...         ...   \n",
       "584   32.903236   46.642784   31.679495   36.315010   29.811001   32.518738   \n",
       "585  109.561638   50.503128   66.590225  144.432068  201.716476  156.727737   \n",
       "586   35.323669   27.976421   36.866512   51.115898   61.877457   56.489166   \n",
       "587   86.208717   62.405777   56.055653   47.341019   50.427452   48.265728   \n",
       "588   50.522659   68.674652   65.790802   50.790550   66.571907  113.074432   \n",
       "\n",
       "             39  Labels  \n",
       "0    224.491592       1  \n",
       "1    122.855034       1  \n",
       "2     33.092609       2  \n",
       "3    394.947571       3  \n",
       "4     37.779202       3  \n",
       "..          ...     ...  \n",
       "584   37.335407       2  \n",
       "585  138.671890       0  \n",
       "586   78.596199       1  \n",
       "587   57.144554       0  \n",
       "588   36.910999       3  \n",
       "\n",
       "[589 rows x 41 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "110dcab2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         0\n",
       "1         0\n",
       "2         0\n",
       "3         0\n",
       "4         0\n",
       "5         0\n",
       "6         0\n",
       "7         0\n",
       "8         0\n",
       "9         0\n",
       "10        0\n",
       "11        0\n",
       "12        0\n",
       "13        0\n",
       "14        0\n",
       "15        0\n",
       "16        0\n",
       "17        0\n",
       "18        0\n",
       "19        0\n",
       "20        0\n",
       "21        0\n",
       "22        0\n",
       "23        0\n",
       "24        0\n",
       "25        0\n",
       "26        0\n",
       "27        0\n",
       "28        0\n",
       "29        0\n",
       "30        0\n",
       "31        0\n",
       "32        0\n",
       "33        0\n",
       "34        0\n",
       "35        0\n",
       "36        0\n",
       "37        0\n",
       "38        0\n",
       "39        0\n",
       "Labels    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de7b9d17",
   "metadata": {},
   "source": [
    "# Split Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "0d9b3c65",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(589, 40) (589,)\n"
     ]
    }
   ],
   "source": [
    "X = x_data\n",
    "# y = target = the column we are learning to predict. \n",
    "y = y_data\n",
    "print(np.array(X).shape,np.array(y).shape)\n",
    "# split X and y into training and testing sets. By default, it splits 75% training and 25% test\n",
    "# random_state=1 for reproducibility\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.25,random_state=1)\n",
    "#print(X_train.shape, y_train.shape)\n",
    "#print(X_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee890b14",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "244d80b3",
   "metadata": {},
   "source": [
    "# RandomForest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d8fbf991",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to plot confusion matrix. \n",
    "# Ref:http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html\n",
    "import itertools\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label',fontsize=15)\n",
    "    plt.xlabel('Predicted label',fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1297f395",
   "metadata": {},
   "source": [
    "# Base Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "38fa8b95",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.7094594594594594\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a Random Forest classifier object\n",
    "classifier = RandomForestClassifier()\n",
    "\n",
    "# Train the classifier\n",
    "classifier.fit(X_train, y_train)\n",
    "\n",
    "#Make predictions\n",
    "y_pred_class = classifier.predict(X_test)\n",
    "# print the model's accuracy\n",
    "print(\"Accuracy: \", metrics.accuracy_score(y_test, y_pred_class))\n",
    "\n",
    "# compute the confusion matrix\n",
    "cnf_matrix = confusion_matrix(y_test,y_pred_class)\n",
    "\n",
    "\n",
    "# plot cnf_matrix\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix, classes=['EN','FR','AR','JP'],normalize=True,\n",
    "                      title='Confusion matrix with all features')\n",
    "\n",
    "# calculate AUC\n",
    "y_pred_prob = classifier.predict_proba(X_test)[:, 1]\n",
    "# = roc_auc_score(y_test, y_pred_prob)\n",
    "#print(\"ROC_AUC_Score: \", roc_auc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14309968",
   "metadata": {},
   "source": [
    "# Max Depht"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4feec15d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.6824324324324325\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a Random Forest classifier object\n",
    "classifier = RandomForestClassifier(max_depth=9)\n",
    "\n",
    "# Train the classifier\n",
    "classifier.fit(X_train, y_train)\n",
    "\n",
    "#Make predictions\n",
    "y_pred_class = classifier.predict(X_test)\n",
    "# print the model's accuracy\n",
    "print(\"Accuracy: \", metrics.accuracy_score(y_test, y_pred_class))\n",
    "\n",
    "# compute the confusion matrix\n",
    "cnf_matrix = confusion_matrix(y_test,y_pred_class)\n",
    "\n",
    "\n",
    "# plot cnf_matrix\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix, classes=['EN','FR','AR','JP'],normalize=True,\n",
    "                      title='Confusion matrix with all features')\n",
    "\n",
    "# calculate AUC\n",
    "y_pred_prob = classifier.predict_proba(X_test)[:, 1]\n",
    "# = roc_auc_score(y_test, y_pred_prob)\n",
    "#print(\"ROC_AUC_Score: \", roc_auc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "973a2bf7",
   "metadata": {},
   "source": [
    "# n_estimators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ee3cf7cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((100, 148),\n",
       " array([2., 0., 1., 0., 1., 0., 0., 1., 0., 3., 2., 0., 2., 2., 0., 1., 0.,\n",
       "        1., 1., 2., 2., 2., 1., 1., 1., 2., 1., 1., 0., 1., 1., 3., 2., 3.,\n",
       "        3., 0., 1., 1., 1., 3., 1., 2., 2., 1., 1., 2., 2., 1., 1., 2., 1.,\n",
       "        1., 0., 0., 0., 3., 3., 2., 1., 2., 3., 1., 1., 3., 0., 1., 0., 1.,\n",
       "        1., 0., 0., 1., 1., 1., 0., 1., 1., 3., 1., 2., 1., 2., 1., 2., 3.,\n",
       "        2., 1., 0., 1., 0., 1., 1., 1., 0., 1., 0., 1., 1., 1., 0.]),\n",
       " 1.19,\n",
