ProgrammingAICA2Zoppi.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Programming AI - CA2 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "!pip install pandas scikit-learn seaborn matplotlib\n",
    "!pip install tensorflow==2.12.0\n",
    "!pip install keras==2.12.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Descriptive statistics.\n",
    "\n",
    "We want to understand how is this dataset, the variance, the medium values and the distribution of the type. The shape of dataset and how is formed. \n",
    "We also noticed that the id is not needed for our goals. So sooner or later we'll get rid of that. There are no missing values in the current dataset so there is need to drop value except for id and type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
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       "      <th>id</th>\n",
       "      <th>ri</th>\n",
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       "      <th>0</th>\n",
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       "      <td>4</td>\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",
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       "      <td>1.24</td>\n",
       "      <td>73.08</td>\n",
       "      <td>0.55</td>\n",
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       "      <td>6</td>\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",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\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",
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       "      <td>13.15</td>\n",
       "      <td>3.61</td>\n",
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       "      <td>3.58</td>\n",
       "      <td>1.37</td>\n",
       "      <td>72.08</td>\n",
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      "text/plain": [
       "   id       ri     na    mg    al     si     k    ca   ba    fe  type\n",
       "0   1  1.52101  13.64  4.49  1.10  71.78  0.06  8.75  0.0  0.00     1\n",
       "1   2  1.51761  13.89  3.60  1.36  72.73  0.48  7.83  0.0  0.00     1\n",
       "2   3  1.51618  13.53  3.55  1.54  72.99  0.39  7.78  0.0  0.00     1\n",
       "3   4  1.51766  13.21  3.69  1.29  72.61  0.57  8.22  0.0  0.00     1\n",
       "4   5  1.51742  13.27  3.62  1.24  73.08  0.55  8.07  0.0  0.00     1\n",
       "5   6  1.51596  12.79  3.61  1.62  72.97  0.64  8.07  0.0  0.26     1\n",
       "6   7  1.51743  13.30  3.60  1.14  73.09  0.58  8.17  0.0  0.00     1\n",
       "7   8  1.51756  13.15  3.61  1.05  73.24  0.57  8.24  0.0  0.00     1\n",
       "8   9  1.51918  14.04  3.58  1.37  72.08  0.56  8.30  0.0  0.00     1\n",
       "9  10  1.51755  13.00  3.60  1.36  72.99  0.57  8.40  0.0  0.11     1"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "glass_df = pd.read_csv('glass_data.csv')\n",
    "# Display the first few rows of the dataset\n",
    "glass_df.head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
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       "      <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",
       "      <td>214.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>107.500000</td>\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>61.920648</td>\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>1.000000</td>\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>54.250000</td>\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>107.500000</td>\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>160.750000</td>\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>214.000000</td>\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",
       "</div>"
      ],
      "text/plain": [
       "               id          ri          na          mg          al          si  \\\n",
       "count  214.000000  214.000000  214.000000  214.000000  214.000000  214.000000   \n",
       "mean   107.500000    1.518365   13.407850    2.684533    1.444907   72.650935   \n",
       "std     61.920648    0.003037    0.816604    1.442408    0.499270    0.774546   \n",
       "min      1.000000    1.511150   10.730000    0.000000    0.290000   69.810000   \n",
       "25%     54.250000    1.516522   12.907500    2.115000    1.190000   72.280000   \n",
       "50%    107.500000    1.517680   13.300000    3.480000    1.360000   72.790000   \n",
       "75%    160.750000    1.519157   13.825000    3.600000    1.630000   73.087500   \n",
       "max    214.000000    1.533930   17.380000    4.490000    3.500000   75.410000   \n",
       "\n",
       "                k          ca          ba          fe        type  \n",
       "count  214.000000  214.000000  214.000000  214.000000  214.000000  \n",
       "mean     0.497056    8.956963    0.175047    0.057009    2.780374  \n",
       "std      0.652192    1.423153    0.497219    0.097439    2.103739  \n",
       "min      0.000000    5.430000    0.000000    0.000000    1.000000  \n",
       "25%      0.122500    8.240000    0.000000    0.000000    1.000000  \n",
       "50%      0.555000    8.600000    0.000000    0.000000    2.000000  \n",
       "75%      0.610000    9.172500    0.000000    0.100000    3.000000  \n",
       "max      6.210000   16.190000    3.150000    0.510000    7.000000  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glass_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id        int64\n",
       "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": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glass_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id      107.500000\n",
       "ri        1.518365\n",
       "na       13.407850\n",
       "mg        2.684533\n",
       "al        1.444907\n",
       "si       72.650935\n",
       "k         0.497056\n",
       "ca        8.956963\n",
       "ba        0.175047\n",
       "fe        0.057009\n",
       "type      2.780374\n",
       "dtype: float64"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glass_df.mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id      107.50000\n",
       "ri        1.51768\n",
       "na       13.30000\n",
       "mg        3.48000\n",
       "al        1.36000\n",
       "si       72.79000\n",
       "k         0.55500\n",
       "ca        8.60000\n",
       "ba        0.00000\n",
       "fe        0.00000\n",
       "type      2.00000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glass_df.median()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id       1.0000\n",
       "ri       1.5159\n",
       "na      13.0000\n",
       "mg       0.0000\n",
       "al       1.5400\n",
       "si      72.8600\n",
       "k        0.0000\n",
       "ca       8.0300\n",
       "ba       0.0000\n",
       "fe       0.0000\n",
       "type     2.0000\n",
       "Name: 0, dtype: float64"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glass_df.mode().iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Standard Deviation\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "id      61.920648\n",
       "ri       0.003037\n",
       "na       0.816604\n",
       "mg       1.442408\n",
       "al       0.499270\n",
       "si       0.774546\n",
       "k        0.652192\n",
       "ca       1.423153\n",
       "ba       0.497219\n",
       "fe       0.097439\n",
       "type     2.103739\n",
       "dtype: float64"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Standard Deviation\")\n",
    "glass_df.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variance\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "id      3834.166667\n",
       "ri         0.000009\n",
       "na         0.666841\n",
       "mg         2.080540\n",
       "al         0.249270\n",
       "si         0.599921\n",
       "k          0.425354\n",
       "ca         2.025366\n",
       "ba         0.247227\n",
       "fe         0.009494\n",
       "type       4.425716\n",
       "dtype: float64"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Variance')\n",
    "glass_df.var()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Range\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "id      213.00000\n",
       "ri        0.02278\n",
       "na        6.65000\n",
       "mg        4.49000\n",
       "al        3.21000\n",
       "si        5.60000\n",
       "k         6.21000\n",
       "ca       10.76000\n",
       "ba        3.15000\n",
       "fe        0.51000\n",
       "type      6.00000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Range')\n",
    "glass_df.max() - glass_df.min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to know how each feature behaves and what happens in order to identify any anomalies. The dataset doens't have categorical data since the categories of glass are encoded with a number so there is not need of one shot encoding. We notice that we might need to scale the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1500 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the distribution of each feature\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "plt.figure(figsize=(12, 6))\n",
    "features = glass_df.drop(columns=['id', 'type'])\n",
    "\n",
    "# Extended names for features\n",
    "feature_extended_name = {\n",
    "   'na': 'Sodium',\n",
    "   'ri': 'Refractive index',\n",
    "   'mg': 'Magnesium',\n",
    "   'al': 'Aluminium',\n",
    "   'si': 'Silicon',\n",
    "   'k': 'Potassium',\n",
    "   'ca': 'Calcium',\n",
    "   'ba': 'Barium',\n",
    "   'fe': 'Iron'\n",
    "}\n",
    "\n",
    "# Create a histogram for each feature\n",
    "num_features = len(features.columns)\n",
    "num_cols = 3  # Number of columns for subplots\n",
