Multinom_log_reg.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lab-11: Multinomial Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 427,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.linear_model import LogisticRegression as LR\n",
    "from sklearn.model_selection import train_test_split as tt_split\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import accuracy_score as acc\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"C:/Users/Suresh Kumar/Downloads/glass_multiclass.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RI</th>\n",
       "      <th>Na</th>\n",
       "      <th>Mg</th>\n",
       "      <th>Al</th>\n",
       "      <th>Si</th>\n",
       "      <th>K</th>\n",
       "      <th>Ca</th>\n",
       "      <th>Ba</th>\n",
       "      <th>Fe</th>\n",
       "      <th>Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.52101</td>\n",
       "      <td>13.64</td>\n",
       "      <td>4.49</td>\n",
       "      <td>1.10</td>\n",
       "      <td>71.78</td>\n",
       "      <td>0.06</td>\n",
       "      <td>8.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.51761</td>\n",
       "      <td>13.89</td>\n",
       "      <td>3.60</td>\n",
       "      <td>1.36</td>\n",
       "      <td>72.73</td>\n",
       "      <td>0.48</td>\n",
       "      <td>7.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.51618</td>\n",
       "      <td>13.53</td>\n",
       "      <td>3.55</td>\n",
       "      <td>1.54</td>\n",
       "      <td>72.99</td>\n",
       "      <td>0.39</td>\n",
       "      <td>7.78</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.51766</td>\n",
       "      <td>13.21</td>\n",
       "      <td>3.69</td>\n",
       "      <td>1.29</td>\n",
       "      <td>72.61</td>\n",
       "      <td>0.57</td>\n",
       "      <td>8.22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.51742</td>\n",
       "      <td>13.27</td>\n",
       "      <td>3.62</td>\n",
       "      <td>1.24</td>\n",
       "      <td>73.08</td>\n",
       "      <td>0.55</td>\n",
       "      <td>8.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        RI     Na    Mg    Al     Si     K    Ca   Ba   Fe  Type\n",
       "0  1.52101  13.64  4.49  1.10  71.78  0.06  8.75  0.0  0.0     1\n",
       "1  1.51761  13.89  3.60  1.36  72.73  0.48  7.83  0.0  0.0     1\n",
       "2  1.51618  13.53  3.55  1.54  72.99  0.39  7.78  0.0  0.0     1\n",
       "3  1.51766  13.21  3.69  1.29  72.61  0.57  8.22  0.0  0.0     1\n",
       "4  1.51742  13.27  3.62  1.24  73.08  0.55  8.07  0.0  0.0     1"
      ]
     },
     "execution_count": 429,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train, test = tt_split(data, test_size = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_data = train.iloc[:, :-1]\n",
    "train_label = train.iloc[:,-1]\n",
    "test_data = test.iloc[:,:-1]\n",
    "test_label = test.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model-3: independent variables (RI,Na, Mg, AI, SI, K, Ca, Ba, Fe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 432,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf = LR(penalty= 'l2', multi_class = 'multinomial', solver = 'newton-cg', class_weight = 'balanced')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 433,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight='balanced', dual=False,\n",
       "          fit_intercept=True, intercept_scaling=1, max_iter=100,\n",
       "          multi_class='multinomial', n_jobs=1, penalty='l2',\n",
       "          random_state=None, solver='newton-cg', tol=0.0001, verbose=0,\n",
       "          warm_start=False)"
      ]
     },
     "execution_count": 433,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(train_data, train_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 434,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred = clf.predict(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 435,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.53488372093023251"
      ]
     },
     "execution_count": 435,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc(test_label, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 436,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[8, 2, 4, 0, 0, 0],\n",
       "       [2, 5, 8, 0, 1, 0],\n",
       "       [1, 2, 1, 0, 0, 0],\n",
       "       [0, 0, 0, 1, 0, 0],\n",
       "       [0, 0, 0, 0, 2, 0],\n",
       "       [0, 0, 0, 0, 0, 6]], dtype=int64)"
      ]
     },
     "execution_count": 436,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(test_label, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7321206080239222"
      ]
     },
     "execution_count": 437,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(test_label, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.48148148148148151"
      ]
     },
     "execution_count": 438,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fpr, tpr, thresholds = metrics.roc_curve(test_label, y_pred, pos_label=2)\n",
    "metrics.auc(fpr, tpr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model-1: independent variables (RI,Na, Mg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 439,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_data_model2 = train_data.iloc[:,:3]\n",