       " 1)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# all tree predictions on all data\n",
    "preds = np.stack([tree.predict(X_test) for tree in classifier.estimators_]) # or np.asarray()\n",
    "\n",
    "# predictions of the 10 trees, mean, and actual value (for first point in validation set)\n",
    "preds.shape, preds[:,0], np.mean(preds[:,0]), y_test[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "9eee582d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x21524a0f6a0>]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([\n",
    "    metrics.r2_score(y_test, np.mean(preds[:num_trees], axis = 0)) \n",
    "    for num_trees in range(1, 61)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "ae9e1b81",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.581081081081081\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a Random Forest classifier object\n",
    "classifier = RandomForestClassifier(n_estimators=20, max_depth=8)\n",
    "\n",
    "# Train the classifier\n",
    "classifier.fit(X_train, y_train)\n",
    "\n",
    "#Make predictions\n",
    "y_pred_class = classifier.predict(X_test)\n",
    "# print the model's accuracy\n",
    "print(\"Accuracy: \", metrics.accuracy_score(y_test, y_pred_class))\n",
    "\n",
    "# compute the confusion matrix\n",
    "cnf_matrix = confusion_matrix(y_test,y_pred_class)\n",
    "\n",
    "\n",
    "# plot cnf_matrix\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix, classes=['EN','FR','AR','JP'],normalize=True,\n",
    "                      title='Confusion matrix with all features')\n",
    "\n",
    "# calculate AUC\n",
    "y_pred_prob = classifier.predict_proba(X_test)[:, 1]\n",
    "# = roc_auc_score(y_test, y_pred_prob)\n",
    "#print(\"ROC_AUC_Score: \", roc_auc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f6b5305",
   "metadata": {},
   "source": [
    "# Final model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ffbe45b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[-310.8645,\n",
       "  75.35624,\n",
       "  -36.131123,\n",
       "  -24.358885,\n",
       "  -41.369907,\n",
       "  -18.476292,\n",
       "  9.404835,\n",
       "  12.60756,\n",
       "  -13.367997,\n",
       "  11.142961,\n",
       "  4.21523,\n",
       "  2.6318753,\n",
       "  -0.06537817,\n",
       "  22.39306,\n",
       "  -7.3038154,\n",
       "  4.9740686,\n",
       "  -4.6652617,\n",
       "  10.28968,\n",
       "  -7.8345976,\n",
       "  -5.934568,\n",
       "  10169.09,\n",
       "  1298.4164,\n",
       "  631.3558,\n",
       "  702.8438,\n",
       "  391.2146,\n",
       "  245.53572,\n",
       "  165.53355,\n",
       "  78.63838,\n",
       "  136.31277,\n",
       "  70.70082,\n",
       "  58.63886,\n",
       "  63.49769,\n",
       "  29.229853,\n",
       "  43.046387,\n",
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     "execution_count": 40,
     "metadata": {},
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  {
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   "execution_count": 41,
   "id": "4b078d7c",
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       " 2,\n",
       " 3,\n",
       " 1,\n",
       " 3,\n",
       " 0,\n",
       " 0,\n",
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       " 1,\n",
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       " 3,\n",
       " 1,\n",
       " 2,\n",
       " 2,\n",
       " 2,\n",
       " 3,\n",
       " 0,\n",
       " 2,\n",
       " 2,\n",
       " 1,\n",
       " 3,\n",
       " 3,\n",
       " 2,\n",
       " 2,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 0,\n",
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       " 3,\n",
       " 1,\n",
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       " 2,\n",
       " 0,\n",
       " 2,\n",
       " 2,\n",
       " 3,\n",
       " 0,\n",
       " 3,\n",
       " 1,\n",
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       " 2,\n",
       " 3,\n",
       " 3,\n",
       " 1,\n",
       " 1,\n",
       " 3,\n",
       " 3,\n",
       " 3,\n",
       " 3,\n",
       " 1,\n",
       " 0,\n",
       " 2,\n",
       " 0,\n",
       " 2,\n",
       " 2,\n",
       " 2,\n",
       " 2,\n",
       " 3,\n",
       " 2,\n",
       " 0,\n",
       " 1,\n",
       " 2,\n",
       " 2,\n",
       " 0,\n",
       " 0,\n",
       " 0,\n",
       " 0,\n",
       " 1,\n",
       " 1,\n",
       " 0,\n",
       " 2,\n",
       " 0,\n",
       " 3,\n",
       " 3,\n",
       " 1,\n",
       " 2,\n",
       " 0,\n",
       " 3,\n",
       " 2,\n",
       " 3,\n",
       " 2,\n",
       " 2]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f45e3596",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.6081081081081081\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a Random Forest classifier object\n",
    "clf = RandomForestClassifier(n_estimators=20, max_depth=8)\n",
    "\n",
    "# Train the classifier\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "#Make predictions\n",
    "y_pred_class = clf.predict(X_test)\n",
    "# print the model's accuracy\n",
    "print(\"Accuracy: \", metrics.accuracy_score(y_test, y_pred_class))\n",
    "\n",
    "# compute the confusion matrix\n",
    "cnf_matrix = confusion_matrix(y_test,y_pred_class)\n",
    "\n",
    "\n",
    "# plot cnf_matrix\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix, classes=['EN','FR','AR','JP'],normalize=True,\n",
    "                      title='Confusion matrix with all features')\n",
    "\n",
    "# # calculate AUC\n",
    "# y_pred_prob = clf.predict_proba(X_test)[:, 1]\n",
    "# # = roc_auc_score(y_test, y_pred_prob)\n",
    "# #print(\"ROC_AUC_Score: \", roc_auc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c93c4016",
   "metadata": {},
   "source": [
    "# CatBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "80b152cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from catboost import CatBoostClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "3e0d6559",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\tlearn: 1.3545675\ttotal: 382ms\tremaining: 37.8s\n",
      "1:\tlearn: 1.3310633\ttotal: 576ms\tremaining: 28.2s\n",
      "2:\tlearn: 1.3070953\ttotal: 774ms\tremaining: 25s\n",
      "3:\tlearn: 1.2838564\ttotal: 973ms\tremaining: 23.4s\n",
      "4:\tlearn: 1.2609378\ttotal: 1.17s\tremaining: 22.2s\n",
      "5:\tlearn: 1.2351633\ttotal: 1.34s\tremaining: 21s\n",
      "6:\tlearn: 1.2126844\ttotal: 1.53s\tremaining: 20.3s\n",
      "7:\tlearn: 1.1882463\ttotal: 1.71s\tremaining: 19.7s\n",
