Glass Type Classification with Deep Learning.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "b2c39eb4-76b5-df31-f4a7-3ad46dbf156e",
    "_uuid": "a60826dab6412af3e5e224a8f380d5836b502333",
    "id": "k6t-3PvXL9TV"
   },
   "source": [
    "# Glass Type Classification with Deep Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "a1da2060-fd99-7cf6-ade3-964c4cd303df",
    "_uuid": "6e6735dedd3d963094f14580ed8fa2d190e3814f",
    "id": "TSqomqIwL9Tn"
   },
   "source": [
    "### Loading and exploring the shape of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, f1_score, recall_score, precision_score\n",
    "import torch\n",
    "from torch.utils.data import DataLoader, TensorDataset\n",
    "from torch import nn, optim\n",
    "from torch.autograd import Variable\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_cell_guid": "dd4f341c-f072-efea-f0f5-891184db0a07",
    "_uuid": "8904387c44fbfdce5e647d69351069dc3d2a5de7",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "eQqfHv08L9Tn",
    "outputId": "6b8e03ee-4b47-4e88-b72a-8f70784218fb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['RI', 'Na', 'Mg', 'Al', 'Si', 'K', 'Ca', 'Ba', 'Fe']\n",
      "(214, 10)\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('glass.csv')\n",
    "features = df.columns[:-1].tolist()\n",
    "print(features)\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "41fc1169-af80-d9c4-fc46-2ecb533475ac",
    "_uuid": "bb629a8d8dd86cf398476544613fb653077092b2",
    "id": "GDivaRVaL9To"
   },
   "source": [
    "The dataset consists of 214 observations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_cell_guid": "0aa76eab-6060-b7f4-338e-23a5c543a68c",
    "_uuid": "8cd639b544a3ef8ba165385acb93513b606151ed",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 520
    },
    "id": "ujzpO14YL9To",
    "outputId": "e10da52d-9980-4994-eb4d-9b895aaf28cf"
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RI</th>\n",
       "      <th>Na</th>\n",
       "      <th>Mg</th>\n",
       "      <th>Al</th>\n",
       "      <th>Si</th>\n",
       "      <th>K</th>\n",
       "      <th>Ca</th>\n",
       "      <th>Ba</th>\n",
       "      <th>Fe</th>\n",
       "      <th>Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.52101</td>\n",
       "      <td>13.64</td>\n",
       "      <td>4.49</td>\n",
       "      <td>1.10</td>\n",
       "      <td>71.78</td>\n",
       "      <td>0.06</td>\n",
       "      <td>8.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.51761</td>\n",
       "      <td>13.89</td>\n",
       "      <td>3.60</td>\n",
       "      <td>1.36</td>\n",
       "      <td>72.73</td>\n",
       "      <td>0.48</td>\n",
       "      <td>7.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.51618</td>\n",
       "      <td>13.53</td>\n",
       "      <td>3.55</td>\n",
       "      <td>1.54</td>\n",
       "      <td>72.99</td>\n",
       "      <td>0.39</td>\n",
       "      <td>7.78</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.51766</td>\n",
       "      <td>13.21</td>\n",
       "      <td>3.69</td>\n",
       "      <td>1.29</td>\n",
       "      <td>72.61</td>\n",
       "      <td>0.57</td>\n",
       "      <td>8.22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.51742</td>\n",
       "      <td>13.27</td>\n",
       "      <td>3.62</td>\n",
       "      <td>1.24</td>\n",
       "      <td>73.08</td>\n",
       "      <td>0.55</td>\n",
       "      <td>8.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.51596</td>\n",
       "      <td>12.79</td>\n",
       "      <td>3.61</td>\n",
       "      <td>1.62</td>\n",
       "      <td>72.97</td>\n",
       "      <td>0.64</td>\n",
       "      <td>8.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.51743</td>\n",
       "      <td>13.30</td>\n",
       "      <td>3.60</td>\n",
       "      <td>1.14</td>\n",
       "      <td>73.09</td>\n",
       "      <td>0.58</td>\n",
       "      <td>8.17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.51756</td>\n",