    "test_data_model2 = test_data.iloc[:,:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf2 = LR(penalty= 'l2', multi_class = 'multinomial', solver = 'newton-cg', class_weight = 'balanced')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.55813953488372092"
      ]
     },
     "execution_count": 441,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf2.fit(train_data_model2, train_label)\n",
    "y_pred2 = clf2.predict(test_data_model2)\n",
    "acc(test_label, y_pred2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 442,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9, 1, 4, 0, 0, 0],\n",
       "       [8, 3, 4, 0, 0, 1],\n",
       "       [0, 0, 4, 0, 0, 0],\n",
       "       [0, 0, 0, 1, 0, 0],\n",
       "       [0, 0, 0, 0, 1, 1],\n",
       "       [0, 0, 0, 0, 0, 6]], dtype=int64)"
      ]
     },
     "execution_count": 442,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(test_label, y_pred2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.70533266882631451"
      ]
     },
     "execution_count": 443,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(test_label, y_pred2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 444,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.33333333333333331"
      ]
     },
     "execution_count": 444,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fpr2, tpr2, thresholds2 = metrics.roc_curve(test_label, y_pred2, pos_label=2)\n",
    "metrics.auc(fpr2, tpr2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model-2: independent variables (RI,Na, Mg, AI, SI, K)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 445,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_data_model3 = train_data.iloc[:,:6]\n",
    "test_data_model3 = test_data.iloc[:,:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 446,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf3 = LR(penalty= 'l2', multi_class = 'multinomial', solver = 'newton-cg', class_weight = 'balanced')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 447,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.53488372093023251"
      ]
     },
     "execution_count": 447,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf3.fit(train_data_model3, train_label)\n",
    "y_pred3 = clf3.predict(test_data_model3)\n",
    "acc(test_label, y_pred3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 448,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9, 1, 4, 0, 0, 0],\n",
       "       [3, 4, 8, 0, 1, 0],\n",
       "       [1, 2, 1, 0, 0, 0],\n",
       "       [0, 0, 0, 1, 0, 0],\n",
       "       [0, 0, 0, 0, 2, 0],\n",
       "       [0, 0, 0, 0, 0, 6]], dtype=int64)"
      ]
     },
     "execution_count": 448,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(test_label, y_pred3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 449,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7321206080239222"
      ]
     },
     "execution_count": 449,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(test_label, y_pred3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 450,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.47453703703703709"
      ]
     },
     "execution_count": 450,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fpr3, tpr3, thresholds3 = metrics.roc_curve(test_label, y_pred3, pos_label=2)\n",
    "metrics.auc(fpr3, tpr3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model-4: independent variables (SI, K, Ca, Ba, Fe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 451,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_data_model4 = train_data.iloc[:,4:]\n",
    "test_data_model4 = test_data.iloc[:,4:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 452,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf4 = LR(penalty= 'l2', multi_class = 'multinomial', solver = 'newton-cg', class_weight = 'balanced')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 453,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.53488372093023251"
      ]
     },
     "execution_count": 453,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf4.fit(train_data_model4, train_label)\n",
    "y_pred4 = clf4.predict(test_data_model4)\n",
    "acc(test_label, y_pred4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 454,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7, 2, 5, 0, 0, 0],\n",
       "       [1, 4, 9, 0, 2, 0],\n",
       "       [0, 0, 3, 0, 1, 0],\n",
       "       [0, 0, 0, 1, 0, 0],\n",
       "       [0, 0, 0, 0, 2, 0],\n",
       "       [0, 0, 0, 0, 0, 6]], dtype=int64)"
      ]
     },
     "execution_count": 454,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(test_label, y_pred4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 455,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.60889608771492654"
      ]
     },
     "execution_count": 455,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(test_label, y_pred4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 456,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.44328703703703709"
      ]
     },
     "execution_count": 456,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fpr4, tpr4, thresholds4 = metrics.roc_curve(test_label, y_pred4, pos_label=2)\n",
    "metrics.auc(fpr4, tpr4)"
   ]
  }
 ],
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