      "8:\tlearn: 1.1697743\ttotal: 1.91s\tremaining: 19.3s\n",
      "9:\tlearn: 1.1505781\ttotal: 2.1s\tremaining: 18.9s\n",
      "10:\tlearn: 1.1335646\ttotal: 2.28s\tremaining: 18.5s\n",
      "11:\tlearn: 1.1129043\ttotal: 2.46s\tremaining: 18.1s\n",
      "12:\tlearn: 1.0984837\ttotal: 2.64s\tremaining: 17.7s\n",
      "13:\tlearn: 1.0819244\ttotal: 2.82s\tremaining: 17.3s\n",
      "14:\tlearn: 1.0637080\ttotal: 2.99s\tremaining: 17s\n",
      "15:\tlearn: 1.0504454\ttotal: 3.17s\tremaining: 16.6s\n",
      "16:\tlearn: 1.0313375\ttotal: 3.35s\tremaining: 16.3s\n",
      "17:\tlearn: 1.0123988\ttotal: 3.52s\tremaining: 16s\n",
      "18:\tlearn: 0.9975816\ttotal: 3.7s\tremaining: 15.8s\n",
      "19:\tlearn: 0.9811341\ttotal: 3.88s\tremaining: 15.5s\n",
      "20:\tlearn: 0.9684130\ttotal: 4.06s\tremaining: 15.3s\n",
      "21:\tlearn: 0.9550634\ttotal: 4.24s\tremaining: 15s\n",
      "22:\tlearn: 0.9377828\ttotal: 4.42s\tremaining: 14.8s\n",
      "23:\tlearn: 0.9235951\ttotal: 4.61s\tremaining: 14.6s\n",
      "24:\tlearn: 0.9065519\ttotal: 4.79s\tremaining: 14.4s\n",
      "25:\tlearn: 0.8918950\ttotal: 4.97s\tremaining: 14.1s\n",
      "26:\tlearn: 0.8774767\ttotal: 5.15s\tremaining: 13.9s\n",
      "27:\tlearn: 0.8654426\ttotal: 5.33s\tremaining: 13.7s\n",
      "28:\tlearn: 0.8532828\ttotal: 5.51s\tremaining: 13.5s\n",
      "29:\tlearn: 0.8412812\ttotal: 5.68s\tremaining: 13.2s\n",
      "30:\tlearn: 0.8305009\ttotal: 5.85s\tremaining: 13s\n",
      "31:\tlearn: 0.8212287\ttotal: 6.02s\tremaining: 12.8s\n",
      "32:\tlearn: 0.8104197\ttotal: 6.19s\tremaining: 12.6s\n",
      "33:\tlearn: 0.7987599\ttotal: 6.37s\tremaining: 12.4s\n",
      "34:\tlearn: 0.7883320\ttotal: 6.54s\tremaining: 12.1s\n",
      "35:\tlearn: 0.7774906\ttotal: 6.71s\tremaining: 11.9s\n",
      "36:\tlearn: 0.7655916\ttotal: 6.89s\tremaining: 11.7s\n",
      "37:\tlearn: 0.7548187\ttotal: 7.07s\tremaining: 11.5s\n",
      "38:\tlearn: 0.7447926\ttotal: 7.24s\tremaining: 11.3s\n",
      "39:\tlearn: 0.7321092\ttotal: 7.41s\tremaining: 11.1s\n",
      "40:\tlearn: 0.7222576\ttotal: 7.58s\tremaining: 10.9s\n",
      "41:\tlearn: 0.7114614\ttotal: 7.78s\tremaining: 10.7s\n",
      "42:\tlearn: 0.7005558\ttotal: 7.99s\tremaining: 10.6s\n",
      "43:\tlearn: 0.6910029\ttotal: 8.2s\tremaining: 10.4s\n",
      "44:\tlearn: 0.6804807\ttotal: 8.4s\tremaining: 10.3s\n",
      "45:\tlearn: 0.6722272\ttotal: 8.62s\tremaining: 10.1s\n",
      "46:\tlearn: 0.6646659\ttotal: 8.83s\tremaining: 9.95s\n",
      "47:\tlearn: 0.6557043\ttotal: 9.06s\tremaining: 9.81s\n",
      "48:\tlearn: 0.6472309\ttotal: 9.27s\tremaining: 9.65s\n",
      "49:\tlearn: 0.6383547\ttotal: 9.48s\tremaining: 9.48s\n",
      "50:\tlearn: 0.6302701\ttotal: 9.69s\tremaining: 9.31s\n",
      "51:\tlearn: 0.6221408\ttotal: 9.89s\tremaining: 9.13s\n",
      "52:\tlearn: 0.6139080\ttotal: 10.1s\tremaining: 8.94s\n",
      "53:\tlearn: 0.6051314\ttotal: 10.3s\tremaining: 8.75s\n",
      "54:\tlearn: 0.5959992\ttotal: 10.5s\tremaining: 8.56s\n",
      "55:\tlearn: 0.5890032\ttotal: 10.7s\tremaining: 8.38s\n",
      "56:\tlearn: 0.5811059\ttotal: 10.9s\tremaining: 8.2s\n",
      "57:\tlearn: 0.5734958\ttotal: 11.1s\tremaining: 8.03s\n",
      "58:\tlearn: 0.5653843\ttotal: 11.3s\tremaining: 7.85s\n",
      "59:\tlearn: 0.5601661\ttotal: 11.5s\tremaining: 7.67s\n",
      "60:\tlearn: 0.5537295\ttotal: 11.7s\tremaining: 7.48s\n",
      "61:\tlearn: 0.5486444\ttotal: 11.9s\tremaining: 7.29s\n",
      "62:\tlearn: 0.5408855\ttotal: 12.1s\tremaining: 7.11s\n",
      "63:\tlearn: 0.5339536\ttotal: 12.3s\tremaining: 6.92s\n",
      "64:\tlearn: 0.5279864\ttotal: 12.5s\tremaining: 6.74s\n",
      "65:\tlearn: 0.5214594\ttotal: 12.7s\tremaining: 6.55s\n",
      "66:\tlearn: 0.5140730\ttotal: 12.9s\tremaining: 6.35s\n",
      "67:\tlearn: 0.5082391\ttotal: 13.1s\tremaining: 6.17s\n",
      "68:\tlearn: 0.5021282\ttotal: 13.3s\tremaining: 5.98s\n",
      "69:\tlearn: 0.4958051\ttotal: 13.5s\tremaining: 5.79s\n",
      "70:\tlearn: 0.4895647\ttotal: 13.7s\tremaining: 5.6s\n",
      "71:\tlearn: 0.4833086\ttotal: 13.9s\tremaining: 5.41s\n",
      "72:\tlearn: 0.4783170\ttotal: 14.1s\tremaining: 5.22s\n",
      "73:\tlearn: 0.4714209\ttotal: 14.3s\tremaining: 5.03s\n",
      "74:\tlearn: 0.4648586\ttotal: 14.5s\tremaining: 4.83s\n",
      "75:\tlearn: 0.4599928\ttotal: 14.7s\tremaining: 4.65s\n",
      "76:\tlearn: 0.4535926\ttotal: 14.9s\tremaining: 4.46s\n",
      "77:\tlearn: 0.4484792\ttotal: 15.1s\tremaining: 4.27s\n",
      "78:\tlearn: 0.4431020\ttotal: 15.3s\tremaining: 4.07s\n",
      "79:\tlearn: 0.4365789\ttotal: 15.5s\tremaining: 3.88s\n",
      "80:\tlearn: 0.4311651\ttotal: 15.7s\tremaining: 3.69s\n",
      "81:\tlearn: 0.4269555\ttotal: 15.9s\tremaining: 3.49s\n",
      "82:\tlearn: 0.4207645\ttotal: 16.1s\tremaining: 3.3s\n",
      "83:\tlearn: 0.4174128\ttotal: 16.3s\tremaining: 3.1s\n",
      "84:\tlearn: 0.4128122\ttotal: 16.5s\tremaining: 2.91s\n",
      "85:\tlearn: 0.4082144\ttotal: 16.7s\tremaining: 2.72s\n",
      "86:\tlearn: 0.4033348\ttotal: 16.9s\tremaining: 2.52s\n",
      "87:\tlearn: 0.3997260\ttotal: 17.1s\tremaining: 2.33s\n",
      "88:\tlearn: 0.3945866\ttotal: 17.3s\tremaining: 2.14s\n",
      "89:\tlearn: 0.3902153\ttotal: 17.5s\tremaining: 1.95s\n",
      "90:\tlearn: 0.3866805\ttotal: 17.7s\tremaining: 1.75s\n",
      "91:\tlearn: 0.3823706\ttotal: 17.9s\tremaining: 1.56s\n",
      "92:\tlearn: 0.3775596\ttotal: 18.1s\tremaining: 1.36s\n",
      "93:\tlearn: 0.3732649\ttotal: 18.3s\tremaining: 1.17s\n",
      "94:\tlearn: 0.3686212\ttotal: 18.5s\tremaining: 971ms\n",
      "95:\tlearn: 0.3656769\ttotal: 18.6s\tremaining: 777ms\n",
      "96:\tlearn: 0.3616765\ttotal: 18.8s\tremaining: 582ms\n",
      "97:\tlearn: 0.3575345\ttotal: 19s\tremaining: 388ms\n",
      "98:\tlearn: 0.3534935\ttotal: 19.2s\tremaining: 194ms\n",
      "99:\tlearn: 0.3497168\ttotal: 19.4s\tremaining: 0us\n",
      "Accuracy:  0.6824324324324325\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a Random Forest classifier object\n",
    "cbm = CatBoostClassifier(iterations=100, depth=8, learning_rate=0.1, loss_function='MultiClass')\n",
    "\n",
    "# Train the classifier\n",
    "cbm.fit(X_train, y_train)\n",
    "\n",
    "#Make predictions\n",
    "y_pred_class = cbm.predict(X_test)\n",
    "# print the model's accuracy\n",
    "print(\"Accuracy: \", metrics.accuracy_score(y_test, y_pred_class))\n",
    "\n",
    "# compute the confusion matrix\n",