    "num_rows = (num_features + num_cols - 1) // num_cols  # Calculate number of rows needed\n",
    "\n",
    "plt.figure(figsize=(15, num_rows * 5))  # Adjust the figure size\n",
    "\n",
    "for i, feature in enumerate(features.columns):\n",
    "    plt.subplot(num_rows, num_cols, i + 1)\n",
    "    sns.histplot(data=features, x=feature, kde=True)\n",
    "    name = feature_extended_name.get(feature, feature)\n",
    "    plt.title(f'Distribution of {name}')\n",
    "    \n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(12,12))\n",
    "type_counts = glass_df['type'].value_counts()\n",
    "type_counts.plot(kind='bar', color='skyblue')\n",
    "plt.title('Distribution of Glass Types')\n",
    "plt.xlabel('Type of Glass')\n",
    "plt.ylabel('Count')\n",
    "plt.xticks(rotation=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    76\n",
       "1    70\n",
       "7    29\n",
       "3    17\n",
       "5    13\n",
       "6     9\n",
       "Name: type, dtype: int64"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glass_df['type'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The observation that we've here:\n",
    "- Dataset 214 items.\n",
    "- The dataset is unbalanced since:\n",
    "    - glass type 4 is missing.\n",
    "    - glass type 2 and type 1 has a higher number of items then the others.\n",
    "    - glass type 6 has less instances.\n",
    "\n",
    "Probabally we should use SMOTE before correlation matrix computation and after to see how the unbalancing affects correlations between features. Later we will apply SMOTE.\n",
    "SMOTE is a data augmentation tecnique it increases the size of the training set by generating many realistic variants of each training instance. In case of a unbalanced dataset, it is used to generate more samples of the less frequent classes. Before doing this we want to detect outliers and remove them.\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Detect outliers.\n",
    "Outliers are data points that significantly deviate from the majority of the data. They can be caused by errors, anomalies, or simply rare events. Outlies can lead to overfitting. Outliers may lead to overfitting if the model tries to fit them perfectly, thus capturing their noise rather than the actual patterns in the data. We decided to ignore outliners. For detecting outliners in classification problem we use Isolation Forest.\n",
    "## Isolation Forest\n",
    "The algoritm builds a random forest in which each decision tree is grown randomly: at each node, it pickes a feature randomly, the it picks a random\n",
    "threasold value to split the dataset in two. This process is repeated until all instances are isolated. Anomalies are usually far from other instances,\n",
    "so on average they tend to get isolated in fewer steps then normal instances. Once we've indentified the outliners we select and drop them. It's not the best approach since we risk underfit doing but the number is not meaniful in our case, we can drop. Alternative strategy could be repleace with the mean.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of outliers detected: 22\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</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",
       "      <th>outlier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>104</td>\n",
       "      <td>1.52725</td>\n",
       "      <td>13.80</td>\n",
       "      <td>3.15</td>\n",
       "      <td>0.66</td>\n",
       "      <td>70.57</td>\n",
       "      <td>0.08</td>\n",
       "      <td>11.64</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>106</td>\n",
       "      <td>1.52475</td>\n",
       "      <td>11.45</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.88</td>\n",
       "      <td>72.19</td>\n",
       "      <td>0.81</td>\n",
       "      <td>13.24</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.34</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>107</td>\n",
       "      <td>1.53125</td>\n",
       "      <td>10.73</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.10</td>\n",
       "      <td>69.81</td>\n",
       "      <td>0.58</td>\n",
       "      <td>13.30</td>\n",
       "      <td>3.15</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>108</td>\n",
       "      <td>1.53393</td>\n",
       "      <td>12.30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>70.16</td>\n",
       "      <td>0.12</td>\n",
       "      <td>16.19</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>110</td>\n",
       "      <td>1.51818</td>\n",
       "      <td>13.72</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.56</td>\n",
       "      <td>74.45</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.99</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>111</td>\n",
       "      <td>1.52664</td>\n",
       "      <td>11.23</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.77</td>\n",
       "      <td>73.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.68</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>112</td>\n",
       "      <td>1.52739</td>\n",
       "      <td>11.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.75</td>\n",
       "      <td>73.08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.96</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>113</td>\n",
       "      <td>1.52777</td>\n",
       "      <td>12.64</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.67</td>\n",
       "      <td>72.02</td>\n",
       "      <td>0.06</td>\n",
       "      <td>14.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>132</td>\n",
       "      <td>1.52614</td>\n",
       "      <td>13.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.36</td>\n",
       "      <td>71.24</td>\n",
       "      <td>0.19</td>\n",
       "      <td>13.44</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.10</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>164</td>\n",
       "      <td>1.51514</td>\n",
       "      <td>14.01</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.50</td>\n",
       "      <td>69.89</td>\n",
       "      <td>1.68</td>\n",
       "      <td>5.87</td>\n",
       "      <td>2.20</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>172</td>\n",
       "      <td>1.51316</td>\n",
       "      <td>13.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.04</td>\n",
       "      <td>70.48</td>\n",
       "      <td>6.21</td>\n",
       "      <td>6.96</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>173</td>\n",
       "      <td>1.51321</td>\n",
       "      <td>13.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.02</td>\n",
       "      <td>70.70</td>\n",
       "      <td>6.21</td>\n",
       "      <td>6.93</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>175</td>\n",
       "      <td>1.52058</td>\n",
       "      <td>12.85</td>\n",
       "      <td>1.61</td>\n",
       "      <td>2.17</td>\n",
       "      <td>72.18</td>\n",
       "      <td>0.76</td>\n",
       "      <td>9.70</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.51</td>\n",
       "      <td>5</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>176</td>\n",
       "      <td>1.52119</td>\n",
       "      <td>12.97</td>\n",
       "      <td>0.33</td>\n",
       "      <td>1.51</td>\n",
       "      <td>73.39</td>\n",
       "      <td>0.13</td>\n",
       "      <td>11.27</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.28</td>\n",
       "      <td>5</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>185</td>\n",
       "      <td>1.51115</td>\n",
       "      <td>17.38</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.34</td>\n",
       "      <td>75.41</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.65</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>186</td>\n",
       "      <td>1.51131</td>\n",
       "      <td>13.69</td>\n",
       "      <td>3.20</td>\n",
       "      <td>1.81</td>\n",
       "      <td>72.81</td>\n",
       "      <td>1.76</td>\n",
       "      <td>5.43</td>\n",
       "      <td>1.19</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>187</td>\n",
       "      <td>1.51838</td>\n",
       "      <td>14.32</td>\n",
       "      <td>3.26</td>\n",
       "      <td>2.22</td>\n",
       "      <td>71.25</td>\n",
       "      <td>1.46</td>\n",
       "      <td>5.79</td>\n",
       "      <td>1.63</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>189</td>\n",
       "      <td>1.52247</td>\n",
       "      <td>14.86</td>\n",
       "      <td>2.20</td>\n",
       "      <td>2.06</td>\n",
       "      <td>70.26</td>\n",
       "      <td>0.76</td>\n",
       "      <td>9.76</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>190</td>\n",
       "      <td>1.52365</td>\n",
       "      <td>15.79</td>\n",
       "      <td>1.83</td>\n",
       "      <td>1.31</td>\n",
       "      <td>70.43</td>\n",
       "      <td>0.31</td>\n",
       "      <td>8.61</td>\n",
       "      <td>1.68</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>202</td>\n",
       "      <td>1.51653</td>\n",
       "      <td>11.95</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.19</td>\n",
       "      <td>75.18</td>\n",
       "      <td>2.70</td>\n",
       "      <td>8.93</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>208</td>\n",
       "      <td>1.51831</td>\n",
       "      <td>14.39</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.82</td>\n",
       "      <td>72.86</td>\n",
       "      <td>1.41</td>\n",
       "      <td>6.47</td>\n",
       "      <td>2.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>210</td>\n",