       "      <td>13.15</td>\n",
       "      <td>3.61</td>\n",
       "      <td>1.05</td>\n",
       "      <td>73.24</td>\n",
       "      <td>0.57</td>\n",
       "      <td>8.24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.51918</td>\n",
       "      <td>14.04</td>\n",
       "      <td>3.58</td>\n",
       "      <td>1.37</td>\n",
       "      <td>72.08</td>\n",
       "      <td>0.56</td>\n",
       "      <td>8.30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.51755</td>\n",
       "      <td>13.00</td>\n",
       "      <td>3.60</td>\n",
       "      <td>1.36</td>\n",
       "      <td>72.99</td>\n",
       "      <td>0.57</td>\n",
       "      <td>8.40</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.51571</td>\n",
       "      <td>12.72</td>\n",
       "      <td>3.46</td>\n",
       "      <td>1.56</td>\n",
       "      <td>73.20</td>\n",
       "      <td>0.67</td>\n",
       "      <td>8.09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.51763</td>\n",
       "      <td>12.80</td>\n",
       "      <td>3.66</td>\n",
       "      <td>1.27</td>\n",
       "      <td>73.01</td>\n",
       "      <td>0.60</td>\n",
       "      <td>8.56</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.51589</td>\n",
       "      <td>12.88</td>\n",
       "      <td>3.43</td>\n",
       "      <td>1.40</td>\n",
       "      <td>73.28</td>\n",
       "      <td>0.69</td>\n",
       "      <td>8.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.51748</td>\n",
       "      <td>12.86</td>\n",
       "      <td>3.56</td>\n",
       "      <td>1.27</td>\n",
       "      <td>73.21</td>\n",
       "      <td>0.54</td>\n",
       "      <td>8.38</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.17</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.51763</td>\n",
       "      <td>12.61</td>\n",
       "      <td>3.59</td>\n",
       "      <td>1.31</td>\n",
       "      <td>73.29</td>\n",
       "      <td>0.58</td>\n",
       "      <td>8.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "         RI     Na    Mg    Al     Si     K    Ca   Ba    Fe  Type\n",
       "0   1.52101  13.64  4.49  1.10  71.78  0.06  8.75  0.0  0.00     1\n",
       "1   1.51761  13.89  3.60  1.36  72.73  0.48  7.83  0.0  0.00     1\n",
       "2   1.51618  13.53  3.55  1.54  72.99  0.39  7.78  0.0  0.00     1\n",
       "3   1.51766  13.21  3.69  1.29  72.61  0.57  8.22  0.0  0.00     1\n",
       "4   1.51742  13.27  3.62  1.24  73.08  0.55  8.07  0.0  0.00     1\n",
       "5   1.51596  12.79  3.61  1.62  72.97  0.64  8.07  0.0  0.26     1\n",
       "6   1.51743  13.30  3.60  1.14  73.09  0.58  8.17  0.0  0.00     1\n",
       "7   1.51756  13.15  3.61  1.05  73.24  0.57  8.24  0.0  0.00     1\n",
       "8   1.51918  14.04  3.58  1.37  72.08  0.56  8.30  0.0  0.00     1\n",
       "9   1.51755  13.00  3.60  1.36  72.99  0.57  8.40  0.0  0.11     1\n",
       "10  1.51571  12.72  3.46  1.56  73.20  0.67  8.09  0.0  0.24     1\n",
       "11  1.51763  12.80  3.66  1.27  73.01  0.60  8.56  0.0  0.00     1\n",
       "12  1.51589  12.88  3.43  1.40  73.28  0.69  8.05  0.0  0.24     1\n",
       "13  1.51748  12.86  3.56  1.27  73.21  0.54  8.38  0.0  0.17     1\n",
       "14  1.51763  12.61  3.59  1.31  73.29  0.58  8.50  0.0  0.00     1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(15)"
   ]
  },
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   "execution_count": 8,
   "metadata": {
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    },
    "id": "gxfuZSjfL9Tp",
    "outputId": "d1a19685-b24a-40a5-f0f8-03e5f461d189"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RI      float64\n",
       "Na      float64\n",
       "Mg      float64\n",
       "Al      float64\n",
       "Si      float64\n",
       "K       float64\n",
       "Ca      float64\n",
       "Ba      float64\n",
       "Fe      float64\n",