    "cnf_matrix = confusion_matrix(y_test,y_pred_class)\n",
    "\n",
    "\n",
    "# plot cnf_matrix\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix, classes=['EN','FR','AR','JP'],normalize=True,\n",
    "                      title='Confusion matrix with all features')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "ed183794",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up data as Pool object\n",
    "train_data = Pool(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "749ff275",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up CatBoostClassifier with default hyperparameters\n",
    "clf = CatBoostClassifier()\n",
    "\n",
    "# Define hyperparameters to tune\n",
    "params = {\n",
    "    'iterations': [500, 1000, 1500],\n",
    "    'learning_rate': [0.01, 0.03, 0.05],\n",
    "    'depth': [4, 6, 8],\n",
    "    'l2_leaf_reg': [1, 3, 5],\n",
    "    'bagging_temperature': [0.5, 1, 1.5]\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5db088d",
   "metadata": {},
   "source": [
    "# HP TUNING : Iterations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "8e414185",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "72:\tlearn: 0.2488561\ttotal: 13.8s\tremaining: 5.11s\n",
      "73:\tlearn: 0.2431591\ttotal: 14s\tremaining: 4.93s\n",
      "74:\tlearn: 0.2379499\ttotal: 14.2s\tremaining: 4.75s\n",
      "75:\tlearn: 0.2346900\ttotal: 14.4s\tremaining: 4.55s\n",
      "76:\tlearn: 0.2298336\ttotal: 14.6s\tremaining: 4.37s\n",
      "77:\tlearn: 0.2272404\ttotal: 14.8s\tremaining: 4.17s\n",
      "78:\tlearn: 0.2246280\ttotal: 15s\tremaining: 3.98s\n",
      "79:\tlearn: 0.2203049\ttotal: 15.2s\tremaining: 3.79s\n",
      "80:\tlearn: 0.2163565\ttotal: 15.3s\tremaining: 3.6s\n",
      "81:\tlearn: 0.2138655\ttotal: 15.5s\tremaining: 3.41s\n",
      "82:\tlearn: 0.2103329\ttotal: 15.7s\tremaining: 3.22s\n",
      "83:\tlearn: 0.2073737\ttotal: 15.9s\tremaining: 3.03s\n",
      "84:\tlearn: 0.2037163\ttotal: 16.1s\tremaining: 2.83s\n",
      "85:\tlearn: 0.2009644\ttotal: 16.2s\tremaining: 2.64s\n",
      "86:\tlearn: 0.1977609\ttotal: 16.4s\tremaining: 2.46s\n",
      "87:\tlearn: 0.1947554\ttotal: 16.6s\tremaining: 2.26s\n",
      "88:\tlearn: 0.1918395\ttotal: 16.8s\tremaining: 2.07s\n",
      "89:\tlearn: 0.1891103\ttotal: 17s\tremaining: 1.89s\n",
      "90:\tlearn: 0.1864155\ttotal: 17.2s\tremaining: 1.7s\n",
      "91:\tlearn: 0.1838809\ttotal: 17.4s\tremaining: 1.51s\n",
      "92:\tlearn: 0.1805756\ttotal: 17.6s\tremaining: 1.32s\n",
      "93:\tlearn: 0.1772236\ttotal: 17.7s\tremaining: 1.13s\n",
      "94:\tlearn: 0.1741573\ttotal: 17.9s\tremaining: 943ms\n",
      "95:\tlearn: 0.1717652\ttotal: 18.1s\tremaining: 754ms\n",
      "96:\tlearn: 0.1689657\ttotal: 18.3s\tremaining: 565ms\n",
      "97:\tlearn: 0.1660952\ttotal: 18.5s\tremaining: 377ms\n",
      "98:\tlearn: 0.1633319\ttotal: 18.7s\tremaining: 188ms\n",
      "99:\tlearn: 0.1612931\ttotal: 18.8s\tremaining: 0us\n",
      "Accuracy:  0.6486486486486487\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a Random Forest classifier object\n",
    "cbm = CatBoostClassifier(iterations=100, depth=8, learning_rate=0.1,l2_leaf_reg=1, bagging_temperature=0.5,  loss_function='MultiClass')\n",
    "\n",
    "# Train the classifier\n",
    "cbm.fit(X_train, y_train)\n",
    "\n",
    "#Make predictions\n",
    "y_pred_class = cbm.predict(X_test)\n",
    "# print the model's accuracy\n",
    "print(\"Accuracy: \", metrics.accuracy_score(y_test, y_pred_class))\n",
    "\n",
    "# compute the confusion matrix\n",
    "cnf_matrix = confusion_matrix(y_test,y_pred_class)\n",
    "\n",
    "\n",
    "# plot cnf_matrix\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix, classes=['EN','FR','AR','JP'],normalize=True,\n",
    "                      title='Confusion matrix with all features')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "5e78ac6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "88:\tlearn: 0.1841931\ttotal: 6.46s\tremaining: 8.05s\n",
      "89:\tlearn: 0.1811933\ttotal: 6.53s\tremaining: 7.98s\n",
      "90:\tlearn: 0.1787649\ttotal: 6.59s\tremaining: 7.9s\n",
      "91:\tlearn: 0.1754844\ttotal: 6.67s\tremaining: 7.82s\n",
      "92:\tlearn: 0.1724181\ttotal: 6.74s\tremaining: 7.75s\n",
      "93:\tlearn: 0.1694627\ttotal: 6.81s\tremaining: 7.68s\n",
      "94:\tlearn: 0.1667431\ttotal: 6.88s\tremaining: 7.6s\n",
      "95:\tlearn: 0.1639153\ttotal: 6.95s\tremaining: 7.53s\n",
      "96:\tlearn: 0.1618122\ttotal: 7.03s\tremaining: 7.46s\n",
      "97:\tlearn: 0.1593053\ttotal: 7.09s\tremaining: 7.38s\n",
      "98:\tlearn: 0.1569691\ttotal: 7.17s\tremaining: 7.31s\n",
      "99:\tlearn: 0.1549589\ttotal: 7.24s\tremaining: 7.24s\n",
      "100:\tlearn: 0.1527453\ttotal: 7.31s\tremaining: 7.17s\n",
      "101:\tlearn: 0.1506765\ttotal: 7.39s\tremaining: 7.1s\n",
      "102:\tlearn: 0.1486135\ttotal: 7.46s\tremaining: 7.03s\n",
      "103:\tlearn: 0.1465806\ttotal: 7.54s\tremaining: 6.96s\n",
      "104:\tlearn: 0.1449004\ttotal: 7.61s\tremaining: 6.89s\n",
      "105:\tlearn: 0.1430974\ttotal: 7.69s\tremaining: 6.82s\n",
      "106:\tlearn: 0.1407801\ttotal: 7.77s\tremaining: 6.75s\n",
      "107:\tlearn: 0.1387226\ttotal: 7.85s\tremaining: 6.69s\n",
      "108:\tlearn: 0.1368935\ttotal: 7.92s\tremaining: 6.62s\n",
      "109:\tlearn: 0.1357440\ttotal: 8s\tremaining: 6.54s\n",
      "110:\tlearn: 0.1339446\ttotal: 8.07s\tremaining: 6.47s\n",
      "111:\tlearn: 0.1324643\ttotal: 8.14s\tremaining: 6.4s\n",
      "112:\tlearn: 0.1308692\ttotal: 8.22s\tremaining: 6.33s\n",
      "113:\tlearn: 0.1288319\ttotal: 8.3s\tremaining: 6.26s\n",
      "114:\tlearn: 0.1273820\ttotal: 8.37s\tremaining: 6.19s\n",
      "115:\tlearn: 0.1259128\ttotal: 8.44s\tremaining: 6.11s\n",
      "116:\tlearn: 0.1243610\ttotal: 8.52s\tremaining: 6.04s\n",
      "117:\tlearn: 0.1229692\ttotal: 8.59s\tremaining: 5.97s\n",
      "118:\tlearn: 0.1215688\ttotal: 8.67s\tremaining: 5.9s\n",
      "119:\tlearn: 0.1201816\ttotal: 8.74s\tremaining: 5.83s\n",
      "120:\tlearn: 0.1186048\ttotal: 8.81s\tremaining: 5.75s\n",
      "121:\tlearn: 0.1169138\ttotal: 8.88s\tremaining: 5.68s\n",
      "122:\tlearn: 0.1150597\ttotal: 8.95s\tremaining: 5.61s\n",
      "123:\tlearn: 0.1136486\ttotal: 9.03s\tremaining: 5.54s\n",
      "124:\tlearn: 0.1123959\ttotal: 9.11s\tremaining: 5.46s\n",
      "125:\tlearn: 0.1112580\ttotal: 9.18s\tremaining: 5.39s\n",
      "126:\tlearn: 0.1100784\ttotal: 9.25s\tremaining: 5.32s\n",
