Neural Networks.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "wine = pd.read_csv('../Downloads/wine_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cultivator</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>Malic_Acid</th>\n",
       "      <th>Ash</th>\n",
       "      <th>Alcalinity of ash</th>\n",
       "      <th>Magnesium</th>\n",
       "      <th>Total_Phenols</th>\n",
       "      <th>Flavanoids</th>\n",
       "      <th>Nonflavanoid_Phenols</th>\n",
       "      <th>Proanthocyanins</th>\n",
       "      <th>Color_Intensity</th>\n",
       "      <th>Hue</th>\n",
       "      <th>OD280/OD315 of diluted wines</th>\n",
       "      <th>Proline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.64</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Cultivator  Alcohol  Malic_Acid   Ash  Alcalinity of ash  Magnesium  \\\n",
       "0           1    14.23        1.71  2.43               15.6        127   \n",
       "1           1    13.20        1.78  2.14               11.2        100   \n",
       "2           1    13.16        2.36  2.67               18.6        101   \n",
       "3           1    14.37        1.95  2.50               16.8        113   \n",
       "4           1    13.24        2.59  2.87               21.0        118   \n",
       "\n",
       "   Total_Phenols  Flavanoids  Nonflavanoid_Phenols  Proanthocyanins  \\\n",
       "0           2.80        3.06                  0.28             2.29   \n",
       "1           2.65        2.76                  0.26             1.28   \n",
       "2           2.80        3.24                  0.30             2.81   \n",
       "3           3.85        3.49                  0.24             2.18   \n",
       "4           2.80        2.69                  0.39             1.82   \n",
       "\n",
       "   Color_Intensity   Hue  OD280/OD315 of diluted wines  Proline  \n",
       "0             5.64  1.04                          3.92     1065  \n",
       "1             4.38  1.05                          3.40     1050  \n",
       "2             5.68  1.03                          3.17     1185  \n",
       "3             7.80  0.86                          3.45     1480  \n",
       "4             4.32  1.04                          2.93      735  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cultivator</th>\n",
       "      <td>178.0</td>\n",
       "      <td>1.938202</td>\n",
       "      <td>0.775035</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alcohol</th>\n",
       "      <td>178.0</td>\n",
       "      <td>13.000618</td>\n",
       "      <td>0.811827</td>\n",
       "      <td>11.03</td>\n",
       "      <td>12.3625</td>\n",
       "      <td>13.050</td>\n",
       "      <td>13.6775</td>\n",
       "      <td>14.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malic_Acid</th>\n",
       "      <td>178.0</td>\n",
       "      <td>2.336348</td>\n",
       "      <td>1.117146</td>\n",
       "      <td>0.74</td>\n",
       "      <td>1.6025</td>\n",
       "      <td>1.865</td>\n",
       "      <td>3.0825</td>\n",
       "      <td>5.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ash</th>\n",
       "      <td>178.0</td>\n",
       "      <td>2.366517</td>\n",
       "      <td>0.274344</td>\n",
       "      <td>1.36</td>\n",
       "      <td>2.2100</td>\n",
       "      <td>2.360</td>\n",
       "      <td>2.5575</td>\n",
       "      <td>3.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alcalinity of ash</th>\n",
       "      <td>178.0</td>\n",
       "      <td>19.494944</td>\n",
       "      <td>3.339564</td>\n",
       "      <td>10.60</td>\n",
       "      <td>17.2000</td>\n",
       "      <td>19.500</td>\n",
       "      <td>21.5000</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Magnesium</th>\n",
       "      <td>178.0</td>\n",
       "      <td>99.741573</td>\n",
       "      <td>14.282484</td>\n",
       "      <td>70.00</td>\n",
       "      <td>88.0000</td>\n",
       "      <td>98.000</td>\n",
       "      <td>107.0000</td>\n",
       "      <td>162.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total_Phenols</th>\n",
       "      <td>178.0</td>\n",
       "      <td>2.295112</td>\n",
       "      <td>0.625851</td>\n",
       "      <td>0.98</td>\n",
       "      <td>1.7425</td>\n",
       "      <td>2.355</td>\n",
       "      <td>2.8000</td>\n",
       "      <td>3.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flavanoids</th>\n",
       "      <td>178.0</td>\n",
       "      <td>2.029270</td>\n",
       "      <td>0.998859</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.2050</td>\n",
       "      <td>2.135</td>\n",
       "      <td>2.8750</td>\n",
       "      <td>5.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nonflavanoid_Phenols</th>\n",
       "      <td>178.0</td>\n",
       "      <td>0.361854</td>\n",
       "      <td>0.124453</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.2700</td>\n",
       "      <td>0.340</td>\n",
       "      <td>0.4375</td>\n",
       "      <td>0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Proanthocyanins</th>\n",
       "      <td>178.0</td>\n",
       "      <td>1.590899</td>\n",
       "      <td>0.572359</td>\n",
       "      <td>0.41</td>\n",
       "      <td>1.2500</td>\n",
       "      <td>1.555</td>\n",
       "      <td>1.9500</td>\n",
       "      <td>3.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Color_Intensity</th>\n",
       "      <td>178.0</td>\n",
       "      <td>5.058090</td>\n",
       "      <td>2.318286</td>\n",
       "      <td>1.28</td>\n",
       "      <td>3.2200</td>\n",