       "      <td>1.51623</td>\n",
       "      <td>14.14</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.88</td>\n",
       "      <td>72.61</td>\n",
       "      <td>0.08</td>\n",
       "      <td>9.18</td>\n",
       "      <td>1.06</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id       ri     na    mg    al     si     k     ca    ba    fe  type  \\\n",
       "103  104  1.52725  13.80  3.15  0.66  70.57  0.08  11.64  0.00  0.00     2   \n",
       "105  106  1.52475  11.45  0.00  1.88  72.19  0.81  13.24  0.00  0.34     2   \n",
       "106  107  1.53125  10.73  0.00  2.10  69.81  0.58  13.30  3.15  0.28     2   \n",
       "107  108  1.53393  12.30  0.00  1.00  70.16  0.12  16.19  0.00  0.24     2   \n",
       "109  110  1.51818  13.72  0.00  0.56  74.45  0.00  10.99  0.00  0.00     2   \n",
       "110  111  1.52664  11.23  0.00  0.77  73.21  0.00  14.68  0.00  0.00     2   \n",
       "111  112  1.52739  11.02  0.00  0.75  73.08  0.00  14.96  0.00  0.00     2   \n",
       "112  113  1.52777  12.64  0.00  0.67  72.02  0.06  14.40  0.00  0.00     2   \n",
       "131  132  1.52614  13.70  0.00  1.36  71.24  0.19  13.44  0.00  0.10     2   \n",
       "163  164  1.51514  14.01  2.68  3.50  69.89  1.68   5.87  2.20  0.00     5   \n",
       "171  172  1.51316  13.02  0.00  3.04  70.48  6.21   6.96  0.00  0.00     5   \n",
       "172  173  1.51321  13.00  0.00  3.02  70.70  6.21   6.93  0.00  0.00     5   \n",
       "174  175  1.52058  12.85  1.61  2.17  72.18  0.76   9.70  0.24  0.51     5   \n",
       "175  176  1.52119  12.97  0.33  1.51  73.39  0.13  11.27  0.00  0.28     5   \n",
       "184  185  1.51115  17.38  0.00  0.34  75.41  0.00   6.65  0.00  0.00     6   \n",
       "185  186  1.51131  13.69  3.20  1.81  72.81  1.76   5.43  1.19  0.00     7   \n",
       "186  187  1.51838  14.32  3.26  2.22  71.25  1.46   5.79  1.63  0.00     7   \n",
       "188  189  1.52247  14.86  2.20  2.06  70.26  0.76   9.76  0.00  0.00     7   \n",
       "189  190  1.52365  15.79  1.83  1.31  70.43  0.31   8.61  1.68  0.00     7   \n",
       "201  202  1.51653  11.95  0.00  1.19  75.18  2.70   8.93  0.00  0.00     7   \n",
       "207  208  1.51831  14.39  0.00  1.82  72.86  1.41   6.47  2.88  0.00     7   \n",
       "209  210  1.51623  14.14  0.00  2.88  72.61  0.08   9.18  1.06  0.00     7   \n",
       "\n",
       "     outlier  \n",
       "103       -1  \n",
       "105       -1  \n",
       "106       -1  \n",
       "107       -1  \n",
       "109       -1  \n",
       "110       -1  \n",
       "111       -1  \n",
       "112       -1  \n",
       "131       -1  \n",
       "163       -1  \n",
       "171       -1  \n",
       "172       -1  \n",
       "174       -1  \n",
       "175       -1  \n",
       "184       -1  \n",
       "185       -1  \n",
       "186       -1  \n",
       "188       -1  \n",
       "189       -1  \n",
       "201       -1  \n",
       "207       -1  \n",
       "209       -1  "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import IsolationForest\n",
    "# Drop the 'id' and 'type' columns for outlier detection\n",
    "features = glass_df.drop(columns=['type'])\n",
    "# Initialize the Isolation Forest model\n",
    "iso_forest = IsolationForest(contamination=0.1, random_state=42)\n",
    "\n",
    "# Fit the model and predict outliers\n",
    "glass_df['outlier'] = iso_forest.fit_predict(features)\n",
    "\n",
    "# -1 indicates outliers, 1 indicates inliers\n",
    "outliers = glass_df[glass_df['outlier'] == -1]\n",
    "inliers = glass_df[glass_df['outlier'] == 1]\n",
    "\n",
    "# Display the number of outliers detected\n",
    "print(f\"Number of outliers detected: {len(outliers)}\")\n",
    "\n",
    "# Plot the inliers and outliers\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.scatter(glass_df.index, glass_df['ri'], c=glass_df['outlier'], cmap='coolwarm', edgecolor='k', s=50)\n",
    "plt.title('Isolation Forest: Outliers Detection')\n",
    "plt.show()\n",
    "\n",
    "# Display the outliers\n",
    "outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Remove the outliners and check again the balancing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id         214\n",
       "ri         214\n",
       "na         214\n",
       "mg         214\n",
       "al         214\n",
       "si         214\n",
       "k          214\n",
       "ca         214\n",
       "ba         214\n",
       "fe         214\n",
       "type       214\n",
       "outlier    214\n",
       "dtype: int64"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glass_df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "glass_df = glass_df[glass_df['outlier'] != -1]\n",
    "glass_df = glass_df.drop(columns=['outlier'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id      192\n",
       "ri      192\n",
       "na      192\n",
       "mg      192\n",
       "al      192\n",
       "si      192\n",
       "k       192\n",
       "ca      192\n",
       "ba      192\n",
       "fe      192\n",
       "type    192\n",
       "dtype: int64"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glass_df.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the picture below we show the distribution of the data. It is unbalanced some types of glasses are more present than others and this is a problem. We might want use sampling, SMOTE algorithm to augument the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(12,12))\n",
    "type_counts = glass_df['type'].value_counts()\n",
    "type_counts.plot(kind='bar', color='skyblue')\n",
    "plt.title('Distribution of Glass Types')\n",
    "plt.xlabel('Type of Glass')\n",
    "plt.ylabel('Count')\n",
    "plt.xticks(rotation=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    70\n",
       "2    67\n",
       "7    22\n",
       "3    17\n",
       "5     8\n",
       "6     8\n",
       "Name: type, dtype: int64"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glass_df['type'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we've remove some data containing outliers. The next step is to rebalance thru SMOTE the dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/bin/bash: /home/jozoppi/pyconda/lib/libtinfo.so.6: no version information available (required by /bin/bash)\n",
      "Requirement already satisfied: imbalanced-learn in /home/jozoppi/pyconda/lib/python3.11/site-packages (0.12.2)\n",
      "Requirement already satisfied: numpy>=1.17.3 in /home/jozoppi/pyconda/lib/python3.11/site-packages (from imbalanced-learn) (1.23.5)\n",
      "Requirement already satisfied: scipy>=1.5.0 in /home/jozoppi/pyconda/lib/python3.11/site-packages (from imbalanced-learn) (1.12.0)\n",
      "Requirement already satisfied: scikit-learn>=1.0.2 in /home/jozoppi/pyconda/lib/python3.11/site-packages (from imbalanced-learn) (1.4.2)\n",
      "Requirement already satisfied: joblib>=1.1.1 in /home/jozoppi/pyconda/lib/python3.11/site-packages (from imbalanced-learn) (1.4.2)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /home/jozoppi/pyconda/lib/python3.11/site-packages (from imbalanced-learn) (3.5.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install imbalanced-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation Matrix on Original Data:\n",
      "            ri        na        mg        al        si         k        ca  \\\n",
      "ri    1.000000  0.056694  0.077057 -0.535435 -0.641326 -0.317770  0.682650   \n",
      "na    0.056694  1.000000 -0.515443  0.257375 -0.218881 -0.737161  0.041075   \n",
      "mg    0.077057 -0.515443  1.000000 -0.647897 -0.382386  0.520749 -0.437191   \n",
      "al   -0.535435  0.257375 -0.647897  1.000000  0.427754 -0.084501 -0.161135   \n",
      "si   -0.641326 -0.218881 -0.382386  0.427754  1.000000  0.136207 -0.243004   \n",
      "k    -0.317770 -0.737161  0.520749 -0.084501  0.136207  1.000000 -0.378233   \n",
      "ca    0.682650  0.041075 -0.437191 -0.161135 -0.243004 -0.378233  1.000000   \n",
      "ba   -0.166051  0.544451 -0.673522  0.509793  0.268762 -0.511066 -0.062972   \n",
      "fe    0.002661 -0.182187  0.180491 -0.151436 -0.058938  0.145233 -0.046220   \n",
      "type -0.151150  0.549827 -0.852899  0.655184  0.325234 -0.561308  0.237615   \n",
      "\n",
      "            ba        fe      type  \n",
      "ri   -0.166051  0.002661 -0.151150  \n",
      "na    0.544451 -0.182187  0.549827  \n",
      "mg   -0.673522  0.180491 -0.852899  \n",
      "al    0.509793 -0.151436  0.655184  \n",
      "si    0.268762 -0.058938  0.325234  \n",
      "k    -0.511066  0.145233 -0.561308  \n",
      "ca   -0.062972 -0.046220  0.237615  \n",
      "ba    1.000000 -0.119944  0.674780  \n",
      "fe   -0.119944  1.000000 -0.209002  \n",
      "type  0.674780 -0.209002  1.000000  \n"
     ]
    }
   ],
   "source": [
    "\n",
    "glass_df = glass_df.drop(columns=['id'])  \n",
    "\n",
    "X = glass_df.drop(columns=['type']) \n",
    "y = glass_df['type']\n",
    "\n",
    "# Compute correlation matrix on original data\n",
    "correlation_matrix_original = glass_df.corr()\n",
    "print(\"Correlation Matrix on Original Data:\")\n",
    "print(correlation_matrix_original)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation Matrix on Unbalanced data\n",
      "            ri        na        mg        al        si         k        ca  \\\n",
      "ri    1.000000  0.056694  0.077057 -0.535435 -0.641326 -0.317770  0.682650   \n",
      "na    0.056694  1.000000 -0.515443  0.257375 -0.218881 -0.737161  0.041075   \n",
      "mg    0.077057 -0.515443  1.000000 -0.647897 -0.382386  0.520749 -0.437191   \n",