      "127:\tlearn: 0.1086782\ttotal: 9.33s\tremaining: 5.25s\n",
      "128:\tlearn: 0.1071717\ttotal: 9.4s\tremaining: 5.17s\n",
      "129:\tlearn: 0.1061685\ttotal: 9.47s\tremaining: 5.1s\n",
      "130:\tlearn: 0.1048824\ttotal: 9.54s\tremaining: 5.02s\n",
      "131:\tlearn: 0.1034867\ttotal: 9.61s\tremaining: 4.95s\n",
      "132:\tlearn: 0.1021283\ttotal: 9.68s\tremaining: 4.88s\n",
      "133:\tlearn: 0.1010601\ttotal: 9.76s\tremaining: 4.81s\n",
      "134:\tlearn: 0.1005240\ttotal: 9.83s\tremaining: 4.73s\n",
      "135:\tlearn: 0.0993733\ttotal: 9.91s\tremaining: 4.66s\n",
      "136:\tlearn: 0.0983180\ttotal: 9.98s\tremaining: 4.59s\n",
      "137:\tlearn: 0.0974084\ttotal: 10.1s\tremaining: 4.51s\n",
      "138:\tlearn: 0.0963227\ttotal: 10.1s\tremaining: 4.44s\n",
      "139:\tlearn: 0.0952090\ttotal: 10.2s\tremaining: 4.37s\n",
      "140:\tlearn: 0.0941109\ttotal: 10.3s\tremaining: 4.29s\n",
      "141:\tlearn: 0.0931479\ttotal: 10.3s\tremaining: 4.22s\n",
      "142:\tlearn: 0.0922982\ttotal: 10.4s\tremaining: 4.15s\n",
      "143:\tlearn: 0.0913712\ttotal: 10.5s\tremaining: 4.07s\n",
      "144:\tlearn: 0.0905402\ttotal: 10.6s\tremaining: 4s\n",
      "145:\tlearn: 0.0896135\ttotal: 10.6s\tremaining: 3.93s\n",
      "146:\tlearn: 0.0887657\ttotal: 10.7s\tremaining: 3.86s\n",
      "147:\tlearn: 0.0881059\ttotal: 10.8s\tremaining: 3.79s\n",
      "148:\tlearn: 0.0873216\ttotal: 10.9s\tremaining: 3.71s\n",
      "149:\tlearn: 0.0863871\ttotal: 10.9s\tremaining: 3.64s\n",
      "150:\tlearn: 0.0858514\ttotal: 11s\tremaining: 3.57s\n",
      "151:\tlearn: 0.0851153\ttotal: 11.1s\tremaining: 3.49s\n",
      "152:\tlearn: 0.0842816\ttotal: 11.1s\tremaining: 3.42s\n",
      "153:\tlearn: 0.0833757\ttotal: 11.2s\tremaining: 3.35s\n",
      "154:\tlearn: 0.0826203\ttotal: 11.3s\tremaining: 3.27s\n",
      "155:\tlearn: 0.0818366\ttotal: 11.3s\tremaining: 3.2s\n",
      "156:\tlearn: 0.0812001\ttotal: 11.4s\tremaining: 3.13s\n",
      "157:\tlearn: 0.0805330\ttotal: 11.5s\tremaining: 3.06s\n",
      "158:\tlearn: 0.0799748\ttotal: 11.6s\tremaining: 2.98s\n",
      "159:\tlearn: 0.0793587\ttotal: 11.6s\tremaining: 2.91s\n",
      "160:\tlearn: 0.0787153\ttotal: 11.7s\tremaining: 2.83s\n",
      "161:\tlearn: 0.0779915\ttotal: 11.8s\tremaining: 2.76s\n",
      "162:\tlearn: 0.0773916\ttotal: 11.8s\tremaining: 2.69s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "163:\tlearn: 0.0767247\ttotal: 11.9s\tremaining: 2.61s\n",
      "164:\tlearn: 0.0760397\ttotal: 12s\tremaining: 2.54s\n",
      "165:\tlearn: 0.0753097\ttotal: 12s\tremaining: 2.46s\n",
      "166:\tlearn: 0.0746164\ttotal: 12.1s\tremaining: 2.39s\n",
      "167:\tlearn: 0.0739606\ttotal: 12.2s\tremaining: 2.32s\n",
      "168:\tlearn: 0.0733997\ttotal: 12.2s\tremaining: 2.25s\n",
      "169:\tlearn: 0.0728663\ttotal: 12.3s\tremaining: 2.17s\n",
      "170:\tlearn: 0.0722760\ttotal: 12.4s\tremaining: 2.1s\n",
      "171:\tlearn: 0.0716628\ttotal: 12.5s\tremaining: 2.03s\n",
      "172:\tlearn: 0.0711108\ttotal: 12.5s\tremaining: 1.95s\n",
      "173:\tlearn: 0.0704125\ttotal: 12.6s\tremaining: 1.88s\n",
      "174:\tlearn: 0.0698397\ttotal: 12.7s\tremaining: 1.81s\n",
      "175:\tlearn: 0.0692386\ttotal: 12.7s\tremaining: 1.73s\n",
      "176:\tlearn: 0.0687067\ttotal: 12.8s\tremaining: 1.66s\n",
      "177:\tlearn: 0.0683188\ttotal: 12.9s\tremaining: 1.59s\n",
      "178:\tlearn: 0.0676989\ttotal: 12.9s\tremaining: 1.52s\n",
      "179:\tlearn: 0.0672572\ttotal: 13s\tremaining: 1.44s\n",
      "180:\tlearn: 0.0669483\ttotal: 13.1s\tremaining: 1.37s\n",
      "181:\tlearn: 0.0664213\ttotal: 13.1s\tremaining: 1.3s\n",
      "182:\tlearn: 0.0659270\ttotal: 13.2s\tremaining: 1.23s\n",
      "183:\tlearn: 0.0653927\ttotal: 13.3s\tremaining: 1.15s\n",
      "184:\tlearn: 0.0647872\ttotal: 13.3s\tremaining: 1.08s\n",
      "185:\tlearn: 0.0644696\ttotal: 13.4s\tremaining: 1.01s\n",
      "186:\tlearn: 0.0640479\ttotal: 13.5s\tremaining: 936ms\n",
      "187:\tlearn: 0.0636922\ttotal: 13.5s\tremaining: 864ms\n",
      "188:\tlearn: 0.0632031\ttotal: 13.6s\tremaining: 792ms\n",
      "189:\tlearn: 0.0628395\ttotal: 13.7s\tremaining: 720ms\n",
      "190:\tlearn: 0.0624017\ttotal: 13.7s\tremaining: 648ms\n",
      "191:\tlearn: 0.0619620\ttotal: 13.8s\tremaining: 576ms\n",
      "192:\tlearn: 0.0615701\ttotal: 13.9s\tremaining: 504ms\n",
      "193:\tlearn: 0.0611235\ttotal: 13.9s\tremaining: 431ms\n",
      "194:\tlearn: 0.0606502\ttotal: 14s\tremaining: 359ms\n",
      "195:\tlearn: 0.0602826\ttotal: 14.1s\tremaining: 288ms\n",
      "196:\tlearn: 0.0598096\ttotal: 14.2s\tremaining: 216ms\n",
      "197:\tlearn: 0.0593596\ttotal: 14.2s\tremaining: 144ms\n",
      "198:\tlearn: 0.0590589\ttotal: 14.3s\tremaining: 71.9ms\n",
      "199:\tlearn: 0.0587444\ttotal: 14.4s\tremaining: 0us\n",
      "Accuracy:  0.6418918918918919\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a Random Forest classifier object\n",
    "cbm = CatBoostClassifier(iterations=200, depth=8, learning_rate=0.1,l2_leaf_reg=1, bagging_temperature=0.5,  loss_function='MultiClass')\n",
    "\n",
    "# Train the classifier\n",
    "cbm.fit(X_train, y_train)\n",
    "\n",
    "#Make predictions\n",
    "y_pred_class = cbm.predict(X_test)\n",
    "# print the model's accuracy\n",
    "print(\"Accuracy: \", metrics.accuracy_score(y_test, y_pred_class))\n",
    "\n",
    "# compute the confusion matrix\n",
    "cnf_matrix = confusion_matrix(y_test,y_pred_class)\n",
    "\n",
    "\n",
    "# plot cnf_matrix\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix, classes=['EN','FR','AR','JP'],normalize=True,\n",
    "                      title='Confusion matrix with all features')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "f482aa01",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "77:\tlearn: 0.0464435\ttotal: 14.8s\tremaining: 23.2s\n",
      "78:\tlearn: 0.0460872\ttotal: 15s\tremaining: 23s\n",
      "79:\tlearn: 0.0454201\ttotal: 15.2s\tremaining: 22.8s\n",
      "80:\tlearn: 0.0446396\ttotal: 15.4s\tremaining: 22.6s\n",
      "81:\tlearn: 0.0440891\ttotal: 15.6s\tremaining: 22.4s\n",
      "82:\tlearn: 0.0432606\ttotal: 15.8s\tremaining: 22.3s\n",
      "83:\tlearn: 0.0423714\ttotal: 16s\tremaining: 22.1s\n",
      "84:\tlearn: 0.0416165\ttotal: 16.2s\tremaining: 21.9s\n",
      "85:\tlearn: 0.0410286\ttotal: 16.3s\tremaining: 21.7s\n",
      "86:\tlearn: 0.0404095\ttotal: 16.5s\tremaining: 21.5s\n",
      "87:\tlearn: 0.0399783\ttotal: 16.7s\tremaining: 21.3s\n",