       "Type      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_cell_guid": "4c5e49d9-fb5a-3147-14c8-546c3b0b852c",
    "_uuid": "404c386ce3a3a8050d44420ae5f193c71b66afbd",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "id": "BV0ESB1wL9Tt",
    "outputId": "e2989af0-bfc7-4597-8d61-30f21230b022"
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RI</th>\n",
       "      <th>Na</th>\n",
       "      <th>Mg</th>\n",
       "      <th>Al</th>\n",
       "      <th>Si</th>\n",
       "      <th>K</th>\n",
       "      <th>Ca</th>\n",
       "      <th>Ba</th>\n",
       "      <th>Fe</th>\n",
       "      <th>Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>214.000000</td>\n",
       "      <td>214.000000</td>\n",
       "      <td>214.000000</td>\n",
       "      <td>214.000000</td>\n",
       "      <td>214.000000</td>\n",
       "      <td>214.000000</td>\n",
       "      <td>214.000000</td>\n",
       "      <td>214.000000</td>\n",
       "      <td>214.000000</td>\n",
       "      <td>214.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.518365</td>\n",
       "      <td>13.407850</td>\n",
       "      <td>2.684533</td>\n",
       "      <td>1.444907</td>\n",
       "      <td>72.650935</td>\n",
       "      <td>0.497056</td>\n",
       "      <td>8.956963</td>\n",
       "      <td>0.175047</td>\n",
       "      <td>0.057009</td>\n",
       "      <td>2.780374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.003037</td>\n",
       "      <td>0.816604</td>\n",
       "      <td>1.442408</td>\n",
       "      <td>0.499270</td>\n",
       "      <td>0.774546</td>\n",
       "      <td>0.652192</td>\n",
       "      <td>1.423153</td>\n",
       "      <td>0.497219</td>\n",
       "      <td>0.097439</td>\n",
       "      <td>2.103739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.511150</td>\n",
       "      <td>10.730000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.290000</td>\n",
       "      <td>69.810000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.430000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.516522</td>\n",
       "      <td>12.907500</td>\n",
       "      <td>2.115000</td>\n",
       "      <td>1.190000</td>\n",
       "      <td>72.280000</td>\n",
       "      <td>0.122500</td>\n",
       "      <td>8.240000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.517680</td>\n",
       "      <td>13.300000</td>\n",
       "      <td>3.480000</td>\n",
       "      <td>1.360000</td>\n",
       "      <td>72.790000</td>\n",
       "      <td>0.555000</td>\n",
       "      <td>8.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.519157</td>\n",
       "      <td>13.825000</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>1.630000</td>\n",
       "      <td>73.087500</td>\n",
       "      <td>0.610000</td>\n",
       "      <td>9.172500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.533930</td>\n",
       "      <td>17.380000</td>\n",
       "      <td>4.490000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>75.410000</td>\n",
       "      <td>6.210000</td>\n",
       "      <td>16.190000</td>\n",
       "      <td>3.150000</td>\n",
       "      <td>0.510000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "               RI          Na          Mg          Al          Si           K  \\\n",
       "count  214.000000  214.000000  214.000000  214.000000  214.000000  214.000000   \n",
       "mean     1.518365   13.407850    2.684533    1.444907   72.650935    0.497056   \n",
       "std      0.003037    0.816604    1.442408    0.499270    0.774546    0.652192   \n",
       "min      1.511150   10.730000    0.000000    0.290000   69.810000    0.000000   \n",
       "25%      1.516522   12.907500    2.115000    1.190000   72.280000    0.122500   \n",
       "50%      1.517680   13.300000    3.480000    1.360000   72.790000    0.555000   \n",
       "75%      1.519157   13.825000    3.600000    1.630000   73.087500    0.610000   \n",
