ML_WORKING.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import scale\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
    "from prettytable import PrettyTable\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score, make_scorer\n",
    "from sklearn.naive_bayes import GaussianNB, MultinomialNB\n",
    "from geneticfs import GeneticFS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('final_cleaned_dataset.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1249f1ba940>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(pd.isnull(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['URL', 'embedded_domain_in_path', 'ip_address', 'dictionary_word',\n",
       "       'targeted_brand', 'ext url', 'Phishing', 'Subdomains', 'Prefix-Suffix',\n",
       "       'Anchor Tag', 'Tags', 'Forms', 'SSL Expiry Date', 'SSL Cert Check',\n",
       "       'Alexa Ranking', 'URL Length', 'URL Shortened', 'Redirects User',\n",
       "       'URL has @ Symbol', 'URL has HTTPS', 'URL Small', 'URL Medium',\n",
       "       'URL Large', 'Small Domain', 'Large Domain', 'ORG Domain', 'COM Domain',\n",
       "       'Has PHP Pages', 'Is Palindrome', 'Numbers in Domain', 'X Domain',\n",
       "       'Vowels in URL', 'Deal in URL', 'Download in URL', 'Free in URL',\n",
       "       'BJKT in URL', 'URL is Happy', 'Just Why', 'Rar or Zip in URL',\n",
       "       'Large number of Subdomains', 'Small number of Subdomains'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Phishing'].replace('yes',1, inplace=True)\n",
    "data['Phishing'].replace('no',0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "numerical_features = ['Subdomains', 'Anchor Tag', 'SSL Expiry Date', 'Alexa Ranking', 'URL Length', 'Vowels in URL']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(['Tags'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:590: DataConversionWarning: Data with input dtype int64 was converted to float64 by the scale function.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:590: DataConversionWarning: Data with input dtype int64 was converted to float64 by the scale function.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:590: DataConversionWarning: Data with input dtype int64 was converted to float64 by the scale function.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:590: DataConversionWarning: Data with input dtype int64 was converted to float64 by the scale function.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:590: DataConversionWarning: Data with input dtype int64 was converted to float64 by the scale function.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:590: DataConversionWarning: Data with input dtype int64 was converted to float64 by the scale function.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "for col in numerical_features:\n",
    "    data[col] = scale(data[col].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>URL</th>\n",
       "      <th>embedded_domain_in_path</th>\n",
       "      <th>ip_address</th>\n",
       "      <th>dictionary_word</th>\n",
       "      <th>targeted_brand</th>\n",
       "      <th>ext url</th>\n",
       "      <th>Phishing</th>\n",
       "      <th>Subdomains</th>\n",
       "      <th>Prefix-Suffix</th>\n",
       "      <th>Anchor Tag</th>\n",
       "      <th>...</th>\n",
       "      <th>Vowels in URL</th>\n",
       "      <th>Deal in URL</th>\n",
       "      <th>Download in URL</th>\n",
       "      <th>Free in URL</th>\n",
       "      <th>BJKT in URL</th>\n",
       "      <th>URL is Happy</th>\n",
       "      <th>Just Why</th>\n",
       "      <th>Rar or Zip in URL</th>\n",
       "      <th>Large number of Subdomains</th>\n",
       "      <th>Small number of Subdomains</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>https://locking-app-adverds.000webhostapp.com/...</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.284342</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.937982</td>\n",
       "      <td>...</td>\n",
       "      <td>0.759499</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>https://mxtoolbox.com/public/upgradev2.aspx?ps...</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.989844</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.666334</td>\n",
       "      <td>...</td>\n",
       "      <td>2.197964</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>http://parolishop.com.br/questionreviews.htm</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.284342</td>\n",
       "      <td>1</td>\n",
       "      <td>0.860532</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.402337</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>http://innovate.ieee.org/innovate/35675?lt=xpl...</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.284342</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.721656</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.125709</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>http://ietbhaddal.edu.in/bb.mobile/mobile/inde...</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.284342</td>\n",
       "      <td>1</td>\n",
       "      <td>0.937982</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.236360</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 URL  embedded_domain_in_path  \\\n",
       "0  https://locking-app-adverds.000webhostapp.com/...                        1   \n",
       "1  https://mxtoolbox.com/public/upgradev2.aspx?ps...                        0   \n",
       "2       http://parolishop.com.br/questionreviews.htm                        0   \n",
       "3  http://innovate.ieee.org/innovate/35675?lt=xpl...                        1   \n",
       "4  http://ietbhaddal.edu.in/bb.mobile/mobile/inde...                        0   \n",
       "\n",
       "   ip_address  dictionary_word  targeted_brand  ext url  Phishing  Subdomains  \\\n",
       "0          -1                1              -1       -1         1    0.284342   \n",
       "1          -1               -1              -1       -1         0   -0.989844   \n",
       "2          -1               -1              -1       -1         1    0.284342   \n",