      "88:\tlearn: 0.0394829\ttotal: 16.9s\tremaining: 21.1s\n",
      "89:\tlearn: 0.0389648\ttotal: 17.1s\tremaining: 20.9s\n",
      "90:\tlearn: 0.0385245\ttotal: 17.3s\tremaining: 20.7s\n",
      "91:\tlearn: 0.0380350\ttotal: 17.4s\tremaining: 20.5s\n",
      "92:\tlearn: 0.0376680\ttotal: 17.6s\tremaining: 20.3s\n",
      "93:\tlearn: 0.0371182\ttotal: 17.8s\tremaining: 20.1s\n",
      "94:\tlearn: 0.0366695\ttotal: 18s\tremaining: 19.9s\n",
      "95:\tlearn: 0.0361517\ttotal: 18.2s\tremaining: 19.7s\n",
      "96:\tlearn: 0.0357595\ttotal: 18.4s\tremaining: 19.6s\n",
      "97:\tlearn: 0.0352983\ttotal: 18.6s\tremaining: 19.4s\n",
      "98:\tlearn: 0.0349246\ttotal: 18.8s\tremaining: 19.2s\n",
      "99:\tlearn: 0.0344717\ttotal: 19s\tremaining: 19s\n",
      "100:\tlearn: 0.0340435\ttotal: 19.2s\tremaining: 18.8s\n",
      "101:\tlearn: 0.0335691\ttotal: 19.4s\tremaining: 18.6s\n",
      "102:\tlearn: 0.0332311\ttotal: 19.6s\tremaining: 18.4s\n",
      "103:\tlearn: 0.0329324\ttotal: 19.8s\tremaining: 18.3s\n",
      "104:\tlearn: 0.0326190\ttotal: 20s\tremaining: 18.1s\n",
      "105:\tlearn: 0.0324253\ttotal: 20.2s\tremaining: 17.9s\n",
      "106:\tlearn: 0.0320085\ttotal: 20.3s\tremaining: 17.7s\n",
      "107:\tlearn: 0.0318309\ttotal: 20.5s\tremaining: 17.5s\n",
      "108:\tlearn: 0.0314808\ttotal: 20.7s\tremaining: 17.3s\n",
      "109:\tlearn: 0.0311893\ttotal: 20.9s\tremaining: 17.1s\n",
      "110:\tlearn: 0.0309371\ttotal: 21.1s\tremaining: 16.9s\n",
      "111:\tlearn: 0.0305980\ttotal: 21.3s\tremaining: 16.7s\n",
      "112:\tlearn: 0.0303796\ttotal: 21.5s\tremaining: 16.5s\n",
      "113:\tlearn: 0.0300935\ttotal: 21.6s\tremaining: 16.3s\n",
      "114:\tlearn: 0.0298510\ttotal: 21.8s\tremaining: 16.1s\n",
      "115:\tlearn: 0.0296119\ttotal: 22s\tremaining: 15.9s\n",
      "116:\tlearn: 0.0291943\ttotal: 22.2s\tremaining: 15.8s\n",
      "117:\tlearn: 0.0289941\ttotal: 22.4s\tremaining: 15.6s\n",
      "118:\tlearn: 0.0286763\ttotal: 22.6s\tremaining: 15.4s\n",
      "119:\tlearn: 0.0283651\ttotal: 22.8s\tremaining: 15.2s\n",
      "120:\tlearn: 0.0281832\ttotal: 23s\tremaining: 15s\n",
      "121:\tlearn: 0.0279420\ttotal: 23.2s\tremaining: 14.8s\n",
      "122:\tlearn: 0.0276641\ttotal: 23.3s\tremaining: 14.6s\n",
      "123:\tlearn: 0.0274116\ttotal: 23.5s\tremaining: 14.4s\n",
      "124:\tlearn: 0.0271848\ttotal: 23.7s\tremaining: 14.2s\n",
      "125:\tlearn: 0.0271593\ttotal: 23.9s\tremaining: 14s\n",
      "126:\tlearn: 0.0268372\ttotal: 24.1s\tremaining: 13.8s\n",
      "127:\tlearn: 0.0266068\ttotal: 24.3s\tremaining: 13.7s\n",
      "128:\tlearn: 0.0265406\ttotal: 24.5s\tremaining: 13.5s\n",
      "129:\tlearn: 0.0263842\ttotal: 24.6s\tremaining: 13.3s\n",
      "130:\tlearn: 0.0261865\ttotal: 24.8s\tremaining: 13.1s\n",
      "131:\tlearn: 0.0259751\ttotal: 25s\tremaining: 12.9s\n",
      "132:\tlearn: 0.0258619\ttotal: 25.2s\tremaining: 12.7s\n",
      "133:\tlearn: 0.0256082\ttotal: 25.4s\tremaining: 12.5s\n",
      "134:\tlearn: 0.0254041\ttotal: 25.6s\tremaining: 12.3s\n",
      "135:\tlearn: 0.0252773\ttotal: 25.7s\tremaining: 12.1s\n",
      "136:\tlearn: 0.0251100\ttotal: 25.9s\tremaining: 11.9s\n",
      "137:\tlearn: 0.0249838\ttotal: 26.1s\tremaining: 11.7s\n",
      "138:\tlearn: 0.0247768\ttotal: 26.3s\tremaining: 11.5s\n",
      "139:\tlearn: 0.0246407\ttotal: 26.5s\tremaining: 11.3s\n",
      "140:\tlearn: 0.0244374\ttotal: 26.6s\tremaining: 11.1s\n",
      "141:\tlearn: 0.0242512\ttotal: 26.8s\tremaining: 11s\n",
      "142:\tlearn: 0.0240011\ttotal: 27s\tremaining: 10.8s\n",
      "143:\tlearn: 0.0238181\ttotal: 27.2s\tremaining: 10.6s\n",
      "144:\tlearn: 0.0237185\ttotal: 27.3s\tremaining: 10.4s\n",
      "145:\tlearn: 0.0235472\ttotal: 27.5s\tremaining: 10.2s\n",
      "146:\tlearn: 0.0233632\ttotal: 27.7s\tremaining: 9.99s\n",
      "147:\tlearn: 0.0232073\ttotal: 27.9s\tremaining: 9.8s\n",
      "148:\tlearn: 0.0230356\ttotal: 28.1s\tremaining: 9.61s\n",
      "149:\tlearn: 0.0228424\ttotal: 28.3s\tremaining: 9.42s\n",
      "150:\tlearn: 0.0226935\ttotal: 28.4s\tremaining: 9.23s\n",
      "151:\tlearn: 0.0226061\ttotal: 28.6s\tremaining: 9.04s\n",
      "152:\tlearn: 0.0224691\ttotal: 28.8s\tremaining: 8.85s\n",
      "153:\tlearn: 0.0223592\ttotal: 29s\tremaining: 8.65s\n",
      "154:\tlearn: 0.0222098\ttotal: 29.2s\tremaining: 8.47s\n",
      "155:\tlearn: 0.0220034\ttotal: 29.4s\tremaining: 8.28s\n",
      "156:\tlearn: 0.0218599\ttotal: 29.5s\tremaining: 8.09s\n",
      "157:\tlearn: 0.0217143\ttotal: 29.7s\tremaining: 7.9s\n",
      "158:\tlearn: 0.0215297\ttotal: 29.9s\tremaining: 7.71s\n",
      "159:\tlearn: 0.0214566\ttotal: 30.1s\tremaining: 7.52s\n",
      "160:\tlearn: 0.0213392\ttotal: 30.2s\tremaining: 7.33s\n",
      "161:\tlearn: 0.0212052\ttotal: 30.4s\tremaining: 7.14s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "162:\tlearn: 0.0211191\ttotal: 30.6s\tremaining: 6.96s\n",
      "163:\tlearn: 0.0209743\ttotal: 30.8s\tremaining: 6.76s\n",
      "164:\tlearn: 0.0208413\ttotal: 31s\tremaining: 6.58s\n",
      "165:\tlearn: 0.0207223\ttotal: 31.2s\tremaining: 6.39s\n",
      "166:\tlearn: 0.0206066\ttotal: 31.4s\tremaining: 6.2s\n",
      "167:\tlearn: 0.0205196\ttotal: 31.6s\tremaining: 6.01s\n",
      "168:\tlearn: 0.0203614\ttotal: 31.7s\tremaining: 5.82s\n",
      "169:\tlearn: 0.0202815\ttotal: 31.9s\tremaining: 5.63s\n",
      "170:\tlearn: 0.0201530\ttotal: 32.1s\tremaining: 5.45s\n",
      "171:\tlearn: 0.0200433\ttotal: 32.3s\tremaining: 5.26s\n",
      "172:\tlearn: 0.0199569\ttotal: 32.5s\tremaining: 5.07s\n",
      "173:\tlearn: 0.0198772\ttotal: 32.7s\tremaining: 4.88s\n",
      "174:\tlearn: 0.0197699\ttotal: 32.9s\tremaining: 4.7s\n",
      "175:\tlearn: 0.0196809\ttotal: 33.1s\tremaining: 4.51s\n",
      "176:\tlearn: 0.0195500\ttotal: 33.2s\tremaining: 4.32s\n",
      "177:\tlearn: 0.0194515\ttotal: 33.5s\tremaining: 4.13s\n",
      "178:\tlearn: 0.0193231\ttotal: 33.7s\tremaining: 3.95s\n",
      "179:\tlearn: 0.0192723\ttotal: 33.9s\tremaining: 3.76s\n",
      "180:\tlearn: 0.0191631\ttotal: 34.1s\tremaining: 3.58s\n",
      "181:\tlearn: 0.0191254\ttotal: 34.3s\tremaining: 3.39s\n",
      "182:\tlearn: 0.0190129\ttotal: 34.5s\tremaining: 3.2s\n",
      "183:\tlearn: 0.0189341\ttotal: 34.7s\tremaining: 3.02s\n",
      "184:\tlearn: 0.0188385\ttotal: 34.9s\tremaining: 2.83s\n",
      "185:\tlearn: 0.0187526\ttotal: 35.1s\tremaining: 2.64s\n",
      "186:\tlearn: 0.0186593\ttotal: 35.3s\tremaining: 2.45s\n",