      "al   -0.535435  0.257375 -0.647897  1.000000  0.427754 -0.084501 -0.161135   \n",
      "si   -0.641326 -0.218881 -0.382386  0.427754  1.000000  0.136207 -0.243004   \n",
      "k    -0.317770 -0.737161  0.520749 -0.084501  0.136207  1.000000 -0.378233   \n",
      "ca    0.682650  0.041075 -0.437191 -0.161135 -0.243004 -0.378233  1.000000   \n",
      "ba   -0.166051  0.544451 -0.673522  0.509793  0.268762 -0.511066 -0.062972   \n",
      "fe    0.002661 -0.182187  0.180491 -0.151436 -0.058938  0.145233 -0.046220   \n",
      "type -0.151150  0.549827 -0.852899  0.655184  0.325234 -0.561308  0.237615   \n",
      "\n",
      "            ba        fe      type  \n",
      "ri   -0.166051  0.002661 -0.151150  \n",
      "na    0.544451 -0.182187  0.549827  \n",
      "mg   -0.673522  0.180491 -0.852899  \n",
      "al    0.509793 -0.151436  0.655184  \n",
      "si    0.268762 -0.058938  0.325234  \n",
      "k    -0.511066  0.145233 -0.561308  \n",
      "ca   -0.062972 -0.046220  0.237615  \n",
      "ba    1.000000 -0.119944  0.674780  \n",
      "fe   -0.119944  1.000000 -0.209002  \n",
      "type  0.674780 -0.209002  1.000000  \n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "# Compute correlation matrix on original data\n",
    "correlation_matrix_raw = glass_df.corr()\n",
    "print(\"Correlation Matrix on Unbalanced data\")\n",
    "print(correlation_matrix_raw)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a heatmap\n",
    "plt.figure(figsize=(10, 8))  # Set the size of the heatmap\n",
    "sns.heatmap(correlation_matrix_original, annot=True, cmap='coolwarm', fmt='.2f', linewidths=0.5)\n",
    "\n",
    "# Set the title for the heatmap\n",
    "plt.title('Correlation Heatmap')\n",
    "\n",
    "# Display the heatmap\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we try to balance the dataset with Data Augumentation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "X = glass_df.drop(columns=['type']) \n",
    "y = glass_df['type']\n",
    "\n",
    "# Apply SMOTE to balance the dataset\n",
    "smote = SMOTE()\n",
    "X_resampled, y_resampled = smote.fit_resample(X, y)\n",
    "glass_df_balanced = pd.DataFrame(X_resampled, columns=X.columns)\n",
    "glass_df_balanced['type'] = y_resampled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(12,12))\n",
    "type_counts = glass_df_balanced['type'].value_counts()\n",
    "type_counts.plot(kind='bar', color='skyblue')\n",
    "plt.title('Distribution of Glass Types')\n",
    "plt.xlabel('Type of Glass')\n",
    "plt.ylabel('Count')\n",
    "plt.xticks(rotation=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data augmentation worked. Now we recompute the correlation matrix to see the effect of data augmentation. We've an equal balanced dataset ready for training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlation_matrix = glass_df_balanced.corr()\n",
    "plt.figure(figsize=(10, 8))  # Set the size of the heatmap\n",
    "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt='.2f', linewidths=0.5)\n",
    "\n",
    "# Set the title for the heatmap\n",
    "plt.title('Correlation Heatmap (Balanced Data)')\n",
    "\n",
    "# Display the heatmap\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The effect of data augumentation has minimal impact on correlation since SMOTE interpolates between minority class samples, it maintains the relationships between features. We can think about splitting target and training dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(ri     1.51299\n",
       " na    11.03000\n",
       " mg     0.00000\n",
       " al     0.29000\n",
       " si    71.15000\n",
       " k      0.00000\n",
       " ca     7.08000\n",
       " ba     0.00000\n",
       " fe     0.00000\n",
       " dtype: float64,\n",
       " ri     1.52667\n",
       " na    15.15000\n",
       " mg     3.98000\n",
       " al     2.74000\n",
       " si    74.55000\n",
       " k      1.10000\n",
       " ca    12.50000\n",
       " ba     1.71000\n",
       " fe     0.37000\n",
       " dtype: float64)"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "# Split the data into training and testing sets\n",
    "X = glass_df_balanced.drop(columns=['type']) \n",
    "y = glass_df_balanced['type'] \n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "X_train.min(),X_train.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-3.112704852564166, 4.724666942770337)"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Feature scaling\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "X_train_scaled.min(),X_train_scaled.max()\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "X_train_scaled.min(),X_train_scaled.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(336, 9)"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_scaled.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_scaled.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the tensorflow random seed to make the results reproducible. The random weight will be the same everytime we run the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TensorFlow version: 2.12.0\n"
     ]
    }
   ],
   "source": [
    "# Import TensorFlow\n",
    "import tensorflow as tf\n",
    "from sklearn.metrics import classification_report, accuracy_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "from tensorflow.keras.wrappers.scikit_learn import KerasClassifier\n",
    "\n",
    "# Print TensorFlow version to verify the installation\n",
    "print(\"TensorFlow version:\", tf.__version__)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Creation\n",
    "Some consideration are put in place during model creation:\n",
    "- We need to individuate the classes. In our case 9, this means thet the final soft-max function should consider 9 neurons as the dimension of the input. SWe use the soft-max function at end because the classes are exclusives. If the classifier were a binary one or multi indipendent labels, we should use the sigmod function.\n",
    "- We use a Dense Neural Network where the number of internal layers, neurons, epochs, activation function can be used.\n",
    "### Early stopping\n",
    "Early stopping is  way to regularize iterative training. The algorithm tries different hyperparamers until it doens't reach a local maximum or the number\n",
    "of trials is exhausted. Typically in Hyperparametes tuning we have to decide the function objective for early stopping. In this case we prefer the loss to the accuracy. Why? The loss quantify how certain the model is about a prediction (basically having a value close to 1 in the right class and close to 0 in the other classes). The accuracy merely account for the number of correct predictions. Similarly, any metrics using hard predictions rather than probabilities have the same problem.\n",
    "### Optimization\n",
    "Adam (Adaptive Moment Estimation) is a optimization technique for training neural networks. on an average, it is the best optimizer .It works with momentums of first and second order. The intuition behind the Adam is that we don’t want to roll so fast just because we can jump over the minimum, we want to decrease the velocity a little bit for a careful search. Adam tends to converge faster, while SGD often converges to more optimal solutions. SGD’s high variance disadvantages gets rectified by Adam (as advantage for Adam). Adam tends to converge faster, while SGD often converges to more optimal solutions.\n",
    "We've tried adam, but it didn't found the right hyperparameters so we forced to sgd to have a better accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "The softmax function should be equal or major to the number of classes. That's 7.\n",
    "      -- 1 building_windows_float_processed\n",
    "      -- 2 building_windows_non_float_processed\n",
    "      -- 3 vehicle_windows_float_processed\n",
    "      -- 4 vehicle_windows_non_float_processed (none in this database)\n",
    "      -- 5 containers\n",
    "      -- 6 tableware\n",
    "      -- 7 headlamps\n",
    "\"\"\"\n",
    "NUMBER_OF_CLASSES=9\n",
    "def create_model(layers=1, neurons=32, epochs=50):\n",
    "    model = Sequential()\n",
    "    model.add(Dense(neurons, activation='relu', input_shape=(X_train_scaled.shape[1],)))\n",
    "    for _ in range(layers - 1):\n",
    "        model.add(Dense(neurons, activation='relu'))\n",
    "    # one per class\n",
    "    model.add(Dense(NUMBER_OF_CLASSES, activation='softmax'))\n",
    "    model.compile(optimizer='sgd',\n",
    "                  loss='sparse_categorical_crossentropy',\n",
    "                  metrics=['accuracy'])\n",
    "    return model\n",
    "# Define early stopping callback\n",
    "early_stopping = EarlyStopping(monitor='loss', patience=30, restore_best_weights=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 12 candidates, totalling 36 fits\n",
      "[CV] END ....................epochs=50, layers=1, neurons=64; total time=   1.6s\n",
      "[CV] END ....................epochs=50, layers=1, neurons=64; total time=   1.5s\n",
      "[CV] END ....................epochs=50, layers=1, neurons=64; total time=   1.5s\n",
      "[CV] END ...................epochs=50, layers=1, neurons=128; total time=   1.6s\n",
      "[CV] END ...................epochs=50, layers=1, neurons=128; total time=   1.5s\n",
      "[CV] END ...................epochs=50, layers=1, neurons=128; total time=   1.5s\n",
      "[CV] END ...................epochs=50, layers=1, neurons=256; total time=   1.6s\n",
      "[CV] END ...................epochs=50, layers=1, neurons=256; total time=   1.6s\n",
      "[CV] END ...................epochs=50, layers=1, neurons=256; total time=   1.5s\n",