       "max      1.533930   17.380000    4.490000    3.500000   75.410000    6.210000   \n",
       "\n",
       "               Ca          Ba          Fe        Type  \n",
       "count  214.000000  214.000000  214.000000  214.000000  \n",
       "mean     8.956963    0.175047    0.057009    2.780374  \n",
       "std      1.423153    0.497219    0.097439    2.103739  \n",
       "min      5.430000    0.000000    0.000000    1.000000  \n",
       "25%      8.240000    0.000000    0.000000    1.000000  \n",
       "50%      8.600000    0.000000    0.000000    2.000000  \n",
       "75%      9.172500    0.000000    0.100000    3.000000  \n",
       "max     16.190000    3.150000    0.510000    7.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_cell_guid": "43f288e9-3640-7e9a-6707-cab227c611f3",
    "_uuid": "98cf7084063fd8ecf714dc1db6988b2cbf03a77e",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "tyyt2NYLL9Tu",
    "outputId": "250d51b5-7a2d-44e6-cceb-b7e7c8d1e697"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Type\n",
       "2    76\n",
       "1    70\n",
       "7    29\n",
       "3    17\n",
       "5    13\n",
       "6     9\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "X = df.drop('Type', axis=1) \n",
    "y = df['Type']\n",
    "\n",
    "label_encoder = LabelEncoder()\n",
    "y_encoded = label_encoder.fit_transform(y)\n",
    "\n",
    "# Scaling and transformation\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "# Apply PCA\n",
    "pca = PCA(n_components=0.95) \n",
    "X_pca = pca.fit_transform(X_scaled)\n",
    "\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_pca, y_encoded, test_size=0.2, random_state=42)\n",
    "\n",
    "\n",
    "X_train_tensor = torch.tensor(X_train, dtype=torch.float32)\n",
    "X_test_tensor = torch.tensor(X_test, dtype=torch.float32)\n",
    "y_train_tensor = torch.tensor(y_train, dtype=torch.long)\n",
    "y_test_tensor = torch.tensor(y_test, dtype=torch.long)\n",
    "\n",
    "train_dataset = TensorDataset(X_train_tensor, y_train_tensor)\n",
    "test_dataset = TensorDataset(X_test_tensor, y_test_tensor)\n",
    "\n",
    "train_loader = DataLoader(train_dataset, batch_size=4, shuffle=True)\n",
    "test_loader = DataLoader(test_dataset, batch_size=4, shuffle=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLP(\n",
       "  (fc1): Linear(in_features=6, out_features=512, bias=True)\n",
       "  (dropout): Dropout(p=0.5, inplace=False)\n",
       "  (fc2): Linear(in_features=512, out_features=512, bias=True)\n",
       "  (fc3): Linear(in_features=512, out_features=6, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class MLP(nn.Module):\n",
    "    def __init__(self, input_dim, hidden_dim, output_dim):\n",
    "        super(MLP, self).__init__()\n",
    "        self.fc1 = nn.Linear(input_dim, hidden_dim)\n",
    "        self.dropout = nn.Dropout(0.5)\n",
    "        self.fc2 = nn.Linear(hidden_dim, hidden_dim)\n",
    "        self.fc3 = nn.Linear(hidden_dim, output_dim)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        x = torch.relu(self.fc1(x))\n",
    "        x = self.dropout(x)\n",
    "        x = torch.relu(self.fc2(x))\n",
    "        x = self.fc3(x)\n",
    "        return x\n",
    "\n",
    "input_dim = X_train_tensor.shape[1]\n",
    "hidden_dim = 512\n",
    "output_dim = len(np.unique(y_encoded))\n",
    "\n",
    "model = MLP(input_dim, hidden_dim, output_dim)\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
    "\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "model.to(device)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Training loop\n",
    "num_epochs = 100\n",
    "early_stopping_patience = 10\n",
    "best_loss = float('inf')\n",
    "patience_counter = 0\n",
    "\n",
    "# Lists to store metrics\n",
    "train_losses = []\n",
    "val_losses = []\n",
    "val_accuracies = []\n",
    "val_f1_scores = []\n",
    "val_recalls = []\n",
    "val_precisions = []\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    model.train()\n",