       "3          -1               -1              -1       -1         0    0.284342   \n",
       "4          -1               -1               1       -1         1    0.284342   \n",
       "\n",
       "   Prefix-Suffix  Anchor Tag             ...              Vowels in URL  \\\n",
       "0             -1    0.937982             ...                   0.759499   \n",
       "1              1   -0.666334             ...                   2.197964   \n",
       "2              1    0.860532             ...                  -0.402337   \n",
       "3              1   -0.721656             ...                  -0.125709   \n",
       "4              1    0.937982             ...                  -0.236360   \n",
       "\n",
       "   Deal in URL  Download in URL  Free in URL  BJKT in URL  URL is Happy  \\\n",
       "0           -1               -1           -1           -1             1   \n",
       "1           -1               -1           -1           -1             1   \n",
       "2           -1               -1           -1           -1            -1   \n",
       "3           -1               -1           -1           -1            -1   \n",
       "4           -1               -1           -1           -1            -1   \n",
       "\n",
       "   Just Why  Rar or Zip in URL  Large number of Subdomains  \\\n",
       "0         1                 -1                          -1   \n",
       "1         1                 -1                          -1   \n",
       "2         1                 -1                          -1   \n",
       "3         1                 -1                          -1   \n",
       "4         1                 -1                          -1   \n",
       "\n",
       "   Small number of Subdomains  \n",
       "0                          -1  \n",
       "1                           1  \n",
       "2                          -1  \n",
       "3                          -1  \n",
       "4                          -1  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data['Phishing'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(['Phishing'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(['URL'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['embedded_domain_in_path', 'ip_address', 'dictionary_word',\n",
       "       'targeted_brand', 'ext url', 'Subdomains', 'Prefix-Suffix',\n",
       "       'Anchor Tag', 'Forms', 'SSL Expiry Date', 'SSL Cert Check',\n",
       "       'Alexa Ranking', 'URL Length', 'URL Shortened', 'Redirects User',\n",
       "       'URL has @ Symbol', 'URL has HTTPS', 'URL Small', 'URL Medium',\n",
       "       'URL Large', 'Small Domain', 'Large Domain', 'ORG Domain', 'COM Domain',\n",
       "       'Has PHP Pages', 'Is Palindrome', 'Numbers in Domain', 'X Domain',\n",
       "       'Vowels in URL', 'Deal in URL', 'Download in URL', 'Free in URL',\n",
       "       'BJKT in URL', 'URL is Happy', 'Just Why', 'Rar or Zip in URL',\n",
       "       'Large number of Subdomains', 'Small number of Subdomains'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_X_cols = data.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_X = data[all_X_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#KNN\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split( all_X, y, test_size=0.30, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def knn_kfold_validation(neighbours, X_train, y_train, algo='auto'):\n",
    "    cv_scores=[]\n",
    "    for k in neighbours:\n",
    "        knn = KNeighborsClassifier(n_neighbors=k,algorithm='brute')\n",
    "        scores = cross_val_score(knn, X_train, y_train, scoring='accuracy', cv=5)\n",
    "        cv_scores.append(scores.mean())\n",
    "        print(\"K : %d , Score : %f\" % (k,scores.mean()))\n",
    "    return cv_scores\n",
    "\n",
    "def optimal_k_plot(neighbours, cv_scores):    \n",
    "    optimal_k = neighbours[cv_scores.index(max(cv_scores))]\n",
    "    plt.plot(neighbours, cv_scores)\n",
    "    for xy in zip(neighbours,np.round(cv_scores,2)):\n",
    "        plt.annotate(\"('%s',%s)\"% xy, xy=xy, textcoords='data')\n",
    "    plt.xlabel('Number of Neighbors K')\n",
    "    plt.ylabel('CV_Scores')\n",
    "    print(\"CV_SCORES: \",cv_scores)\n",
    "    return optimal_k\n",
    "\n",
    "def optimal_knn(optimal_k, X_train, X_test, y_train, y_test, algo='auto'):\n",
    "    # ============================== KNN with k = optimal_k ===============================================\n",
    "    # instantiate learning model k = optimal_k\n",
    "    knn_optimal = KNeighborsClassifier(n_neighbors=optimal_k, algorithm = algo)\n",
    "\n",
    "    # fitting the model\n",
    "    knn_optimal.fit(X_train, y_train)\n",
    "\n",
    "    # predict the response\n",
    "    pred = knn_optimal.predict(X_test)\n",
    "\n",
    "    # evaluate accuracy\n",
    "    acc = accuracy_score(y_test, pred) * 100\n",
    "    print('\\nThe accuracy of the knn classifier for k = %d is %f%%' % (optimal_k, acc))\n",
    "    \n",
    "    return pred\n",
    "\n",
    "def gen_confusion_matrix(y_test,pred):\n",
    "    print(sns.heatmap(confusion_matrix(y_test,pred),cmap=\"viridis\",fmt='g', annot=True))\n",
    "    \n",
    "def gen_scores(y_test, pred):\n",
    "    gen_confusion_matrix(y_test,pred)\n",
    "    pre = np.round(precision_score(y_test,pred,average='weighted'),4)\n",
    "    recall = np.round(recall_score(y_test,pred,average='weighted'),4)\n",
    "    f1 = np.round(f1_score(y_test,pred,average='weighted'),4)\n",
    "    accuracy = np.round(accuracy_score(y_test,pred),4)\n",
    "    table = PrettyTable()\n",
    "    table.field_names = ['Precision Score', 'Recall Score', 'f1 Score', 'Accuracy']\n",
    "    table.add_row([pre,recall,f1,accuracy])\n",
    "    print(table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "neighbours = list(range(1,41,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K : 1 , Score : 0.904615\n",
      "K : 3 , Score : 0.913287\n",
      "K : 5 , Score : 0.915284\n",
      "K : 7 , Score : 0.913280\n",
      "K : 9 , Score : 0.909942\n",
      "K : 11 , Score : 0.898609\n",
      "K : 13 , Score : 0.899946\n",
      "K : 15 , Score : 0.893280\n",