      "[CV] END ....................epochs=50, layers=2, neurons=64; total time=   1.9s\n",
      "[CV] END ....................epochs=50, layers=2, neurons=64; total time=   1.8s\n",
      "[CV] END ....................epochs=50, layers=2, neurons=64; total time=   1.8s\n",
      "[CV] END ...................epochs=50, layers=2, neurons=128; total time=   2.0s\n",
      "[CV] END ...................epochs=50, layers=2, neurons=128; total time=   2.0s\n",
      "[CV] END ...................epochs=50, layers=2, neurons=128; total time=   1.9s\n",
      "[CV] END ...................epochs=50, layers=2, neurons=256; total time=   1.8s\n",
      "[CV] END ...................epochs=50, layers=2, neurons=256; total time=   2.4s\n",
      "[CV] END ...................epochs=50, layers=2, neurons=256; total time=   1.8s\n",
      "[CV] END ....................epochs=50, layers=3, neurons=64; total time=   2.0s\n",
      "[CV] END ....................epochs=50, layers=3, neurons=64; total time=   1.9s\n",
      "[CV] END ....................epochs=50, layers=3, neurons=64; total time=   2.0s\n",
      "[CV] END ...................epochs=50, layers=3, neurons=128; total time=   1.9s\n",
      "[CV] END ...................epochs=50, layers=3, neurons=128; total time=   2.0s\n",
      "[CV] END ...................epochs=50, layers=3, neurons=128; total time=   1.9s\n",
      "[CV] END ...................epochs=50, layers=3, neurons=256; total time=   2.1s\n",
      "[CV] END ...................epochs=50, layers=3, neurons=256; total time=   2.0s\n",
      "[CV] END ...................epochs=50, layers=3, neurons=256; total time=   2.0s\n",
      "[CV] END ....................epochs=50, layers=4, neurons=64; total time=   2.2s\n",
      "[CV] END ....................epochs=50, layers=4, neurons=64; total time=   2.6s\n",
      "[CV] END ....................epochs=50, layers=4, neurons=64; total time=   2.5s\n",
      "[CV] END ...................epochs=50, layers=4, neurons=128; total time=   2.6s\n",
      "[CV] END ...................epochs=50, layers=4, neurons=128; total time=   2.2s\n",
      "[CV] END ...................epochs=50, layers=4, neurons=128; total time=   2.3s\n",
      "[CV] END ...................epochs=50, layers=4, neurons=256; total time=   2.3s\n",
      "[CV] END ...................epochs=50, layers=4, neurons=256; total time=   2.3s\n",
      "[CV] END ...................epochs=50, layers=4, neurons=256; total time=   2.4s\n",
      "Best Parameters: {'epochs': 50, 'layers': 3, 'neurons': 256}\n",
      "Best Score: 0.7827381094296774\n"
     ]
    }
   ],
   "source": [
    "# Define the grid search parameters\n",
    "param_grid = {\n",
    "    'layers': [1, 2, 3, 4],  # Number of hidden layers\n",
    "    'neurons': [64, 128, 256],  # Number of neurons per layer\n",
    "    'epochs': [50]  # Number of epochs\n",
    "}\n",
    "\n",
    "# Create KerasClassifier\n",
    "model = KerasClassifier(build_fn=create_model, verbose=0, callbacks=[early_stopping])\n",
    "\n",
    "# Perform grid search with early stopping\n",
    "grid_search = GridSearchCV(estimator=model, param_grid=param_grid, cv=3, verbose=2)\n",
    "result = grid_search.fit(X_train_scaled, y_train)\n",
    "# Get the best parameters and best score\n",
    "best_params = grid_search.best_params_\n",
    "best_score = grid_search.best_score_\n",
    "\n",
    "print(\"Best Parameters:\", best_params)\n",
    "print(\"Best Score:\", best_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 0s 3ms/step\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.67      0.38      0.48        16\n",
      "           2       0.59      0.53      0.56        19\n",
      "           3       0.44      0.80      0.57        10\n",
      "           5       1.00      1.00      1.00        11\n",
      "           6       0.94      1.00      0.97        15\n",
      "           7       1.00      1.00      1.00        13\n",
      "\n",
      "    accuracy                           0.75        84\n",
      "   macro avg       0.77      0.78      0.76        84\n",
      "weighted avg       0.77      0.75      0.74        84\n",
      "\n",
      "Accuracy: 0.75\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the best model on the test set\n",
    "best_model_grid_cv = grid_search.best_estimator_\n",
    "y_pred = best_model_grid_cv.predict(X_test_scaled)\n",
    "\n",
    "# Print classification report and accuracy score\n",
    "print(\"Classification Report:\")\n",
    "print(classification_report(y_test, y_pred))\n",
    "\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy:\", accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_38\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_133 (Dense)           (None, 256)               2560      \n",
      "                                                                 \n",
      " dense_134 (Dense)           (None, 256)               65792     \n",
      "                                                                 \n",
      " dense_135 (Dense)           (None, 256)               65792     \n",
      "                                                                 \n",
      " dense_136 (Dense)           (None, 9)                 2313      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 136,457\n",
      "Trainable params: 136,457\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = create_model(layers=3, neurons=256, epochs=50)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "9/9 [==============================] - 1s 21ms/step - loss: 2.2687 - accuracy: 0.0112 - val_loss: 2.2066 - val_accuracy: 0.0588\n",
      "Epoch 2/50\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 2.1866 - accuracy: 0.1828 - val_loss: 2.1285 - val_accuracy: 0.2941\n",
      "Epoch 3/50\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 2.1088 - accuracy: 0.4328 - val_loss: 2.0578 - val_accuracy: 0.4706\n",
      "Epoch 4/50\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 2.0372 - accuracy: 0.5075 - val_loss: 1.9880 - val_accuracy: 0.5147\n",
      "Epoch 5/50\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 1.9665 - accuracy: 0.5672 - val_loss: 1.9219 - val_accuracy: 0.5588\n",
      "Epoch 6/50\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 1.8987 - accuracy: 0.6157 - val_loss: 1.8567 - val_accuracy: 0.6176\n",
      "Epoch 7/50\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 1.8318 - accuracy: 0.6306 - val_loss: 1.7907 - val_accuracy: 0.6029\n",
      "Epoch 8/50\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 1.7645 - accuracy: 0.6493 - val_loss: 1.7250 - val_accuracy: 0.6324\n",
      "Epoch 9/50\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 1.6962 - accuracy: 0.6903 - val_loss: 1.6599 - val_accuracy: 0.6765\n",
      "Epoch 10/50\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 1.6310 - accuracy: 0.7164 - val_loss: 1.5957 - val_accuracy: 0.6765\n",
      "Epoch 11/50\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 1.5655 - accuracy: 0.7463 - val_loss: 1.5309 - val_accuracy: 0.6912\n",
      "Epoch 12/50\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 1.5011 - accuracy: 0.7537 - val_loss: 1.4687 - val_accuracy: 0.6618\n",
      "Epoch 13/50\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 1.4379 - accuracy: 0.7575 - val_loss: 1.4107 - val_accuracy: 0.6618\n",
      "Epoch 14/50\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 1.3775 - accuracy: 0.7724 - val_loss: 1.3530 - val_accuracy: 0.6618\n",
      "Epoch 15/50\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 1.3190 - accuracy: 0.7799 - val_loss: 1.2974 - val_accuracy: 0.6765\n",
      "Epoch 16/50\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 1.2627 - accuracy: 0.7724 - val_loss: 1.2469 - val_accuracy: 0.7059\n",
      "Epoch 17/50\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 1.2093 - accuracy: 0.7649 - val_loss: 1.1948 - val_accuracy: 0.6912\n",
      "Epoch 18/50\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 1.1579 - accuracy: 0.7799 - val_loss: 1.1473 - val_accuracy: 0.6912\n",
      "Epoch 19/50\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 1.1106 - accuracy: 0.7836 - val_loss: 1.1024 - val_accuracy: 0.6618\n",
      "Epoch 20/50\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 1.0655 - accuracy: 0.7873 - val_loss: 1.0595 - val_accuracy: 0.6471\n",
      "Epoch 21/50\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 1.0231 - accuracy: 0.7687 - val_loss: 1.0156 - val_accuracy: 0.6618\n",
      "Epoch 22/50\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 0.9826 - accuracy: 0.7799 - val_loss: 0.9765 - val_accuracy: 0.6765\n",
      "Epoch 23/50\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 0.9457 - accuracy: 0.7873 - val_loss: 0.9408 - val_accuracy: 0.6618\n",
      "Epoch 24/50\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 0.9114 - accuracy: 0.7836 - val_loss: 0.9040 - val_accuracy: 0.6765\n",
      "Epoch 25/50\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 0.8791 - accuracy: 0.7873 - val_loss: 0.8706 - val_accuracy: 0.6912\n",
      "Epoch 26/50\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 0.8500 - accuracy: 0.7873 - val_loss: 0.8404 - val_accuracy: 0.6912\n",
      "Epoch 27/50\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 0.8222 - accuracy: 0.7836 - val_loss: 0.8070 - val_accuracy: 0.7353\n",
      "Epoch 28/50\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 0.7967 - accuracy: 0.7985 - val_loss: 0.7858 - val_accuracy: 0.7353\n",
      "Epoch 29/50\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 0.7754 - accuracy: 0.7873 - val_loss: 0.7541 - val_accuracy: 0.7353\n",