      "187:\tlearn: 0.0185672\ttotal: 35.5s\tremaining: 2.26s\n",
      "188:\tlearn: 0.0184509\ttotal: 35.6s\tremaining: 2.07s\n",
      "189:\tlearn: 0.0183713\ttotal: 35.8s\tremaining: 1.89s\n",
      "190:\tlearn: 0.0182746\ttotal: 36s\tremaining: 1.7s\n",
      "191:\tlearn: 0.0182013\ttotal: 36.2s\tremaining: 1.51s\n",
      "192:\tlearn: 0.0181272\ttotal: 36.4s\tremaining: 1.32s\n",
      "193:\tlearn: 0.0180505\ttotal: 36.6s\tremaining: 1.13s\n",
      "194:\tlearn: 0.0179825\ttotal: 36.8s\tremaining: 943ms\n",
      "195:\tlearn: 0.0179296\ttotal: 37s\tremaining: 754ms\n",
      "196:\tlearn: 0.0178753\ttotal: 37.1s\tremaining: 566ms\n",
      "197:\tlearn: 0.0178067\ttotal: 37.3s\tremaining: 377ms\n",
      "198:\tlearn: 0.0177318\ttotal: 37.5s\tremaining: 189ms\n",
      "199:\tlearn: 0.0176695\ttotal: 37.7s\tremaining: 0us\n",
      "Accuracy:  0.6891891891891891\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a Random Forest classifier object\n",
    "cbm = CatBoostClassifier(iterations=200, depth=8, learning_rate=0.3,l2_leaf_reg=1, bagging_temperature=0.5,  loss_function='MultiClass')\n",
    "\n",
    "# Train the classifier\n",
    "cbm.fit(X_train, y_train)\n",
    "\n",
    "#Make predictions\n",
    "y_pred_class = cbm.predict(X_test)\n",
    "# print the model's accuracy\n",
    "print(\"Accuracy: \", metrics.accuracy_score(y_test, y_pred_class))\n",
    "\n",
    "# compute the confusion matrix\n",
    "cnf_matrix = confusion_matrix(y_test,y_pred_class)\n",
    "\n",
    "\n",
    "# plot cnf_matrix\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix, classes=['EN','FR','AR','JP'],normalize=True,\n",
    "                      title='Confusion matrix with all features')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "2c6c371a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "72:\tlearn: 0.0536605\ttotal: 5.14s\tremaining: 8.94s\n",
      "73:\tlearn: 0.0527727\ttotal: 5.21s\tremaining: 8.87s\n",
      "74:\tlearn: 0.0517848\ttotal: 5.28s\tremaining: 8.8s\n",
      "75:\tlearn: 0.0510843\ttotal: 5.35s\tremaining: 8.74s\n",
      "76:\tlearn: 0.0503764\ttotal: 5.42s\tremaining: 8.66s\n",
      "77:\tlearn: 0.0496461\ttotal: 5.49s\tremaining: 8.59s\n",
      "78:\tlearn: 0.0489630\ttotal: 5.55s\tremaining: 8.51s\n",
      "79:\tlearn: 0.0481930\ttotal: 5.63s\tremaining: 8.44s\n",
      "80:\tlearn: 0.0475598\ttotal: 5.69s\tremaining: 8.36s\n",
      "81:\tlearn: 0.0468689\ttotal: 5.76s\tremaining: 8.29s\n",
      "82:\tlearn: 0.0462278\ttotal: 5.83s\tremaining: 8.22s\n",
      "83:\tlearn: 0.0454557\ttotal: 5.9s\tremaining: 8.15s\n",
      "84:\tlearn: 0.0447531\ttotal: 5.98s\tremaining: 8.09s\n",
      "85:\tlearn: 0.0441350\ttotal: 6.05s\tremaining: 8.02s\n",
      "86:\tlearn: 0.0438224\ttotal: 6.12s\tremaining: 7.95s\n",
      "87:\tlearn: 0.0433452\ttotal: 6.18s\tremaining: 7.87s\n",
      "88:\tlearn: 0.0427305\ttotal: 6.26s\tremaining: 7.8s\n",
      "89:\tlearn: 0.0422912\ttotal: 6.33s\tremaining: 7.73s\n",
      "90:\tlearn: 0.0418542\ttotal: 6.39s\tremaining: 7.66s\n",
      "91:\tlearn: 0.0412669\ttotal: 6.47s\tremaining: 7.59s\n",
      "92:\tlearn: 0.0408406\ttotal: 6.53s\tremaining: 7.52s\n",
      "93:\tlearn: 0.0403799\ttotal: 6.6s\tremaining: 7.44s\n",
      "94:\tlearn: 0.0402745\ttotal: 6.67s\tremaining: 7.37s\n",
      "95:\tlearn: 0.0397319\ttotal: 6.74s\tremaining: 7.31s\n",
      "96:\tlearn: 0.0391650\ttotal: 6.82s\tremaining: 7.24s\n",
      "97:\tlearn: 0.0387169\ttotal: 6.89s\tremaining: 7.17s\n",
      "98:\tlearn: 0.0386903\ttotal: 6.97s\tremaining: 7.11s\n",
      "99:\tlearn: 0.0382736\ttotal: 7.03s\tremaining: 7.03s\n",
      "100:\tlearn: 0.0380869\ttotal: 7.11s\tremaining: 6.97s\n",
      "101:\tlearn: 0.0377481\ttotal: 7.18s\tremaining: 6.9s\n",
      "102:\tlearn: 0.0373802\ttotal: 7.25s\tremaining: 6.83s\n",
      "103:\tlearn: 0.0370002\ttotal: 7.32s\tremaining: 6.75s\n",
      "104:\tlearn: 0.0365215\ttotal: 7.39s\tremaining: 6.68s\n",
      "105:\tlearn: 0.0360588\ttotal: 7.46s\tremaining: 6.62s\n",
      "106:\tlearn: 0.0356712\ttotal: 7.53s\tremaining: 6.55s\n",
      "107:\tlearn: 0.0353048\ttotal: 7.6s\tremaining: 6.47s\n",
      "108:\tlearn: 0.0349633\ttotal: 7.67s\tremaining: 6.4s\n",
      "109:\tlearn: 0.0348225\ttotal: 7.73s\tremaining: 6.33s\n",
      "110:\tlearn: 0.0344514\ttotal: 7.8s\tremaining: 6.25s\n",
      "111:\tlearn: 0.0342597\ttotal: 7.87s\tremaining: 6.18s\n",
      "112:\tlearn: 0.0339209\ttotal: 7.93s\tremaining: 6.11s\n",
      "113:\tlearn: 0.0335353\ttotal: 8.02s\tremaining: 6.05s\n",
      "114:\tlearn: 0.0331341\ttotal: 8.08s\tremaining: 5.97s\n",
      "115:\tlearn: 0.0328102\ttotal: 8.15s\tremaining: 5.91s\n",
      "116:\tlearn: 0.0324313\ttotal: 8.23s\tremaining: 5.84s\n",
      "117:\tlearn: 0.0321040\ttotal: 8.3s\tremaining: 5.76s\n",
      "118:\tlearn: 0.0318198\ttotal: 8.37s\tremaining: 5.7s\n",
      "119:\tlearn: 0.0315979\ttotal: 8.44s\tremaining: 5.63s\n",
      "120:\tlearn: 0.0314280\ttotal: 8.51s\tremaining: 5.56s\n",
      "121:\tlearn: 0.0311382\ttotal: 8.58s\tremaining: 5.49s\n",
      "122:\tlearn: 0.0309477\ttotal: 8.66s\tremaining: 5.42s\n",
      "123:\tlearn: 0.0307770\ttotal: 8.72s\tremaining: 5.35s\n",
      "124:\tlearn: 0.0305384\ttotal: 8.79s\tremaining: 5.28s\n",
      "125:\tlearn: 0.0302800\ttotal: 8.87s\tremaining: 5.21s\n",
      "126:\tlearn: 0.0300700\ttotal: 8.94s\tremaining: 5.14s\n",
      "127:\tlearn: 0.0298724\ttotal: 9.01s\tremaining: 5.07s\n",
      "128:\tlearn: 0.0296531\ttotal: 9.08s\tremaining: 5s\n",
      "129:\tlearn: 0.0294931\ttotal: 9.15s\tremaining: 4.93s\n",
      "130:\tlearn: 0.0291892\ttotal: 9.22s\tremaining: 4.86s\n",
      "131:\tlearn: 0.0289387\ttotal: 9.29s\tremaining: 4.79s\n",
      "132:\tlearn: 0.0286632\ttotal: 9.37s\tremaining: 4.72s\n",
      "133:\tlearn: 0.0286101\ttotal: 9.44s\tremaining: 4.65s\n",
      "134:\tlearn: 0.0284460\ttotal: 9.51s\tremaining: 4.58s\n",
      "135:\tlearn: 0.0282695\ttotal: 9.58s\tremaining: 4.51s\n",
      "136:\tlearn: 0.0281370\ttotal: 9.65s\tremaining: 4.44s\n",
      "137:\tlearn: 0.0279675\ttotal: 9.73s\tremaining: 4.37s\n",
      "138:\tlearn: 0.0277620\ttotal: 9.8s\tremaining: 4.3s\n",
      "139:\tlearn: 0.0276087\ttotal: 9.88s\tremaining: 4.24s\n",
      "140:\tlearn: 0.0273797\ttotal: 9.96s\tremaining: 4.17s\n",
      "141:\tlearn: 0.0272175\ttotal: 10s\tremaining: 4.1s\n",
      "142:\tlearn: 0.0269782\ttotal: 10.1s\tremaining: 4.02s\n",
      "143:\tlearn: 0.0268227\ttotal: 10.2s\tremaining: 3.95s\n",