      "K : 17 , Score : 0.895944\n",
      "K : 19 , Score : 0.895949\n",
      "K : 21 , Score : 0.892613\n",
      "K : 23 , Score : 0.895280\n",
      "K : 25 , Score : 0.893951\n",
      "K : 27 , Score : 0.892618\n",
      "K : 29 , Score : 0.888618\n",
      "K : 31 , Score : 0.884618\n",
      "K : 33 , Score : 0.886615\n",
      "K : 35 , Score : 0.884615\n",
      "K : 37 , Score : 0.878613\n",
      "K : 39 , Score : 0.876613\n"
     ]
    }
   ],
   "source": [
    "cv_scores = knn_kfold_validation(neighbours, X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CV_SCORES:  [0.9046153846153846, 0.9132865105908584, 0.9152842809364549, 0.9132798216276476, 0.9099420289855071, 0.8986086956521738, 0.8999464882943145, 0.8932798216276477, 0.8959442586399108, 0.895948717948718, 0.892613154960981, 0.8952798216276477, 0.8939509476031215, 0.8926176142697884, 0.8886176142697881, 0.8846176142697881, 0.8866153846153846, 0.8846153846153847, 0.8786131549609809, 0.876613154960981]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "optimal_k_plot(neighbours,cv_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimal_k=5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The accuracy of the knn classifier for k = 5 is 92.690513%\n"
     ]
    }
   ],
   "source": [
    "pred = optimal_knn(optimal_k,X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.62x0.755)\n",
      "+-----------------+--------------+----------+----------+\n",
      "| Precision Score | Recall Score | f1 Score | Accuracy |\n",
      "+-----------------+--------------+----------+----------+\n",
      "|      0.927      |    0.9269    |  0.9269  |  0.9269  |\n",
      "+-----------------+--------------+----------+----------+\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gen_scores(y_test, pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 10 Complete [Time Taken For Last Iteration: 9.79 Seconds]\n",
      "Iteration 20 Complete [Time Taken For Last Iteration: 6.86 Seconds]\n",
      "Iteration 30 Complete [Time Taken For Last Iteration: 7.62 Seconds]\n",
      "Iteration 40 Complete [Time Taken For Last Iteration: 7.12 Seconds]\n",
      "Iteration 50 Complete [Time Taken For Last Iteration: 7.54 Seconds]\n",
      "Iteration 60 Complete [Time Taken For Last Iteration: 6.69 Seconds]\n",
      "Iteration 70 Complete [Time Taken For Last Iteration: 6.86 Seconds]\n",
      "Iteration 80 Complete [Time Taken For Last Iteration: 6.99 Seconds]\n",
      "Iteration 90 Complete [Time Taken For Last Iteration: 3.27 Seconds]\n",
      "Iteration 100 Complete [Time Taken For Last Iteration: 3.73 Seconds]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "\n",
    "gen_model = KNeighborsClassifier(n_neighbors=optimal_k)\n",
    "gfs = GeneticFS()\n",
    "\n",
    "# fit the optimizer\n",
    "gfs.fit(model=gen_model, _type='classification', X=all_X, y=y) # classification model\n",
    "\n",
    "# get results output\n",
    "binary_output_of_optimal_variables, indicies_of_optimal_variables = gfs.results()\n",
    "\n",
    "# plot results of progress\n",
    "gfs.plot_progress()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0,\n",
       "       1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0])"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "binary_output_of_optimal_variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "reduced_cols = list(all_X.columns[indicies_of_optimal_variables])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "#reduced_cols.remove('Is Palindrome')\n",
    "#reduced_cols.remove('BJKT in URL')\n",
    "#reduced_cols.remove('Just Why')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "reduced_X = all_X[reduced_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['embedded_domain_in_path', 'Anchor Tag', 'Forms', 'SSL Expiry Date',\n",
       "       'URL has HTTPS', 'URL Large', 'Has PHP Pages', 'Free in URL',\n",
       "       'BJKT in URL', 'URL is Happy', 'Just Why', 'Rar or Zip in URL',\n",
       "       'Small number of Subdomains'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reduced_X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "reduced_X = all_X[['embedded_domain_in_path', 'ip_address', 'Subdomains', 'Anchor Tag','Forms', 'SSL Expiry Date', 'SSL Cert Check', 'Redirects User','URL has @ Symbol', 'URL Small', 'ORG Domain', 'COM Domain', 'X Domain','Vowels in URL', 'Download in URL', 'Free in URL', 'Rar or Zip in URL','Large number of Subdomains']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split( reduced_X, y, test_size=0.30, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K : 1 , Score : 0.956647\n",
      "K : 3 , Score : 0.956649\n",
      "K : 5 , Score : 0.955978\n",
      "K : 7 , Score : 0.950633\n",
      "K : 9 , Score : 0.941291\n",
      "K : 11 , Score : 0.933291\n",
      "K : 13 , Score : 0.929955\n",
      "K : 15 , Score : 0.924624\n",
      "K : 17 , Score : 0.917291\n",
      "K : 19 , Score : 0.915955\n",
      "K : 21 , Score : 0.911287\n",
      "K : 23 , Score : 0.909284\n",
      "K : 25 , Score : 0.910618\n",
      "K : 27 , Score : 0.911949\n",
      "K : 29 , Score : 0.911949\n",
      "K : 31 , Score : 0.907944\n",
      "K : 33 , Score : 0.910611\n",
      "K : 35 , Score : 0.911275\n",
      "K : 37 , Score : 0.909942\n",
      "K : 39 , Score : 0.910606\n"
     ]
    }
   ],
   "source": [
    "cv_scores = knn_kfold_validation(neighbours, X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CV_SCORES:  [0.9566465997770346, 0.9566488294314383, 0.9559777034559644, 0.950633221850613, 0.9412909698996655, 0.9332909698996655, 0.9299554069119287, 0.9246243032329989, 0.9172909698996655, 0.9159554069119287, 0.9112865105908584, 0.9092842809364547, 0.9106176142697884, 0.911948717948718, 0.911948717948718, 0.9079442586399109, 0.9106109253065775, 0.9112753623188405, 0.9099420289855071, 0.9106064659977704]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "optimal_k_plot(neighbours,cv_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The accuracy of the knn classifier for k = 3 is 95.956454%\n"
     ]
    }
   ],
   "source": [
    "pred = optimal_knn(3,X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.62x0.755)\n",