      "Epoch 30/50\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 0.7527 - accuracy: 0.8022 - val_loss: 0.7333 - val_accuracy: 0.7353\n",
      "Epoch 31/50\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 0.7331 - accuracy: 0.7985 - val_loss: 0.7105 - val_accuracy: 0.7353\n",
      "Epoch 32/50\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 0.7159 - accuracy: 0.8134 - val_loss: 0.6894 - val_accuracy: 0.7500\n",
      "Epoch 33/50\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 0.6987 - accuracy: 0.8060 - val_loss: 0.6715 - val_accuracy: 0.7500\n",
      "Epoch 34/50\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 0.6839 - accuracy: 0.8060 - val_loss: 0.6542 - val_accuracy: 0.7500\n",
      "Epoch 35/50\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 0.6701 - accuracy: 0.8097 - val_loss: 0.6383 - val_accuracy: 0.7500\n",
      "Epoch 36/50\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 0.6582 - accuracy: 0.8134 - val_loss: 0.6244 - val_accuracy: 0.7500\n",
      "Epoch 37/50\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 0.6458 - accuracy: 0.8284 - val_loss: 0.6174 - val_accuracy: 0.7500\n",
      "Epoch 38/50\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 0.6349 - accuracy: 0.8060 - val_loss: 0.5973 - val_accuracy: 0.7794\n",
      "Epoch 39/50\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 0.6242 - accuracy: 0.8209 - val_loss: 0.5862 - val_accuracy: 0.7794\n",
      "Epoch 40/50\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 0.6153 - accuracy: 0.8321 - val_loss: 0.5810 - val_accuracy: 0.7794\n",
      "Epoch 41/50\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 0.6060 - accuracy: 0.8246 - val_loss: 0.5756 - val_accuracy: 0.7500\n",
      "Epoch 42/50\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 0.5981 - accuracy: 0.8246 - val_loss: 0.5667 - val_accuracy: 0.7500\n",
      "Epoch 43/50\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 0.5918 - accuracy: 0.8172 - val_loss: 0.5549 - val_accuracy: 0.7500\n",
      "Epoch 44/50\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 0.5829 - accuracy: 0.8358 - val_loss: 0.5480 - val_accuracy: 0.7500\n",
      "Epoch 45/50\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 0.5774 - accuracy: 0.8246 - val_loss: 0.5497 - val_accuracy: 0.7500\n",
      "Epoch 46/50\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 0.5712 - accuracy: 0.8172 - val_loss: 0.5443 - val_accuracy: 0.7500\n",
      "Epoch 47/50\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 0.5662 - accuracy: 0.8209 - val_loss: 0.5289 - val_accuracy: 0.7500\n",
      "Epoch 48/50\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 0.5585 - accuracy: 0.8396 - val_loss: 0.5303 - val_accuracy: 0.7500\n",
      "Epoch 49/50\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 0.5534 - accuracy: 0.8172 - val_loss: 0.5234 - val_accuracy: 0.7500\n",
      "Epoch 50/50\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 0.5491 - accuracy: 0.8246 - val_loss: 0.5096 - val_accuracy: 0.7500\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train_scaled, y_train, epochs=50, validation_split=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(history.history).plot(figsize=(8,5), xlim=[0,49], ylim=[0,1], grid=True, xlabel='Epoch', style=['r--','r--.','b-','b-*'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see that both training accurancy and validation accuracy increase during the traning while the traning loss and validation loss decrease. This good. The validation lines are at most the same but at the end we've some overfitting at the end appears. The accuracy with this hyperparmeters is always higher than the validation accuracy, this also good. But we're not satisifed because we want both function closer and distinct.. Let's try to do a further exploration, if we fail we use kera-tuners and said in one of recommended books."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 0s 7ms/step - loss: 0.6136 - accuracy: 0.7381\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.6135997772216797, 0.738095223903656]"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test_scaled, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yep. It is normal having a lower accuracy in the test set because the accuracy is trained on the validation set (2% of training set), but it is not acceptable to have less then 80 percent of accuracy for a classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 0s 2ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0.  , 0.26, 0.28, 0.43, 0.  , 0.01, 0.01, 0.01, 0.  ],\n",
       "       [0.01, 0.06, 0.03, 0.05, 0.01, 0.04, 0.73, 0.04, 0.01],\n",
       "       [0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.01, 0.99, 0.  ],\n",
       "       [0.  , 0.01, 0.01, 0.01, 0.  , 0.09, 0.77, 0.09, 0.  ],\n",
       "       [0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.02, 0.97, 0.  ],\n",
       "       [0.  , 0.19, 0.42, 0.35, 0.  , 0.01, 0.01, 0.01, 0.01],\n",
       "       [0.  , 0.28, 0.37, 0.34, 0.  , 0.  , 0.  , 0.  , 0.  ],\n",
       "       [0.01, 0.27, 0.15, 0.39, 0.01, 0.02, 0.11, 0.03, 0.01],\n",
       "       [0.  , 0.3 , 0.41, 0.27, 0.  , 0.01, 0.  , 0.  , 0.  ],\n",
       "       [0.  , 0.22, 0.45, 0.3 , 0.  , 0.01, 0.01, 0.01, 0.  ],\n",
       "       [0.  , 0.14, 0.29, 0.5 , 0.  , 0.  , 0.04, 0.02, 0.  ],\n",
       "       [0.  , 0.3 , 0.42, 0.23, 0.  , 0.02, 0.01, 0.01, 0.01],\n",
       "       [0.  , 0.01, 0.01, 0.01, 0.  , 0.01, 0.83, 0.13, 0.  ],\n",
       "       [0.  , 0.25, 0.34, 0.4 , 0.  , 0.  , 0.  , 0.  , 0.  ],\n",
       "       [0.01, 0.03, 0.02, 0.02, 0.01, 0.02, 0.84, 0.05, 0.01],\n",
       "       [0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.01, 0.99, 0.  ],\n",
       "       [0.  , 0.26, 0.38, 0.34, 0.  , 0.01, 0.  , 0.  , 0.  ],\n",
       "       [0.  , 0.31, 0.3 , 0.36, 0.  , 0.01, 0.  , 0.  , 0.  ],\n",
       "       [0.  , 0.23, 0.26, 0.46, 0.01, 0.01, 0.01, 0.01, 0.01],\n",
       "       [0.  , 0.26, 0.21, 0.52, 0.  , 0.  , 0.01, 0.  , 0.  ],\n",
       "       [0.  , 0.24, 0.35, 0.39, 0.  , 0.01, 0.  , 0.  , 0.  ],\n",
       "       [0.  , 0.3 , 0.13, 0.57, 0.  , 0.  , 0.  , 0.  , 0.  ],\n",
       "       [0.01, 0.05, 0.02, 0.03, 0.01, 0.02, 0.81, 0.04, 0.01],\n",
       "       [0.  , 0.25, 0.25, 0.49, 0.  , 0.  , 0.01, 0.  , 0.  ],\n",
       "       [0.  , 0.3 , 0.54, 0.12, 0.  , 0.02, 0.  , 0.  , 0.  ],\n",
       "       [0.01, 0.02, 0.02, 0.02, 0.01, 0.01, 0.81, 0.09, 0.01],\n",
       "       [0.  , 0.16, 0.61, 0.23, 0.  , 0.  , 0.  , 0.  , 0.  ],\n",
       "       [0.  , 0.01, 0.01, 0.  , 0.  , 0.98, 0.  , 0.  , 0.  ],\n",
       "       [0.  , 0.  , 0.  , 0.  , 0.  , 0.98, 0.01, 0.  , 0.  ],\n",
       "       [0.  , 0.17, 0.52, 0.27, 0.  , 0.01, 0.01, 0.01, 0.01],\n",
       "       [0.01, 0.03, 0.02, 0.02, 0.01, 0.02, 0.79, 0.1 , 0.01],\n",
       "       [0.  , 0.55, 0.22, 0.15, 0.  , 0.04, 0.01, 0.01, 0.01],\n",
       "       [0.  , 0.47, 0.07, 0.35, 0.  , 0.01, 0.08, 0.01, 0.  ],\n",
       "       [0.  , 0.17, 0.42, 0.37, 0.  , 0.01, 0.01, 0.01, 0.01],\n",
       "       [0.01, 0.03, 0.02, 0.02, 0.01, 0.02, 0.86, 0.03, 0.01],\n",
       "       [0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.05, 0.94, 0.  ],\n",
       "       [0.  , 0.19, 0.27, 0.51, 0.  , 0.01, 0.01, 0.01, 0.01],\n",
       "       [0.  , 0.57, 0.07, 0.32, 0.  , 0.01, 0.03, 0.  , 0.  ],\n",
       "       [0.01, 0.02, 0.01, 0.01, 0.  , 0.14, 0.71, 0.1 , 0.  ],\n",
       "       [0.  , 0.01, 0.01, 0.  , 0.  , 0.95, 0.03, 0.01, 0.  ],\n",
       "       [0.  , 0.23, 0.45, 0.29, 0.  , 0.01, 0.01, 0.01, 0.  ],\n",
       "       [0.  , 0.16, 0.36, 0.46, 0.  , 0.  , 0.  , 0.  , 0.  ],\n",
       "       [0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.03, 0.97, 0.  ],\n",
       "       [0.  , 0.37, 0.38, 0.2 , 0.  , 0.03, 0.01, 0.01, 0.01],\n",
       "       [0.  , 0.19, 0.54, 0.26, 0.  , 0.  , 0.  , 0.  , 0.  ],\n",
       "       [0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.01, 0.99, 0.  ],\n",
       "       [0.  , 0.51, 0.07, 0.36, 0.  , 0.01, 0.03, 0.01, 0.  ],\n",
       "       [0.  , 0.  , 0.  , 0.  , 0.  , 0.99, 0.  , 0.  , 0.  ],\n",
       "       [0.01, 0.03, 0.02, 0.02, 0.01, 0.02, 0.83, 0.05, 0.01],\n",
       "       [0.  , 0.42, 0.29, 0.23, 0.  , 0.02, 0.01, 0.01, 0.01],\n",
       "       [0.  , 0.2 , 0.32, 0.45, 0.  , 0.01, 0.01, 0.01, 0.  ],\n",
       "       [0.  , 0.02, 0.01, 0.01, 0.  , 0.01, 0.9 , 0.04, 0.  ],\n",
       "       [0.  , 0.47, 0.11, 0.37, 0.  , 0.01, 0.03, 0.01, 0.  ],\n",
       "       [0.  , 0.2 , 0.37, 0.38, 0.  , 0.01, 0.01, 0.01, 0.01],\n",
       "       [0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.01, 0.99, 0.  ],\n",
       "       [0.  , 0.01, 0.01, 0.  , 0.  , 0.95, 0.01, 0.01, 0.  ],\n",
       "       [0.  , 0.  , 0.  , 0.  , 0.  , 0.98, 0.01, 0.  , 0.  ],\n",
       "       [0.  , 0.01, 0.01, 0.  , 0.  , 0.93, 0.03, 0.01, 0.  ],\n",
       "       [0.  , 0.21, 0.39, 0.38, 0.  , 0.01, 0.01, 0.01, 0.  ],\n",
       "       [0.  , 0.23, 0.32, 0.42, 0.  , 0.01, 0.  , 0.  , 0.  ],\n",
       "       [0.01, 0.05, 0.03, 0.04, 0.01, 0.03, 0.79, 0.04, 0.01],\n",
       "       [0.01, 0.06, 0.03, 0.02, 0.01, 0.31, 0.52, 0.04, 0.01],\n",
       "       [0.  , 0.38, 0.29, 0.29, 0.  , 0.01, 0.01, 0.01, 0.  ],\n",
       "       [0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.01, 0.99, 0.  ],\n",
       "       [0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.03, 0.96, 0.  ],\n",
       "       [0.  , 0.01, 0.01, 0.01, 0.  , 0.02, 0.89, 0.06, 0.  ],\n",