    "    for X_batch, y_batch in train_loader:\n",
    "        X_batch, y_batch = X_batch.to(device), y_batch.to(device)\n",
    "        \n",
    "        outputs = model(X_batch)\n",
    "        loss = criterion(outputs, y_batch)\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "    \n",
    "    train_losses.append(loss.item())\n",
    "    \n",
    "\n",
    "    model.eval()\n",
    "    val_loss = 0\n",
    "    all_preds = []\n",
    "    all_targets = []\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        for X_batch, y_batch in test_loader:\n",
    "            X_batch, y_batch = X_batch.to(device), y_batch.to(device)\n",
    "            outputs = model(X_batch)\n",
    "            loss = criterion(outputs, y_batch)\n",
    "            val_loss += loss.item()\n",
    "            \n",
    "            _, predicted = torch.max(outputs.data, 1)\n",
    "            all_preds.extend(predicted.cpu().numpy())\n",
    "            all_targets.extend(y_batch.cpu().numpy())\n",
    "    \n",
    "    val_loss /= len(test_loader)\n",
    "    val_losses.append(val_loss)\n",
    "    \n",
    "    accuracy = accuracy_score(all_targets, all_preds)\n",
    "    f1 = f1_score(all_targets, all_preds, average='macro')\n",
    "    recall = recall_score(all_targets, all_preds, average='macro')\n",
    "    precision = precision_score(all_targets, all_preds, average='macro')\n",
    "    \n",
    "    val_accuracies.append(accuracy)\n",
    "    val_f1_scores.append(f1)\n",
    "    val_recalls.append(recall)\n",
    "    val_precisions.append(precision)\n",
    "    \n",
    "    if val_loss < best_loss:\n",
    "        best_loss = val_loss\n",
    "        patience_counter = 0\n",
    "        torch.save(model.state_dict(), 'best_model.pt')\n",
    "    else:\n",
    "        patience_counter += 1\n",
    "        if patience_counter >= early_stopping_patience:\n",
    "            print(f'Early stopping at epoch {epoch+1}')\n",
    "            break\n",
    "    \n",
    "    print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item()}, Val Loss: {val_loss}, Accuracy: {accuracy}, F1 Score: {f1}, Recall: {recall}, Precision: {precision}')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "model.load_state_dict(torch.load('best_model.pt'))\n",
    "\n",
    "# Evaluation\n",
    "model.eval()\n",
    "correct = 0\n",
    "total = 0\n",
    "all_preds = []\n",
    "all_targets = []\n",
    "\n",
    "with torch.no_grad():\n",
    "    for X_batch, y_batch in test_loader:\n",
    "        X_batch, y_batch = X_batch.to(device), y_batch.to(device)\n",
    "        outputs = model(X_batch)\n",
    "        _, predicted = torch.max(outputs.data, 1)\n",
    "        total += y_batch.size(0)\n",
    "        correct += (predicted == y_batch).sum().item()\n",
    "        all_preds.extend(predicted.cpu().numpy())\n",
    "        all_targets.extend(y_batch.cpu().numpy())\n",
    "\n",
    "accuracy = 100 * correct / total\n",
    "f1 = f1_score(all_targets, all_preds, average='macro')\n",
    "recall = recall_score(all_targets, all_preds, average='macro')\n",
    "precision = precision_score(all_targets, all_preds, average='macro')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [1/100], Loss: 0.6394459009170532, Val Loss: 0.8161449615250934, Accuracy: 0.813953488372093, F1 Score: 0.7605204400600821, Recall: 0.7408008658008658, Precision: 0.8513227513227513\n",
      "Epoch [2/100], Loss: 0.7078919410705566, Val Loss: 0.9193001550368287, Accuracy: 0.7674418604651163, F1 Score: 0.7739567621920563, Recall: 0.7606421356421356, Precision: 0.825925925925926\n",
      "Epoch [3/100], Loss: 0.7870150208473206, Val Loss: 0.8668138753538105, Accuracy: 0.7906976744186046, F1 Score: 0.772238727140688, Recall: 0.7693001443001443, Precision: 0.7904946904946906\n",
      "Epoch [4/100], Loss: 0.6146475672721863, Val Loss: 0.6262529735368761, Accuracy: 0.8372093023255814, F1 Score: 0.8593896173606318, Recall: 0.840007215007215, Precision: 0.9004273504273503\n",