      "+-----------------+--------------+----------+----------+\n",
      "| Precision Score | Recall Score | f1 Score | Accuracy |\n",
      "+-----------------+--------------+----------+----------+\n",
      "|      0.9599     |    0.9596    |  0.9596  |  0.9596  |\n",
      "+-----------------+--------------+----------+----------+\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gen_scores(y_test, pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "hybrid_X = all_X[['embedded_domain_in_path', 'Subdomains', 'Anchor Tag','Forms', 'SSL Cert Check', 'Redirects User','URL has @ Symbol', 'ORG Domain', 'COM Domain', 'X Domain', 'Download in URL', 'Free in URL', 'Rar or Zip in URL']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K : 1 , Score : 0.939287\n",
      "K : 3 , Score : 0.946631\n",
      "K : 5 , Score : 0.942627\n",
      "K : 7 , Score : 0.944624\n",
      "K : 9 , Score : 0.938624\n",
      "K : 11 , Score : 0.935958\n",
      "K : 13 , Score : 0.933964\n",
      "K : 15 , Score : 0.923291\n",
      "K : 17 , Score : 0.915951\n",
      "K : 19 , Score : 0.910618\n",
      "K : 21 , Score : 0.906620\n",
      "K : 23 , Score : 0.907287\n",
      "K : 25 , Score : 0.907953\n",
      "K : 27 , Score : 0.905953\n",
      "K : 29 , Score : 0.909953\n",
      "K : 31 , Score : 0.909282\n",
      "K : 33 , Score : 0.905282\n",
      "K : 35 , Score : 0.903280\n",
      "K : 37 , Score : 0.901944\n",
      "K : 39 , Score : 0.900611\n",
      "CV_SCORES:  [0.9392865105908583, 0.9466309921962097, 0.9426265328874024, 0.9446243032329991, 0.938624303232999, 0.9359576365663322, 0.9339643255295428, 0.9232909698996655, 0.9159509476031215, 0.9106176142697882, 0.9066198439241917, 0.9072865105908585, 0.9079531772575251, 0.905953177257525, 0.9099531772575251, 0.9092820512820513, 0.9052820512820514, 0.9032798216276477, 0.9019442586399109, 0.9006109253065775]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(hybrid_X, y, test_size=0.30, random_state=0)\n",
    "cv_scores = knn_kfold_validation(neighbours, X_train, y_train)\n",
    "optimal_k_plot(neighbours,cv_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mlxtend.feature_selection import SequentialFeatureSelector as SFS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "sfs1 = SFS(KNeighborsClassifier(3), k_features=13, scoring='f1_weighted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SequentialFeatureSelector(clone_estimator=True, cv=5,\n",
       "             estimator=KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=None, n_neighbors=3, p=2,\n",
       "           weights='uniform'),\n",
       "             floating=False, forward=True, k_features=13, n_jobs=1,\n",
       "             pre_dispatch='2*n_jobs', scoring='f1_weighted', verbose=0)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sfs1.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: {'feature_idx': (11,),\n",
       "  'cv_scores': array([0.96      , 0.96666667, 0.94666667, 0.94999833, 0.96655518]),\n",
       "  'avg_score': 0.9579773700892943,\n",
       "  'feature_names': ('Alexa Ranking',)},\n",
       " 2: {'feature_idx': (7, 11),\n",
       "  'cv_scores': array([0.99666656, 0.97333096, 0.97333333, 0.97333096, 0.96989967]),\n",
       "  'avg_score': 0.9773122959657427,\n",
       "  'feature_names': ('Anchor Tag', 'Alexa Ranking')},\n",
       " 3: {'feature_idx': (2, 7, 11),\n",
       "  'cv_scores': array([0.98999833, 0.98333278, 0.97333333, 0.969999  , 0.97324415]),\n",
       "  'avg_score': 0.9779815181965944,\n",
       "  'feature_names': ('dictionary_word', 'Anchor Tag', 'Alexa Ranking')},\n",
       " 4: {'feature_idx': (2, 7, 8, 11),\n",
       "  'cv_scores': array([0.99333274, 0.98      , 0.98333278, 0.97666693, 0.97993266]),\n",
       "  'avg_score': 0.9826530211559941,\n",
       "  'feature_names': ('dictionary_word',\n",
       "   'Anchor Tag',\n",
       "   'Forms',\n",
       "   'Alexa Ranking')},\n",
       " 5: {'feature_idx': (1, 2, 7, 8, 11),\n",
       "  'cv_scores': array([0.99333274, 0.98      , 0.98333278, 0.97666693, 0.97993266]),\n",
       "  'avg_score': 0.9826530211559941,\n",
       "  'feature_names': ('ip_address',\n",
       "   'dictionary_word',\n",
       "   'Anchor Tag',\n",
       "   'Forms',\n",
       "   'Alexa Ranking')},\n",
       " 6: {'feature_idx': (1, 2, 7, 8, 11, 14),\n",
       "  'cv_scores': array([0.99333274, 0.98      , 0.98333278, 0.97666693, 0.97993266]),\n",
       "  'avg_score': 0.9826530211559941,\n",
       "  'feature_names': ('ip_address',\n",
       "   'dictionary_word',\n",
       "   'Anchor Tag',\n",
       "   'Forms',\n",
       "   'Alexa Ranking',\n",
       "   'Redirects User')},\n",
       " 7: {'feature_idx': (1, 2, 7, 8, 11, 14, 17),\n",
       "  'cv_scores': array([0.99333274, 0.98      , 0.98333278, 0.97666693, 0.97993266]),\n",
       "  'avg_score': 0.9826530211559941,\n",
       "  'feature_names': ('ip_address',\n",
       "   'dictionary_word',\n",
       "   'Anchor Tag',\n",
       "   'Forms',\n",
       "   'Alexa Ranking',\n",
       "   'Redirects User',\n",
       "   'URL Small')},\n",
       " 8: {'feature_idx': (1, 2, 7, 8, 11, 14, 17, 29),\n",
       "  'cv_scores': array([0.99333274, 0.98      , 0.98333278, 0.97666693, 0.97993266]),\n",
       "  'avg_score': 0.9826530211559941,\n",
       "  'feature_names': ('ip_address',\n",
       "   'dictionary_word',\n",
       "   'Anchor Tag',\n",
       "   'Forms',\n",
       "   'Alexa Ranking',\n",
       "   'Redirects User',\n",
       "   'URL Small',\n",
       "   'Deal in URL')},\n",
       " 9: {'feature_idx': (1, 2, 7, 8, 11, 14, 17, 29, 31),\n",
       "  'cv_scores': array([0.99333274, 0.98      , 0.98333278, 0.97666693, 0.97993266]),\n",
       "  'avg_score': 0.9826530211559941,\n",
       "  'feature_names': ('ip_address',\n",
       "   'dictionary_word',\n",
       "   'Anchor Tag',\n",
       "   'Forms',\n",
       "   'Alexa Ranking',\n",
       "   'Redirects User',\n",
       "   'URL Small',\n",
       "   'Deal in URL',\n",
       "   'Free in URL')},\n",
       " 10: {'feature_idx': (1, 2, 7, 8, 11, 14, 17, 29, 31, 32),\n",