       "       [0.  , 0.01, 0.01, 0.  , 0.  , 0.92, 0.04, 0.01, 0.  ],\n",
       "       [0.  , 0.31, 0.29, 0.35, 0.01, 0.02, 0.01, 0.01, 0.01],\n",
       "       [0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.03, 0.97, 0.  ],\n",
       "       [0.01, 0.03, 0.03, 0.03, 0.01, 0.02, 0.79, 0.07, 0.01],\n",
       "       [0.  , 0.14, 0.25, 0.61, 0.  , 0.  , 0.  , 0.  , 0.  ],\n",
       "       [0.01, 0.03, 0.02, 0.02, 0.01, 0.02, 0.83, 0.04, 0.01],\n",
       "       [0.  , 0.01, 0.01, 0.  , 0.  , 0.95, 0.02, 0.01, 0.  ],\n",
       "       [0.  , 0.01, 0.01, 0.  , 0.  , 0.97, 0.  , 0.  , 0.  ],\n",
       "       [0.  , 0.33, 0.28, 0.35, 0.  , 0.01, 0.01, 0.01, 0.01],\n",
       "       [0.  , 0.3 , 0.35, 0.33, 0.  , 0.01, 0.  , 0.  , 0.  ],\n",
       "       [0.  , 0.01, 0.01, 0.  , 0.  , 0.96, 0.01, 0.  , 0.  ],\n",
       "       [0.01, 0.23, 0.3 , 0.35, 0.01, 0.02, 0.04, 0.03, 0.01],\n",
       "       [0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.05, 0.95, 0.  ],\n",
       "       [0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.01, 0.99, 0.  ],\n",
       "       [0.  , 0.4 , 0.29, 0.27, 0.  , 0.02, 0.01, 0.01, 0.01],\n",
       "       [0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.06, 0.94, 0.  ],\n",
       "       [0.  , 0.29, 0.24, 0.42, 0.  , 0.01, 0.01, 0.01, 0.01],\n",
       "       [0.  , 0.5 , 0.08, 0.36, 0.  , 0.  , 0.04, 0.01, 0.  ]],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_sample = X_test_scaled \n",
    "class_probability = model.predict(X_test_sample)\n",
    "class_probability.round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try for a moment to normalize the input and see if we can have better accuracy, also we can try to use kera-tuner in the process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/bin/bash: /home/jozoppi/pyconda/lib/libtinfo.so.6: no version information available (required by /bin/bash)\n",
      "Requirement already satisfied: keras-tuner in /home/jozoppi/pyconda/lib/python3.11/site-packages (1.4.7)\n",
      "Requirement already satisfied: keras in /home/jozoppi/pyconda/lib/python3.11/site-packages (from keras-tuner) (2.12.0)\n",
      "Requirement already satisfied: packaging in /home/jozoppi/pyconda/lib/python3.11/site-packages (from keras-tuner) (23.1)\n",
      "Requirement already satisfied: requests in /home/jozoppi/pyconda/lib/python3.11/site-packages (from keras-tuner) (2.31.0)\n",
      "Requirement already satisfied: kt-legacy in /home/jozoppi/pyconda/lib/python3.11/site-packages (from keras-tuner) (1.0.5)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /home/jozoppi/pyconda/lib/python3.11/site-packages (from requests->keras-tuner) (2.0.4)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /home/jozoppi/pyconda/lib/python3.11/site-packages (from requests->keras-tuner) (3.4)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /home/jozoppi/pyconda/lib/python3.11/site-packages (from requests->keras-tuner) (1.26.18)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /home/jozoppi/pyconda/lib/python3.11/site-packages (from requests->keras-tuner) (2024.2.2)\n"
     ]
    }
   ],
   "source": [
    "!pip install keras-tuner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras_tuner as kt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_model_tuner(hp):\n",
    "    model = Sequential()\n",
    "    learning_rate = hp.Float(\"learning_rate\", min_value=1e-4, max_value=1e-2,sampling='log')\n",
    "    model.add(tf.keras.layers.Input(shape=(X_train_scaled.shape[1],)))  # Adjust the input shape as necessary\n",
    "    for i in range(hp.Int('num_layers', 1, 5)):\n",
    "        # Tune the number of units in each layer\n",
    "        model.add(tf.keras.layers.Dense(\n",
    "            units=hp.Int(f'units_{i}', min_value=32, max_value=256, step=32),\n",
    "            activation='relu')\n",
    "        )\n",
    "    model.add(tf.keras.layers.Dense(9, activation='softmax'))  # Adjust the output layer as necessary\n",
    "    optimizer = hp.Choice(\"optimizer\", values=['sgd', 'adam'])\n",
    "    optimizer_method = None\n",
    "    if optimizer != \"sgd\":\n",
    "        optimizer_method = tf.keras.optimizers.Adam(learning_rate=learning_rate)\n",
    "    else:\n",
    "        optimizer_method = tf.keras.optimizers.SGD(learning_rate=learning_rate)\n",
    "    model.compile(\n",
    "        optimizer=optimizer_method,\n",
    "        loss='sparse_categorical_crossentropy',\n",
    "        metrics=['accuracy']\n",
    "    )    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we perform a random search passing build_model function to constructor.We don't fix the optimizer and increase the number of layers. We decide to use RandomSearch create the model and look for hyperparameters and increase the number of trials to 12. In this case we don't use Early Stopping it will take around 3 minutes in a machine with GeForce RTX."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trial 12 Complete [00h 00m 16s]\n",
      "val_accuracy: 0.9558823506037394\n",
      "\n",
      "Best val_accuracy So Far: 0.9558823704719543\n",
      "Total elapsed time: 00h 03m 26s\n"
     ]
    }
   ],
   "source": [
    "tuner = kt.RandomSearch(\n",
    "    build_model_tuner,\n",
    "    objective='val_accuracy',\n",
    "    max_trials=12,\n",
    "    executions_per_trial=3,\n",
    "    overwrite=True,\n",
    "    directory='hps_ca2',\n",
    "    project_name='hdip',\n",
    "    seed=42\n",
    ")\n",
    "tuner.search(X_train_scaled, y_train, epochs=50,  validation_split = 0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perfect we have around 90 per cent of accuracy on the validation data. Far better than before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Detecting that an object or model or tf.train.Checkpoint is being deleted with unrestored values. See the following logs for the specific values in question. To silence these warnings, use `status.expect_partial()`. See https://www.tensorflow.org/api_docs/python/tf/train/Checkpoint#restorefor details about the status object returned by the restore function.\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.1\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.2\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.3\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.4\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.5\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.6\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.7\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.8\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.9\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.10\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.11\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.12\n",
      "WARNING:tensorflow:Detecting that an object or model or tf.train.Checkpoint is being deleted with unrestored values. See the following logs for the specific values in question. To silence these warnings, use `status.expect_partial()`. See https://www.tensorflow.org/api_docs/python/tf/train/Checkpoint#restorefor details about the status object returned by the restore function.\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.1\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.2\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.3\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.4\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.5\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.6\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.7\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.8\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.9\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.10\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.11\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.12\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.13\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.14\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.15\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.16\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.17\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.18\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.19\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.20\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.21\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.22\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.23\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.24\n",
      "WARNING:tensorflow:Detecting that an object or model or tf.train.Checkpoint is being deleted with unrestored values. See the following logs for the specific values in question. To silence these warnings, use `status.expect_partial()`. See https://www.tensorflow.org/api_docs/python/tf/train/Checkpoint#restorefor details about the status object returned by the restore function.\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.1\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.2\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.3\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.4\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.5\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.6\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.7\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.8\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.9\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.10\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.11\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.12\n"
     ]
    }
   ],
   "source": [