       "      <td>4.690</td>\n",
       "      <td>6.2000</td>\n",
       "      <td>13.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hue</th>\n",
       "      <td>178.0</td>\n",
       "      <td>0.957449</td>\n",
       "      <td>0.228572</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.7825</td>\n",
       "      <td>0.965</td>\n",
       "      <td>1.1200</td>\n",
       "      <td>1.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OD280/OD315 of diluted wines</th>\n",
       "      <td>178.0</td>\n",
       "      <td>2.611685</td>\n",
       "      <td>0.709990</td>\n",
       "      <td>1.27</td>\n",
       "      <td>1.9375</td>\n",
       "      <td>2.780</td>\n",
       "      <td>3.1700</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Proline</th>\n",
       "      <td>178.0</td>\n",
       "      <td>746.893258</td>\n",
       "      <td>314.907474</td>\n",
       "      <td>278.00</td>\n",
       "      <td>500.5000</td>\n",
       "      <td>673.500</td>\n",
       "      <td>985.0000</td>\n",
       "      <td>1680.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              count        mean         std     min       25%  \\\n",
       "Cultivator                    178.0    1.938202    0.775035    1.00    1.0000   \n",
       "Alcohol                       178.0   13.000618    0.811827   11.03   12.3625   \n",
       "Malic_Acid                    178.0    2.336348    1.117146    0.74    1.6025   \n",
       "Ash                           178.0    2.366517    0.274344    1.36    2.2100   \n",
       "Alcalinity of ash             178.0   19.494944    3.339564   10.60   17.2000   \n",
       "Magnesium                     178.0   99.741573   14.282484   70.00   88.0000   \n",
       "Total_Phenols                 178.0    2.295112    0.625851    0.98    1.7425   \n",
       "Flavanoids                    178.0    2.029270    0.998859    0.34    1.2050   \n",
       "Nonflavanoid_Phenols          178.0    0.361854    0.124453    0.13    0.2700   \n",
       "Proanthocyanins               178.0    1.590899    0.572359    0.41    1.2500   \n",
       "Color_Intensity               178.0    5.058090    2.318286    1.28    3.2200   \n",
       "Hue                           178.0    0.957449    0.228572    0.48    0.7825   \n",
       "OD280/OD315 of diluted wines  178.0    2.611685    0.709990    1.27    1.9375   \n",
       "Proline                       178.0  746.893258  314.907474  278.00  500.5000   \n",
       "\n",
       "                                  50%       75%      max  \n",
       "Cultivator                      2.000    3.0000     3.00  \n",
       "Alcohol                        13.050   13.6775    14.83  \n",
       "Malic_Acid                      1.865    3.0825     5.80  \n",
       "Ash                             2.360    2.5575     3.23  \n",
       "Alcalinity of ash              19.500   21.5000    30.00  \n",
       "Magnesium                      98.000  107.0000   162.00  \n",
       "Total_Phenols                   2.355    2.8000     3.88  \n",
       "Flavanoids                      2.135    2.8750     5.08  \n",
       "Nonflavanoid_Phenols            0.340    0.4375     0.66  \n",
       "Proanthocyanins                 1.555    1.9500     3.58  \n",
       "Color_Intensity                 4.690    6.2000    13.00  \n",
       "Hue                             0.965    1.1200     1.71  \n",
       "OD280/OD315 of diluted wines    2.780    3.1700     4.00  \n",
       "Proline                       673.500  985.0000  1680.00  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(178, 14)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x = wine.drop('Cultivator', axis=1)\n",
    "y = wine['Cultivator']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler(copy=True, with_mean=True, with_std=True)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fit only the training data\n",
    "scaler.fit(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_train = scaler.transform(x_train)\n",
    "x_test = scaler.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mlp = MLPClassifier(hidden_layer_sizes=(13,13,13),max_iter=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
       "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "       hidden_layer_sizes=(13, 13, 13), learning_rate='constant',\n",
       "       learning_rate_init=0.001, max_iter=500, momentum=0.9,\n",
       "       nesterovs_momentum=True, power_t=0.5, random_state=None,\n",
       "       shuffle=True, solver='adam', tol=0.0001, validation_fraction=0.1,\n",
       "       verbose=False, warm_start=False)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlp.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictions = mlp.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[13  0  0]\n",
      " [ 0 22  0]\n",
      " [ 0  0 10]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          1       1.00      1.00      1.00        13\n",
      "          2       1.00      1.00      1.00        22\n",
      "          3       1.00      1.00      1.00        10\n",
      "\n",
      "avg / total       1.00      1.00      1.00        45\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(mlp.coefs_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
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    {
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      "text/plain": [
       "13"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "execution_count": null,
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   },
   "outputs": [],
   "source": []
  }
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