       "  'cv_scores': array([0.99333274, 0.98      , 0.98333278, 0.97666693, 0.97993266]),\n",
       "  'avg_score': 0.9826530211559941,\n",
       "  'feature_names': ('ip_address',\n",
       "   'dictionary_word',\n",
       "   'Anchor Tag',\n",
       "   'Forms',\n",
       "   'Alexa Ranking',\n",
       "   'Redirects User',\n",
       "   'URL Small',\n",
       "   'Deal in URL',\n",
       "   'Free in URL',\n",
       "   'BJKT in URL')},\n",
       " 11: {'feature_idx': (1, 2, 7, 8, 11, 14, 17, 29, 31, 32, 34),\n",
       "  'cv_scores': array([0.99333274, 0.98      , 0.98333278, 0.97666693, 0.97993266]),\n",
       "  'avg_score': 0.9826530211559941,\n",
       "  'feature_names': ('ip_address',\n",
       "   'dictionary_word',\n",
       "   'Anchor Tag',\n",
       "   'Forms',\n",
       "   'Alexa Ranking',\n",
       "   'Redirects User',\n",
       "   'URL Small',\n",
       "   'Deal in URL',\n",
       "   'Free in URL',\n",
       "   'BJKT in URL',\n",
       "   'Just Why')},\n",
       " 12: {'feature_idx': (1, 2, 7, 8, 11, 14, 17, 29, 31, 32, 34, 36),\n",
       "  'cv_scores': array([0.99333274, 0.97666693, 0.98333278, 0.97666693, 0.97993266]),\n",
       "  'avg_score': 0.9819864063417555,\n",
       "  'feature_names': ('ip_address',\n",
       "   'dictionary_word',\n",
       "   'Anchor Tag',\n",
       "   'Forms',\n",
       "   'Alexa Ranking',\n",
       "   'Redirects User',\n",
       "   'URL Small',\n",
       "   'Deal in URL',\n",
       "   'Free in URL',\n",
       "   'BJKT in URL',\n",
       "   'Just Why',\n",
       "   'Large number of Subdomains')},\n",
       " 13: {'feature_idx': (1, 2, 7, 8, 11, 14, 15, 17, 29, 31, 32, 34, 36),\n",
       "  'cv_scores': array([0.99333274, 0.97666693, 0.98333278, 0.97333333, 0.97658863]),\n",
       "  'avg_score': 0.9806508812863319,\n",
       "  'feature_names': ('ip_address',\n",
       "   'dictionary_word',\n",
       "   'Anchor Tag',\n",
       "   'Forms',\n",
       "   'Alexa Ranking',\n",
       "   'Redirects User',\n",
       "   'URL has @ Symbol',\n",
       "   'URL Small',\n",
       "   'Deal in URL',\n",
       "   'Free in URL',\n",
       "   'BJKT in URL',\n",
       "   'Just Why',\n",
       "   'Large number of Subdomains')}}"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sfs1.subsets_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(all_X, y, test_size=0.30, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "gnb = GaussianNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB(priors=None, var_smoothing=1e-09)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gnb.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = gnb.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.62x0.755)\n",
      "+-----------------+--------------+----------+----------+\n",
      "| Precision Score | Recall Score | f1 Score | Accuracy |\n",
      "+-----------------+--------------+----------+----------+\n",
      "|      0.7603     |    0.6579    |  0.6277  |  0.6579  |\n",
      "+-----------------+--------------+----------+----------+\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gen_scores(y_test, pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 10 Complete [Time Taken For Last Iteration: 0.35 Seconds]\n",
      "Iteration 20 Complete [Time Taken For Last Iteration: 0.36 Seconds]\n",
      "Iteration 30 Complete [Time Taken For Last Iteration: 0.35 Seconds]\n",
      "Iteration 40 Complete [Time Taken For Last Iteration: 0.35 Seconds]\n",
      "Iteration 50 Complete [Time Taken For Last Iteration: 0.35 Seconds]\n",
      "Iteration 60 Complete [Time Taken For Last Iteration: 0.36 Seconds]\n",
      "Iteration 70 Complete [Time Taken For Last Iteration: 0.36 Seconds]\n",
      "Iteration 80 Complete [Time Taken For Last Iteration: 0.35 Seconds]\n",
      "Iteration 90 Complete [Time Taken For Last Iteration: 0.36 Seconds]\n",
      "Iteration 100 Complete [Time Taken For Last Iteration: 0.37 Seconds]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gen_model = GaussianNB()\n",
    "gfs = GeneticFS()\n",
    "\n",
    "# fit the optimizer\n",
    "gfs.fit(model=gen_model, _type='classification', X=all_X, y=y) # classification model\n",
    "\n",
    "# get results output\n",
    "binary_output_of_optimal_variables, indicies_of_optimal_variables = gfs.results()\n",
    "\n",
    "# plot results of progress\n",
    "gfs.plot_progress()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "reduced_cols = list(all_X.columns[indicies_of_optimal_variables])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "#reduced_cols.remove('Is Palindrome')\n",
    "#reduced_cols.remove('Vowels in URL')\n",
    "#reduced_cols.remove('BJKT in URL')\n",
    "#reduced_cols.remove('URL is Happy')\n",
    "#reduced_cols.remove('Just Why')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(reduced_X.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "reduced_X = all_X[reduced_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(reduced_X, y, test_size=0.30, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "gnb = GaussianNB()\n",
    "gnb.fit(X_train, y_train)\n",
    "pred = gnb.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.62x0.755)\n",
      "+-----------------+--------------+----------+----------+\n",
      "| Precision Score | Recall Score | f1 Score | Accuracy |\n",
      "+-----------------+--------------+----------+----------+\n",
      "|      0.9234     |    0.9222    |  0.9221  |  0.9222  |\n",
      "+-----------------+--------------+----------+----------+\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gen_scores(y_test, pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import RFE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "def apply_RF(X_train, y_train):\n",
    "    tree_list = [10,20,30,40,50,60,70,80,90,100]\n",
    "    \n",
    "    cv_scores=[]\n",
    "    for trees in tree_list:\n",
    "        scores = cross_val_score(RandomForestClassifier(n_estimators=trees), X_train, y_train, scoring=\"accuracy\", cv=5)\n",
    "        cv_scores.append(scores.mean())\n",
    "        print(\"TREES : %f , Score : %f\" % (trees,scores.mean()))\n",
    "    \n",