    "top3_models = tuner.get_best_models(num_models=3)\n",
    "best_model = top3_models[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'learning_rate': 0.0009887195863122636, 'num_layers': 2, 'units_0': 256, 'optimizer': 'adam', 'units_1': 256}\n",
      "{'learning_rate': 0.0043925333029596605, 'num_layers': 2, 'units_0': 192, 'optimizer': 'adam', 'units_1': 160, 'units_2': 32, 'units_3': 160, 'units_4': 32}\n",
      "{'learning_rate': 0.0006951671294413008, 'num_layers': 5, 'units_0': 160, 'optimizer': 'adam', 'units_1': 96, 'units_2': 64, 'units_3': 96, 'units_4': 128}\n"
     ]
    }
   ],
   "source": [
    "best_hps = tuner.get_best_hyperparameters(num_trials=3)\n",
    "for hp in best_hps:\n",
    "    print(hp.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense (Dense)               (None, 256)               2560      \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 256)               65792     \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 9)                 2313      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 70,665\n",
      "Trainable params: 70,665\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "best_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The summarry give use the structure:\n",
    "- 9 neurons input the data\n",
    "- 192 neurons to dense\n",
    "- 192 neuron dense\n",
    "- 9 neurons with the function softmax."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "9/9 [==============================] - 0s 22ms/step - loss: 0.5418 - accuracy: 0.8470 - val_loss: 0.5122 - val_accuracy: 0.7500\n",
      "Epoch 2/50\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 0.5386 - accuracy: 0.8284 - val_loss: 0.5019 - val_accuracy: 0.7500\n",
      "Epoch 3/50\n",
      "9/9 [==============================] - 0s 12ms/step - loss: 0.5336 - accuracy: 0.8321 - val_loss: 0.5025 - val_accuracy: 0.7647\n",
      "Epoch 4/50\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 0.5278 - accuracy: 0.8284 - val_loss: 0.4958 - val_accuracy: 0.7647\n",
      "Epoch 5/50\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 0.5237 - accuracy: 0.8321 - val_loss: 0.4948 - val_accuracy: 0.7647\n",
      "Epoch 6/50\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 0.5194 - accuracy: 0.8246 - val_loss: 0.4966 - val_accuracy: 0.7647\n",
      "Epoch 7/50\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 0.5150 - accuracy: 0.8284 - val_loss: 0.4846 - val_accuracy: 0.7647\n",
      "Epoch 8/50\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 0.5119 - accuracy: 0.8284 - val_loss: 0.4810 - val_accuracy: 0.7647\n",
      "Epoch 9/50\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 0.5058 - accuracy: 0.8321 - val_loss: 0.4711 - val_accuracy: 0.7794\n",
      "Epoch 10/50\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 0.5012 - accuracy: 0.8358 - val_loss: 0.4724 - val_accuracy: 0.7647\n",
      "Epoch 11/50\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 0.4975 - accuracy: 0.8358 - val_loss: 0.4618 - val_accuracy: 0.7941\n",
      "Epoch 12/50\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 0.4938 - accuracy: 0.8507 - val_loss: 0.4623 - val_accuracy: 0.7794\n",
      "Epoch 13/50\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 0.4917 - accuracy: 0.8433 - val_loss: 0.4602 - val_accuracy: 0.7794\n",
      "Epoch 14/50\n",
      "9/9 [==============================] - 0s 12ms/step - loss: 0.4869 - accuracy: 0.8396 - val_loss: 0.4541 - val_accuracy: 0.7941\n",
      "Epoch 15/50\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 0.4839 - accuracy: 0.8396 - val_loss: 0.4528 - val_accuracy: 0.7794\n",
      "Epoch 16/50\n",
      "9/9 [==============================] - 0s 13ms/step - loss: 0.4793 - accuracy: 0.8433 - val_loss: 0.4609 - val_accuracy: 0.7647\n",
      "Epoch 17/50\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 0.4772 - accuracy: 0.8321 - val_loss: 0.4456 - val_accuracy: 0.7941\n",
      "Epoch 18/50\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 0.4724 - accuracy: 0.8507 - val_loss: 0.4426 - val_accuracy: 0.7941\n",
      "Epoch 19/50\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 0.4698 - accuracy: 0.8507 - val_loss: 0.4463 - val_accuracy: 0.7794\n",
      "Epoch 20/50\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 0.4664 - accuracy: 0.8396 - val_loss: 0.4404 - val_accuracy: 0.7794\n",
      "Epoch 21/50\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 0.4633 - accuracy: 0.8396 - val_loss: 0.4339 - val_accuracy: 0.7941\n",
      "Epoch 22/50\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 0.4591 - accuracy: 0.8433 - val_loss: 0.4347 - val_accuracy: 0.7794\n",
      "Epoch 23/50\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 0.4564 - accuracy: 0.8507 - val_loss: 0.4350 - val_accuracy: 0.7794\n",
      "Epoch 24/50\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 0.4530 - accuracy: 0.8396 - val_loss: 0.4275 - val_accuracy: 0.8088\n",
      "Epoch 25/50\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 0.4491 - accuracy: 0.8545 - val_loss: 0.4277 - val_accuracy: 0.8088\n",
      "Epoch 26/50\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 0.4476 - accuracy: 0.8582 - val_loss: 0.4314 - val_accuracy: 0.7941\n",
      "Epoch 27/50\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 0.4442 - accuracy: 0.8433 - val_loss: 0.4183 - val_accuracy: 0.8088\n",
      "Epoch 28/50\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 0.4406 - accuracy: 0.8507 - val_loss: 0.4297 - val_accuracy: 0.7941\n",
      "Epoch 29/50\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 0.4402 - accuracy: 0.8545 - val_loss: 0.4166 - val_accuracy: 0.8088\n",
      "Epoch 30/50\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 0.4356 - accuracy: 0.8619 - val_loss: 0.4161 - val_accuracy: 0.8088\n",
      "Epoch 31/50\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 0.4323 - accuracy: 0.8582 - val_loss: 0.4127 - val_accuracy: 0.8088\n",
      "Epoch 32/50\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 0.4299 - accuracy: 0.8545 - val_loss: 0.4081 - val_accuracy: 0.8088\n",
      "Epoch 33/50\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 0.4249 - accuracy: 0.8507 - val_loss: 0.4055 - val_accuracy: 0.8088\n",
      "Epoch 34/50\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 0.4230 - accuracy: 0.8657 - val_loss: 0.4067 - val_accuracy: 0.8088\n",
      "Epoch 35/50\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 0.4198 - accuracy: 0.8582 - val_loss: 0.4005 - val_accuracy: 0.8088\n",
      "Epoch 36/50\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 0.4189 - accuracy: 0.8619 - val_loss: 0.4025 - val_accuracy: 0.8088\n",
      "Epoch 37/50\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 0.4150 - accuracy: 0.8582 - val_loss: 0.4116 - val_accuracy: 0.7941\n",
      "Epoch 38/50\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 0.4121 - accuracy: 0.8619 - val_loss: 0.3917 - val_accuracy: 0.8235\n",
      "Epoch 39/50\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 0.4090 - accuracy: 0.8657 - val_loss: 0.3915 - val_accuracy: 0.8088\n",
      "Epoch 40/50\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 0.4062 - accuracy: 0.8545 - val_loss: 0.3978 - val_accuracy: 0.8088\n",
      "Epoch 41/50\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 0.4027 - accuracy: 0.8582 - val_loss: 0.4025 - val_accuracy: 0.7941\n",
      "Epoch 42/50\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 0.4010 - accuracy: 0.8507 - val_loss: 0.3969 - val_accuracy: 0.8088\n",
      "Epoch 43/50\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 0.3996 - accuracy: 0.8657 - val_loss: 0.3867 - val_accuracy: 0.8088\n",
      "Epoch 44/50\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 0.3943 - accuracy: 0.8657 - val_loss: 0.3906 - val_accuracy: 0.8235\n",
      "Epoch 45/50\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 0.3938 - accuracy: 0.8694 - val_loss: 0.4006 - val_accuracy: 0.8088\n",
      "Epoch 46/50\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 0.3918 - accuracy: 0.8619 - val_loss: 0.3966 - val_accuracy: 0.8088\n",
      "Epoch 47/50\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 0.3897 - accuracy: 0.8619 - val_loss: 0.3833 - val_accuracy: 0.8382\n",
      "Epoch 48/50\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 0.3844 - accuracy: 0.8657 - val_loss: 0.3966 - val_accuracy: 0.8088\n",
      "Epoch 49/50\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 0.3836 - accuracy: 0.8731 - val_loss: 0.3914 - val_accuracy: 0.8235\n",
      "Epoch 50/50\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 0.3824 - accuracy: 0.8657 - val_loss: 0.3739 - val_accuracy: 0.8235\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train_scaled, y_train, epochs=50, validation_split=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(history.history).plot(figsize=(8,5), xlim=[0,49], ylim=[0,1], grid=True, xlabel='Epoch', style=['r--','r--.','b-','b-*'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Excellent. The loss functions goe together with no variation so no overfitting or underfitting. Ad the accuracy is increasing but \n",
    "with some variations. This graph is far better than before."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion.\n",
    "We've cleaned the data using various data exploration techniques. The dataset was not ablancedWe've crafted a neural network that is able to classify the type of glass with accuracy around 0.95 using kera-tuner. We're able to reach a good result to our classifier and predict the probability of each class in effective way."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}