      "144:\tlearn: 0.0266285\ttotal: 10.2s\tremaining: 3.88s\n",
      "145:\tlearn: 0.0264108\ttotal: 10.3s\tremaining: 3.81s\n",
      "146:\tlearn: 0.0262887\ttotal: 10.4s\tremaining: 3.75s\n",
      "147:\tlearn: 0.0260994\ttotal: 10.5s\tremaining: 3.68s\n",
      "148:\tlearn: 0.0259512\ttotal: 10.5s\tremaining: 3.61s\n",
      "149:\tlearn: 0.0257642\ttotal: 10.6s\tremaining: 3.54s\n",
      "150:\tlearn: 0.0256102\ttotal: 10.7s\tremaining: 3.47s\n",
      "151:\tlearn: 0.0255103\ttotal: 10.8s\tremaining: 3.4s\n",
      "152:\tlearn: 0.0254201\ttotal: 10.8s\tremaining: 3.33s\n",
      "153:\tlearn: 0.0253244\ttotal: 10.9s\tremaining: 3.26s\n",
      "154:\tlearn: 0.0251959\ttotal: 11s\tremaining: 3.19s\n",
      "155:\tlearn: 0.0250759\ttotal: 11.1s\tremaining: 3.12s\n",
      "156:\tlearn: 0.0249044\ttotal: 11.1s\tremaining: 3.05s\n",
      "157:\tlearn: 0.0248048\ttotal: 11.2s\tremaining: 2.98s\n",
      "158:\tlearn: 0.0246801\ttotal: 11.3s\tremaining: 2.91s\n",
      "159:\tlearn: 0.0245209\ttotal: 11.4s\tremaining: 2.84s\n",
      "160:\tlearn: 0.0244049\ttotal: 11.4s\tremaining: 2.77s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "161:\tlearn: 0.0242948\ttotal: 11.5s\tremaining: 2.7s\n",
      "162:\tlearn: 0.0241804\ttotal: 11.6s\tremaining: 2.63s\n",
      "163:\tlearn: 0.0240549\ttotal: 11.7s\tremaining: 2.56s\n",
      "164:\tlearn: 0.0239685\ttotal: 11.8s\tremaining: 2.49s\n",
      "165:\tlearn: 0.0238364\ttotal: 11.8s\tremaining: 2.42s\n",
      "166:\tlearn: 0.0237446\ttotal: 11.9s\tremaining: 2.35s\n",
      "167:\tlearn: 0.0236346\ttotal: 12s\tremaining: 2.28s\n",
      "168:\tlearn: 0.0235467\ttotal: 12.1s\tremaining: 2.21s\n",
      "169:\tlearn: 0.0234470\ttotal: 12.1s\tremaining: 2.14s\n",
      "170:\tlearn: 0.0233301\ttotal: 12.2s\tremaining: 2.07s\n",
      "171:\tlearn: 0.0231941\ttotal: 12.3s\tremaining: 2s\n",
      "172:\tlearn: 0.0231197\ttotal: 12.3s\tremaining: 1.92s\n",
      "173:\tlearn: 0.0230478\ttotal: 12.4s\tremaining: 1.85s\n",
      "174:\tlearn: 0.0229527\ttotal: 12.5s\tremaining: 1.78s\n",
      "175:\tlearn: 0.0228471\ttotal: 12.5s\tremaining: 1.71s\n",
      "176:\tlearn: 0.0227571\ttotal: 12.6s\tremaining: 1.64s\n",
      "177:\tlearn: 0.0226467\ttotal: 12.7s\tremaining: 1.57s\n",
      "178:\tlearn: 0.0225569\ttotal: 12.7s\tremaining: 1.5s\n",
      "179:\tlearn: 0.0224442\ttotal: 12.8s\tremaining: 1.42s\n",
      "180:\tlearn: 0.0224249\ttotal: 12.8s\tremaining: 1.35s\n",
      "181:\tlearn: 0.0223199\ttotal: 12.9s\tremaining: 1.27s\n",
      "182:\tlearn: 0.0222634\ttotal: 13s\tremaining: 1.2s\n",
      "183:\tlearn: 0.0221542\ttotal: 13s\tremaining: 1.13s\n",
      "184:\tlearn: 0.0220547\ttotal: 13.1s\tremaining: 1.06s\n",
      "185:\tlearn: 0.0220483\ttotal: 13.2s\tremaining: 991ms\n",
      "186:\tlearn: 0.0219724\ttotal: 13.2s\tremaining: 920ms\n",
      "187:\tlearn: 0.0218974\ttotal: 13.3s\tremaining: 849ms\n",
      "188:\tlearn: 0.0218128\ttotal: 13.4s\tremaining: 779ms\n",
      "189:\tlearn: 0.0217269\ttotal: 13.5s\tremaining: 708ms\n",
      "190:\tlearn: 0.0216544\ttotal: 13.5s\tremaining: 637ms\n",
      "191:\tlearn: 0.0215825\ttotal: 13.6s\tremaining: 566ms\n",
      "192:\tlearn: 0.0214833\ttotal: 13.7s\tremaining: 496ms\n",
      "193:\tlearn: 0.0213989\ttotal: 13.7s\tremaining: 425ms\n",
      "194:\tlearn: 0.0213050\ttotal: 13.8s\tremaining: 354ms\n",
      "195:\tlearn: 0.0212340\ttotal: 13.9s\tremaining: 283ms\n",
      "196:\tlearn: 0.0211540\ttotal: 13.9s\tremaining: 212ms\n",
      "197:\tlearn: 0.0210782\ttotal: 14s\tremaining: 142ms\n",
      "198:\tlearn: 0.0210004\ttotal: 14.1s\tremaining: 70.8ms\n",
      "199:\tlearn: 0.0209322\ttotal: 14.2s\tremaining: 0us\n",
      "Accuracy:  0.6554054054054054\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a Random Forest classifier object\n",
    "cbm = CatBoostClassifier(iterations=200, depth=8, learning_rate=0.3,l2_leaf_reg=1, bagging_temperature=0.5,  loss_function='MultiClass')\n",
    "\n",
    "# Train the classifier\n",
    "cbm.fit(X_train, y_train)\n",
    "\n",
    "#Make predictions\n",
    "y_pred_class = cbm.predict(X_test)\n",
    "# print the model's accuracy\n",
    "print(\"Accuracy: \", metrics.accuracy_score(y_test, y_pred_class))\n",
    "\n",
    "# compute the confusion matrix\n",
    "cnf_matrix = confusion_matrix(y_test,y_pred_class)\n",
    "\n",
    "\n",
    "# plot cnf_matrix\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix, classes=['EN','FR','AR','JP'],normalize=True,\n",
    "                      title='Confusion matrix with all features')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "48fa8d87",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "92c22d60",
   "metadata": {},
   "source": [
    "# Output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "a3986dd1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted class: \"JP\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"AR\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"AR\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted class: \"EN\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"FR\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"EN\"\n",
      "Predicted class: \"AR\"\n",
      "Predicted class: \"JP\"\n",
      "Predicted class: \"FR\"\n"
     ]
    }
   ],
   "source": [
    "#set data_dir to the directory of your data files\n",
    "data_dir= r\"C:\\Users\\Natha\\emlyon-dsaib\\Python tutorship\\Projet\\language-identification-lid2\\Evaluation_Set\\evaluation_set\"\n",
    "\n",
    "# Read file info file to get the list of audio files and their labels\n",
    "file_list=[]\n",
    "label_list=[]\n",
    "with open(data_dir+\"\\Info.txt\", 'r') as file:\n",
    "    reader = csv.reader(file)\n",
    "    for row in reader:\n",
    "        # The first column contains the file name\n",
    "        file_list.append(row[0])\n",
    "\n",
    "lang_dic={'EN':0,'FR':1,'AR':2,'JP':3}\n",
    "class2lang_dic={0:\"EN\",1:\"FR\",2:\"AR\",3:\"JP\"}\n",
    "\n",
    "for test_sample in file_list:\n",
    "    test_sample_feature=feature_extractor_1 (data_dir+'\\\\' + test_sample)\n",
    "    predicted=class2lang_dic[clf.predict([test_sample_feature])[0]]\n",
    "    print(f'Predicted class: \"{predicted}\"')\n",
    "    # save the predicted output in Output_evaluation.txt\n",
    "    with open(data_dir+\"[YourNAME_YourModelName_Version].csv\",'a+') as file:\n",
    "        file.write(f\"{test_sample},{predicted}\\n\")"
   ]
  }
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