    "    op_index = cv_scores.index(max(cv_scores))\n",
    "    op_trees = tree_list[op_index]\n",
    "    print(\"Optimum Trees\",op_trees)\n",
    "    print(plt.plot(tree_list,cv_scores,marker='o',linestyle=\"dashed\"))\n",
    "    return op_trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split( all_X, y, test_size=0.30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TREES : 10.000000 , Score : 0.987327\n",
      "TREES : 20.000000 , Score : 0.991327\n",
      "TREES : 30.000000 , Score : 0.991993\n",
      "TREES : 40.000000 , Score : 0.991996\n",
      "TREES : 50.000000 , Score : 0.989989\n",
      "TREES : 60.000000 , Score : 0.992658\n",
      "TREES : 70.000000 , Score : 0.993327\n",
      "TREES : 80.000000 , Score : 0.991991\n",
      "TREES : 90.000000 , Score : 0.993324\n",
      "TREES : 100.000000 , Score : 0.993996\n",
      "Optimum Trees 100\n",
      "[<matplotlib.lines.Line2D object at 0x000001249FE28E10>]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "op_trees = apply_RF(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_model = RandomForestClassifier(n_estimators=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_model.fit(X_train, y_train)\n",
    "pred = rf_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.62x0.755)\n",
      "+-----------------+--------------+----------+----------+\n",
      "| Precision Score | Recall Score | f1 Score | Accuracy |\n",
      "+-----------------+--------------+----------+----------+\n",
      "|      0.9831     |    0.9829    |  0.9829  |  0.9829  |\n",
      "+-----------------+--------------+----------+----------+\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gen_scores(y_test, pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.62x0.755)\n",
      "+-----------------+--------------+----------+----------+\n",
      "| Precision Score | Recall Score | f1 Score | Accuracy |\n",
      "+-----------------+--------------+----------+----------+\n",
      "|      0.9877     |    0.9876    |  0.9876  |  0.9876  |\n",
      "+-----------------+--------------+----------+----------+\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf_model = RandomForestClassifier(n_estimators=10)\n",
    "rf_model.fit(X_train, y_train)\n",
    "pred = rf_model.predict(X_test)\n",
    "gen_scores(y_test, pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: DeprecationWarning: time.clock has been deprecated in Python 3.3 and will be removed from Python 3.8: use time.perf_counter or time.process_time instead\n",
      "  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6257313099922612\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:5: DeprecationWarning: time.clock has been deprecated in Python 3.3 and will be removed from Python 3.8: use time.perf_counter or time.process_time instead\n",
      "  \"\"\"\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "start = time.clock()\n",
    "selector = RFE(RandomForestClassifier(n_estimators=10), n_features_to_select=13)\n",
    "selector.fit(X_train, y_train)\n",
    "print (time.clock() - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True, False,  True, False, False, False, False,  True,  True,\n",
       "        True,  True,  True,  True, False, False, False,  True, False,\n",
       "       False, False, False,  True, False, False,  True, False,  True,\n",
       "       False,  True, False, False, False, False, False, False, False,\n",
       "       False, False])"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selector.support_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "reduced_cols = []\n",
    "for i in range(len(all_X.columns)):\n",
    "    if selector.support_[i] == True:\n",
    "        reduced_cols.append(all_X.columns[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "reduced_X = all_X[reduced_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(reduced_X, y, test_size=0.30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.62x0.755)\n",
      "+-----------------+--------------+----------+----------+\n",
      "| Precision Score | Recall Score | f1 Score | Accuracy |\n",
      "+-----------------+--------------+----------+----------+\n",
      "|      0.9876     |    0.9876    |  0.9876  |  0.9876  |\n",
      "+-----------------+--------------+----------+----------+\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf_model = RandomForestClassifier(n_estimators=10)\n",
    "rf_model.fit(X_train, y_train)\n",
    "pred = rf_model.predict(X_test)\n",
    "gen_scores(y_test, pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gen_model = RandomForestClassifier(n_estimators=250)\n",
    "gfs = GeneticFS()\n",
    "\n",
    "# fit the optimizer\n",
    "gfs.fit(model=gen_model, _type='classification', X=all_X, y=y) # classification model\n",
    "\n",
    "# get results output\n",
    "binary_output_of_optimal_variables, indicies_of_optimal_variables = gfs.results()\n",
    "\n",
    "# plot results of progress\n",
    "gfs.plot_progress()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Genetic Consumes like infinite amount of time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Highl overfitting - not good for production - High accuracies because the dataset used is a small one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split( all_X, y, test_size=0.30, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = {'kernel':('linear', 'rbf', 'poly'), 'C':[1, 10]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = GridSearchCV(SVC(gamma='scale'), parameters, cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid={'kernel': ('linear', 'rbf', 'poly'), 'C': [1, 10]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.62x0.755)\n",
      "+-----------------+--------------+----------+----------+\n",
      "| Precision Score | Recall Score | f1 Score | Accuracy |\n",
      "+-----------------+--------------+----------+----------+\n",
      "|      0.9518     |    0.9518    |  0.9518  |  0.9518  |\n",
      "+-----------------+--------------+----------+----------+\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gen_scores(y_test, pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gen_model = GridSearchCV(SVC(gamma='scale'), parameters, cv=5)\n",