      "Epoch [5/100], Loss: 0.8205491900444031, Val Loss: 0.8555138549682769, Accuracy: 0.8372093023255814, F1 Score: 0.8205448201825014, Recall: 0.7963564213564213, Precision: 0.8715811965811966\n",
      "Epoch [6/100], Loss: 0.610707700252533, Val Loss: 0.8957815028049729, Accuracy: 0.8604651162790697, F1 Score: 0.8338906304423546, Recall: 0.8082611832611833, Precision: 0.8848484848484848\n",
      "Epoch [7/100], Loss: 0.660676896572113, Val Loss: 0.8127548223869367, Accuracy: 0.7674418604651163, F1 Score: 0.7270397208803007, Recall: 0.7137445887445887, Precision: 0.7615384615384615\n",
      "Epoch [8/100], Loss: 0.5088979601860046, Val Loss: 0.8252820983867754, Accuracy: 0.813953488372093, F1 Score: 0.7880048431772572, Recall: 0.7546897546897546, Precision: 0.8515151515151514\n",
      "Epoch [9/100], Loss: 0.8419011235237122, Val Loss: 0.7370651602237062, Accuracy: 0.813953488372093, F1 Score: 0.7897586980920316, Recall: 0.7812049062049061, Precision: 0.8135003885003885\n",
      "Epoch [10/100], Loss: 1.202831745147705, Val Loss: 0.753336454148997, Accuracy: 0.7441860465116279, F1 Score: 0.7128456510809452, Recall: 0.7050865800865799, Precision: 0.8092592592592593\n",
      "Epoch [11/100], Loss: 1.2563937902450562, Val Loss: 0.9334866653450511, Accuracy: 0.7674418604651163, F1 Score: 0.7385320956079694, Recall: 0.7276334776334776, Precision: 0.7624643874643874\n",
      "Epoch [12/100], Loss: 0.8490025401115417, Val Loss: 0.6924717002124949, Accuracy: 0.7906976744186046, F1 Score: 0.7692084241103849, Recall: 0.7427849927849928, Precision: 0.8212250712250713\n",
      "Epoch [13/100], Loss: 0.7722284197807312, Val Loss: 0.8375678610225971, Accuracy: 0.813953488372093, F1 Score: 0.7845844061530336, Recall: 0.7812049062049061, Precision: 0.8033152033152033\n",
      "Early stopping at epoch 14\n",
      "Test Accuracy: 83.72%\n",
      "Test F1 Score: 0.86\n",
      "Test Recall: 0.84\n",
      "Test Precision: 0.90\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "print(f'Test Accuracy: {accuracy:.2f}%')\n",
    "print(f'Test F1 Score: {f1:.2f}')\n",
    "print(f'Test Recall: {recall:.2f}')\n",
    "print(f'Test Precision: {precision:.2f}')\n",
    "\n",
    "# Function to plot metrics\n",
    "def plot_metrics(train_losses, val_losses, val_accuracies, val_f1_scores, val_recalls, val_precisions):\n",
    "    epochs = range(1, len(train_losses) + 1)\n",
    "    \n",
    "    plt.figure(figsize=(15, 10))\n",
    "    \n",
    "    plt.subplot(2, 3, 1)\n",
    "    plt.plot(epochs, train_losses, label='Train Loss')\n",
    "    plt.plot(epochs, val_losses, label='Validation Loss')\n",
    "    plt.title('Loss')\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.legend()\n",
    "    \n",
    "    plt.subplot(2, 3, 2)\n",
    "    plt.plot(epochs, val_accuracies, label='Validation Accuracy')\n",
    "    plt.title('Accuracy')\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.ylabel('Accuracy')\n",
    "    plt.legend()\n",
    "    \n",
    "    plt.subplot(2, 3, 3)\n",
    "    plt.plot(epochs, val_f1_scores, label='Validation F1 Score')\n",
    "    plt.title('F1 Score')\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.ylabel('F1 Score')\n",
    "    plt.legend()\n",
    "    \n",
    "    plt.subplot(2, 3, 4)\n",
    "    plt.plot(epochs, val_recalls, label='Validation Recall')\n",
    "    plt.title('Recall')\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.ylabel('Recall')\n",
    "    plt.legend()\n",
    "    \n",
    "    plt.subplot(2, 3, 5)\n",
    "    plt.plot(epochs, val_precisions, label='Validation Precision')\n",
    "    plt.title('Precision')\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.ylabel('Precision')\n",
    "    plt.legend()\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "plot_metrics(train_losses, val_losses, val_accuracies, val_f1_scores, val_recalls, val_precisions)"
   ]
  }
 ],
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