    "gfs = GeneticFS()\n",
    "\n",
    "# fit the optimizer\n",
    "gfs.fit(model=gen_model, _type='classification', X=all_X, y=y) # classification model\n",
    "\n",
    "# get results output\n",
    "binary_output_of_optimal_variables, indicies_of_optimal_variables = gfs.results()\n",
    "\n",
    "# plot results of progress\n",
    "gfs.plot_progress()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "reduced_X_cols = ['embedded_domain_in_path', 'ip_address','Subdomains', 'Anchor Tag', 'Forms', 'SSL Expiry Date', 'SSL Cert Check', 'Redirects User',\n",
    "       'URL has @ Symbol', 'URL has HTTPS', 'URL Small','Download in URL', 'Rar or Zip in URL']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "reduced_X = all_X[reduced_X_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split( reduced_X, y, test_size=0.30, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = {'kernel':('linear', 'rbf', 'poly'), 'C':[1, 10]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = GridSearchCV(SVC(gamma='scale'), parameters, cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid={'kernel': ('linear', 'rbf', 'poly'), 'C': [1, 10]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.62x0.755)\n",
      "+-----------------+--------------+----------+----------+\n",
      "| Precision Score | Recall Score | f1 Score | Accuracy |\n",
      "+-----------------+--------------+----------+----------+\n",
      "|      0.9693     |    0.9689    |  0.9689  |  0.9689  |\n",
      "+-----------------+--------------+----------+----------+\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gen_scores(y_test, pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid={'kernel': ('linear', 'rbf', 'poly'), 'C': [1, 10]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gen_model = clf\n",
    "gfs = GeneticFS()\n",
    "\n",
    "# fit the optimizer\n",
    "gfs.fit(model=gen_model, _type='classification', X=all_X, y=y) # classification model\n",
    "\n",
    "# get results output\n",
    "binary_output_of_optimal_variables, indicies_of_optimal_variables = gfs.results()\n",
    "\n",
    "# plot results of progress\n",
    "gfs.plot_progress()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1943: FutureWarning: You should specify a value for 'cv' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1943: FutureWarning: You should specify a value for 'cv' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1943: FutureWarning: You should specify a value for 'cv' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1943: FutureWarning: You should specify a value for 'cv' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1943: FutureWarning: You should specify a value for 'cv' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1943: FutureWarning: You should specify a value for 'cv' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "clist = [0.01,0.1,1,10,100,1000]\n",
    "for c in clist: \n",
    "    lr = LogisticRegression(penalty='l2', solver='saga')\n",
    "    clf = GridSearchCV(lr,param_grid={'C':clist}, scoring='f1_weighted')\n",
    "    clf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=None, solver='saga',\n",
       "          tol=0.0001, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.62x0.755)\n",
      "+-----------------+--------------+----------+----------+\n",
      "| Precision Score | Recall Score | f1 Score | Accuracy |\n",
      "+-----------------+--------------+----------+----------+\n",
      "|      0.9129     |    0.9129    |  0.9129  |  0.9129  |\n",
      "+-----------------+--------------+----------+----------+\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAD8CAYAAABJsn7AAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAFGFJREFUeJzt3XmQnVWdh/Hn1x1CWJQQ1pBkSISggJqE0YjDjGBQNq2J1oiCwzIMRSMSBzSMoFCKC6MpRsJkHCNhDSoia5HCDE6MC6ZkCxDCEjAtYOgsRAhhEQLpvmf+uC/mip3bt0N3n9w3z4c61e8973aaSr4cznvOeyOlhCRp4LXkboAkbakMYEnKxACWpEwMYEnKxACWpEwMYEnKxACWpEwMYEnKxACWpEwG9fcNKqv2camd/srhe4zL3QRthuZVro83e43eZE7L7r970/d7M+wBS1ImBrCkUqn04p96ImJIRNwdEQ9ExMMR8bWifkxE3BURSyPiJxExuKjfuvjcXuwf3VNbDWBJpbI+dTVcevAqMCmlNA4YDxwREQcC04DpKaWxwHPAycXxJwPPpZT2BqYXx9VlAEsqlb7qAaeql4qPWxUlAZOAG4r62cDHiu3JxWeK/YdGRN0xZgNYUql0pdRw6UlEtEbEImA1MA/4PbA2pdRZHNIBjCi2RwBPART7nwd2qnd9A1hSqVRIDZeIaIuIhTWlrfZaKaWulNJ4YCQwEdi3m1u+nuTd9Xbrpny/T0OTpIHUVT/z/kJKaRYwq4Hj1kbEr4ADgaERMajo5Y4EVhSHdQCjgI6IGATsAKypd117wJJKpTc94HoiYpeIGFpsbwN8CFgC/BL4RHHYicAtxfac4jPF/l+kHr5yyB6wpFJZ33dfszYcmB0RrVQ7q9ellG6NiEeAayPim8D9wOXF8ZcDP4iIdqo932N6uoEBLKlUejMEUU9KaTEwoZv6x6mOB7+xfh1wdG/uYQBLKpWuJnr5gQEsqVTqz+7dvBjAkkqlq9vZYJsnA1hSqaxPBrAkZWEPWJIyqdgDlqQ87AFLUiZdTbTA1wCWVCoOQUhSJq+l1txNaJgBLKlUKg5BSFIePoSTpEy6kj1gScqiYg9YkvJ4LTVPrDVPSyWpAT6Ek6RMupwHLEl5uBJOkjKpOAtCkvKwByxJmax3KbIk5eFCDEnKxIUYkpSJPWBJysSHcJKUiS9kl6RM1vsuCEnKw/cBS1ImroSTpEyaqQfcPP+pkKQGVFJLw6WeiBgVEb+MiCUR8XBEnFHUnx8RyyNiUVGOqjnnSxHRHhGPRcThPbXVHrCkUunDpcidwNSU0n0R8Rbg3oiYV+ybnlL6z9qDI2I/4Bhgf2AP4OcRsU9KqWtjNzCAJZVKXy3ESCmtBFYW2y9GxBJgRJ1TJgPXppReBZ6IiHZgInDHxk5wCEJSqVRSNFwaFRGjgQnAXUXVlIhYHBFXRMSORd0I4Kma0zqoH9gGsKRy6aKl4RIRbRGxsKa0vfF6EbE9cCNwZkrpBWAmsBcwnmoP+TuvH9pNc1K9tjoEIalUetOzTSnNAmZtbH9EbEU1fH+UUrqpOOfpmv2XArcWHzuAUTWnjwRW1Lu/PWBJpVKhpeFST0QEcDmwJKV0UU398JrDPg48VGzPAY6JiK0jYgwwFri73j3sAUsqlfWVPutXHgQcDzwYEYuKui8Dx0bEeKrDC08CpwKklB6OiOuAR6jOoDi93gwIMIAllUxfrYRLKS2g+3HduXXOuQC4oNF7GMB95NVX4fh/g9fWQ2cXHH4wfO5foWMlTP0arH0B9tsHpp0Lg7eCb30X7r6/eu4r62DNWrj7p3l/B/W/XUbuxBdnT2HY7kOpVBJzL/05N8+Yy17jRnPGzFMYPGQwXZ1dzDj9Mh67pz13c5tSM62EM4D7yODBcOV02G5bWN8Jx02Bf3gfzL4OTjgaPnIonP8duPGncOzH4EtTNpz7wxthydJ8bdfA6ers4pKzrqb9/ifYZvshfG/hNO6dt5hTph3HD75+PffctoiJR07glGnHcdak83M3tyk10+soe+yrR8Q7IuLsiJgREf9VbO87EI1rJhHV8AXo7KyGcATceX+1Nwww+XCYv+Cvz/3pfDjq0IFrq/JZs2ot7fc/AcArL61j2ZLl7DxiGCkltn1r9Q/Qdjtsy7MrnsvZzKbWV0uRB0LdHnBEnA0cC1zLhqd5I4EfR8S1KaVv93P7mkpXF3yiDZYtr/Zy/2YPeOv2MKj4t7z7rvD0M395zvJV1WGKAw8Y+PYqr9323IW9J4zh0buWMvPzV/Gt286j7cLjaWlp4YyDzs3dvKZVpu+EOxnYP6W0vrYyIi4CHgYM4BqtrXDz5fDCi/C58+D3f/jrY974R2PuL6o95Nbm+SZt9YEh2w3hKzecxczPX8nLL77CR087jJlfuIoFN93FB45+P1MvO42zD/tG7mY2pfWV5vnL1FMfvEL1pRJvNLzY163a1SWzfvD8m2lfU3rrW2DiBHjgEXjhpeqQBMCq1bDrzn957P/Oh498aODbqHxaB7Xy1Rum8otrfsOCm6v/Y3nYCYew4KbqKtfbr7+Dt0/cO2cTm1p/LEXuLz31gM8E5kfEUjascf4bYG9gysZOql1dUlm1T92leGWxZi0Maq2G77pX4Y6FcPKn4X3j4We/rj6Eu+VnMOmgDec8sQyefwnG75+v3Rp4Uy87jWWPLufG6bf+ue7ZFWt498H7sfjXjzBh0jtZvnRVxhY2t9IMQaSUbouIfai+0WcE1f+D7gDu6WmC8Zbmj8/Cl/4DuipQSXDEIfDBv4O9R1enoc24HPbdGz7xkQ3n/HQ+HDWp+rBOW4b9D3oHHz7hYB5f/Ae+f9+FAFxx7jVc1HYJn734JFoHtfDauvVcfOolmVvavDaHnm2jIqX+7aBuKT1g9c7he4zL3QRthuZVrn/T6fmpOz7TcOb85P3fz5rWzgOWVCqdm8H0skYZwJJKpZmGIAxgSaViAEtSJgawJGViAEtSJqWZByxJzaaz717I3u8MYEml4hCEJGViAEtSJskAlqQ8fAgnSZk4BCFJmXQ5C0KS8nAMWJIycQhCkjLp51ec9ykDWFKpOAtCkjLxIZwkZeIQhCRl4iwIScqkmQK4eQZLJKkBlRQNl3oiYlRE/DIilkTEwxFxRlE/LCLmRcTS4ueORX1ExIyIaI+IxRFxQE9tNYAllUpKjZcedAJTU0r7AgcCp0fEfsA5wPyU0lhgfvEZ4EhgbFHagJk93cAAllQqlUpLw6WelNLKlNJ9xfaLwBJgBDAZmF0cNhv4WLE9Gbg6Vd0JDI2I4fXuYQBLKpXUi9KoiBgNTADuAnZLKa2EakgDuxaHjQCeqjmto6jbKANYUqmkFA2XiGiLiIU1pe2N14uI7YEbgTNTSi/UuXV3g8p1c95ZEJLKpRdd25TSLGDWxvZHxFZUw/dHKaWbiuqnI2J4SmllMcSwuqjvAEbVnD4SWFHv/vaAJZVKb3rA9UREAJcDS1JKF9XsmgOcWGyfCNxSU39CMRviQOD514cqNsYesKRSqVT6bB7wQcDxwIMRsaio+zLwbeC6iDgZWAYcXeybCxwFtAMvAyf1dAMDWFK59NFCjJTSArof1wU4tJvjE3B6b+5hAEsqFd8FIUm5GMCSlEczvQvCAJZULvaAJSmP1HezIPqdASypZAxgScrDIQhJysQAlqRMnAUhSXm4EEOScnEWhCTlEfaAJSkTA1iSMvEhnCRlYg9YkjKp5G5A4wxgSeXiEIQk5eEsCEnKpYkC2G9FlqRM+r0HfPge4/r7FmpC5z3+QO4mqKQcgpCkXFyKLEmZ2AOWpDwcgpCkXAxgScrEAJakPByCkKRcnAUhSXnYA5akXJoogF2KLKlUIjVeerxWxBURsToiHqqpOz8ilkfEoqIcVbPvSxHRHhGPRcThPV3fAJZULqkXpWdXAUd0Uz89pTS+KHMBImI/4Bhg/+Kc70VEa72LG8CSSiUqjZeepJRuB9Y0eOvJwLUppVdTSk8A7cDEeicYwJLUe1MiYnExRLFjUTcCeKrmmI6ibqMMYEnl0oshiIhoi4iFNaWtgTvMBPYCxgMrge8U9d3Nf6s70OEsCEml0ptpaCmlWcCs3lw/pfT0n+8VcSlwa/GxAxhVc+hIYEW9a9kDllQuffsQ7q9ExPCajx8HXp8hMQc4JiK2jogxwFjg7nrXsgcsqVz6cB5wRPwYOATYOSI6gK8Ch0TE+OJOTwKnAqSUHo6I64BHgE7g9JRSV73rG8CSSqWR2Q2NSikd20315XWOvwC4oNHrG8CSSsWlyJKUiwEsSZkYwJKUh0MQkpSLASxJefTlLIj+ZgBLKhd7wJKUh2PAkpSLASxJmRjAkpSHQxCSlIkBLEm5GMCSlIkBLEl5OAQhSbkYwJKUh0uRJSkThyAkKRcDWJIyMYAlKQ+HICQpk6g0TwIbwJLKpXny1wCWVC4OQUhSLgawJOVhD1iScjGAJSkPlyJLUiYOQUhSLql5ErgldwMkqS9Farz0eK2IKyJidUQ8VFM3LCLmRcTS4ueORX1ExIyIaI+IxRFxQE/XtwfcT3YZuRNfnD2FYbsPpVJJzL3059w8Yy57jRvNGTNPYfCQwXR1djHj9Mt47J723M1VP1mzGq64EF54DiLgA0fBoR+Hp34PP5wB61+D1lb49BQY845q5+0nM+HBu2HwEPiXqbDn2Ny/RZPp2w7wVcB3gatr6s4B5qeUvh0R5xSfzwaOBMYW5X3AzOLnRhnA/aSrs4tLzrqa9vufYJvth/C9hdO4d95iTpl2HD/4+vXcc9siJh45gVOmHcdZk87P3Vz1k5ZWOLqtGqLrXoZvToF9D4AbLoOPHgfvem81bG+8HM66EB66B55eDt+8Ep54FH703/DlGbl/i+bSlw/hUkq3R8ToN1RPBg4ptmcDv6IawJOBq1NKCbgzIoZGxPCU0sqNXd8A7idrVq1lzaq1ALzy0jqWLVnOziOGkVJi27duC8B2O2zLsyuey9lM9bOhO1ULwJBtYfgoWPtMtTe87k/V+lf+BEOHVbcX3QHv/1B1/9v2re5b++yGa6hnAzALYrfXQzWltDIidi3qRwBP1RzXUdT1fQBHxEkppSs39fwtyW577sLeE8bw6F1Lmfn5q/jWbefRduHxtLS0cMZB5+ZungbIM6tg2e+rQw2f+gxc/GW44dLqsMPZ06vHrH0Gdtxlwzk77mwA91ovHsJFRBvQVlM1K6U0axPvHN21pt4Jb+Yh3Nc22oqItohYGBELO9Ljb+IWzW/IdkP4yg1nMfPzV/Lyi6/w0dMOY+YXruKf9zyNmV+4iqmXnZa7iRoA616B73+jGrzbbAe/vhU+eSpM+1H15+yLqsd197c1uvtrrY3qzUO4lNKslNJ7akoj4ft0RAwHKH6uLuo7gFE1x40EVtS7UN0ALp7kdVceBHbb2Hm1v9TIeFsDv085tQ5q5as3TOUX1/yGBTffDcBhJxzCgpvuAuD26+/g7RP3ztlEDYDOzmr4vm8SHPD31brfztuw/bcfgCd/V93ecWd47o8bzn3uGdhh2MC2t+mlXpRNMwc4sdg+Ebilpv6EYjbEgcDz9cZ/oechiN2Aw4E3DlQG8NteNXkLNPWy01j26HJunH7rn+ueXbGGdx+8H4t//QgTJr2T5UtXZWyh+ltKcPVF1bHfD//ThvqhO8HvFsPbx8Gji2DXPar14w6EX86B9x5SfQi3zbYOP/RWXy7EiIgfU33gtnNEdABfBb4NXBcRJwPLgKOLw+cCRwHtwMvAST1dv6cAvhXYPqW0qJuG/aqxX2HLtP9B7+DDJxzM44v/wPfvuxCAK869hovaLuGzF59E66AWXlu3notPvSRzS9Wf2h+GO+cHI8Ykvl6MNn38JDj+zOp0s0oXDBpc/QzwronVmRDnngSDt65OQ1Pv9OUL2VNKx25k16HdHJuA03tz/Uj9vGrkwy1HN8+yFA2Y8x5/IHcTtBk6ePTv3vSI9wf+8cKGM+f2Of+edYTdaWiSSsV3QUhSLn4nnCRl0jz5awBLKheHICQpE7+WXpJyaZ78NYAllUs00QvZDWBJ5eJ3wklSHvaAJSmX5slfA1hSuTgLQpJycQhCkvIYgK8k6jMGsKRysQcsSZk0T/4awJLKJSrNMwZhAEsql+bJXwNYUrm4EEOScjGAJSkTA1iSMnEMWJLycBaEJOXiEIQkZWIAS1ImzTMCYQBLKhfnAUtSLgawJGXS1TxjEAawpHKxByxJmfRhAEfEk8CLQBfQmVJ6T0QMA34CjAaeBD6ZUnpuU67f0jfNlKTNRCU1XhrzwZTS+JTSe4rP5wDzU0pjgfnF501iAEsql1RpvGyaycDsYns28LFNvZABLKlcuiqNl54l4P8i4t6IaCvqdksprQQofu66qU11DFhSufRiDLgI1baaqlkppVk1nw9KKa2IiF2BeRHxaB+1EjCAJZVNLwK4CNtZdfavKH6ujoibgYnA0xExPKW0MiKGA6s3takOQUgql5QaL3VExHYR8ZbXt4HDgIeAOcCJxWEnArdsalPtAUsql757HeVuwM0RAdWsvCaldFtE3ANcFxEnA8uAozf1BgawpHLpo3nAKaXHgXHd1D8LHNoX9zCAJZWLS5ElKY+06fN7B5wBLKlcGl/hlp0BLKlcfBmPJGXil3JKUib2gCUpj9TVlbsJDTOAJZWLD+EkKROnoUlSHskesCRlYg9YkvJopodwkZpoykazi4i2N7zsWfLPxRbM9wEPrLaeD9EWyD8XWygDWJIyMYAlKRMDeGA5zqfu+OdiC+VDOEnKxB6wJGViAA+QiDgiIh6LiPaIOCd3e5RfRFwREasj4qHcbVEeBvAAiIhW4H+AI4H9gGMjYr+8rdJm4CrgiNyNUD4G8MCYCLSnlB5PKb0GXAtMztwmZZZSuh1Yk7sdyscAHhgjgKdqPncUdZK2YAbwwIhu6px+Im3hDOCB0QGMqvk8EliRqS2SNhMG8MC4BxgbEWMiYjBwDDAnc5skZWYAD4CUUicwBfgZsAS4LqX0cN5WKbeI+DFwB/D2iOiIiJNzt0kDy5VwkpSJPWBJysQAlqRMDGBJysQAlqRMDGBJysQAlqRMDGBJysQAlqRM/h/J9RsJE1LarwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gen_scores(y_test, pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1943: FutureWarning: You should specify a value for 'cv' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1943: FutureWarning: You should specify a value for 'cv' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1943: FutureWarning: You should specify a value for 'cv' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1943: FutureWarning: You should specify a value for 'cv' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1943: FutureWarning: You should specify a value for 'cv' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1943: FutureWarning: You should specify a value for 'cv' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\prabh\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:334: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "clist = [0.01,0.1,1,10,100,1000]\n",
    "for c in clist: \n",
    "    lr = LogisticRegression(penalty='l2', solver='saga')\n",
    "    clf = GridSearchCV(lr,param_grid={'C':clist}, scoring='f1_weighted')\n",
    "    clf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.62x0.755)\n",
      "+-----------------+--------------+----------+----------+\n",
      "| Precision Score | Recall Score | f1 Score | Accuracy |\n",
      "+-----------------+--------------+----------+----------+\n",
      "|      0.8792     |    0.8787    |  0.8785  |  0.8787  |\n",
      "+-----------------+--------------+----------+----------+\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gen_scores(y_test, pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}