AllFeatures.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1022,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "sys.path.append(os.getcwd() + '/ML')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1023,
   "metadata": {},
   "outputs": [],
   "source": [
    "df= pd.read_csv(r\"./timezone-PA-B.csv\", sep=',')\n",
    "df = df.drop_duplicates(subset=['iid'])\n",
    "dfg= pd.read_csv(r\"./PA-AgeGender-Predicted.csv\", sep=',')\n",
    "dfg = dfg.drop_duplicates(subset=['iid'])\n",
    "dfg['user.id']=dfg['user.id'].astype(\"category\")\n",
    "main= pd.read_csv(r\"./PA-All.csv\", sep=',')\n",
    "main = main.drop_duplicates('iid', keep='first')\n",
    "#main['user.id']=main['user.id'].astype('str')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1024,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(47079, 31)"
      ]
     },
     "execution_count": 1024,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1025,
   "metadata": {},
   "outputs": [],
   "source": [
    "R1= df[['iid', 'A1', 'A2', 'A3', 'truth', 'text', 'hour', 'weekday', 'month']]\n",
    "R2= main[['iid', 'user.id']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1026,
   "metadata": {},
   "outputs": [],
   "source": [
    "M=pd.merge(R2, R1, on='iid', how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1027,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3998, 10)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>iid</th>\n",
       "      <th>user.id</th>\n",
       "      <th>A1</th>\n",
       "      <th>A2</th>\n",
       "      <th>A3</th>\n",
       "      <th>truth</th>\n",
       "      <th>text</th>\n",
       "      <th>hour</th>\n",
       "      <th>weekday</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>@murphdog47 Sad to see hockey hat guy go but y...</td>\n",
       "      <td>22</td>\n",
       "      <td>Sat</td>\n",
       "      <td>02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>2.660984e+08</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>You’re very welcome! Thank you for bringing ho...</td>\n",
       "      <td>22</td>\n",
       "      <td>Sun</td>\n",
       "      <td>02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5ece814af46b2b34fcc9528a</td>\n",
       "      <td>1.255151e+06</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Random #productivity hacks continued:\\n\\n🎽 On ...</td>\n",
       "      <td>21</td>\n",
       "      <td>Sat</td>\n",
       "      <td>02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5ece815ef46b2b34fccd1ae2</td>\n",
       "      <td>1.038975e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Now they’ll say a few useless scripted platitu...</td>\n",
       "      <td>22</td>\n",
       "      <td>Sat</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>2.293366e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A simple exercise you must do everyday to keep...</td>\n",
       "      <td>22</td>\n",
       "      <td>Sat</td>\n",
       "      <td>02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        iid       user.id  A1  A2  A3  truth  \\\n",
       "0  5ece812df46b2b34fcc352ac  1.280033e+09   0   0   0      0   \n",
       "1  5ece812ef46b2b34fcc3a0be  2.660984e+08   0   0   0      0   \n",
       "2  5ece814af46b2b34fcc9528a  1.255151e+06   0   1   0      0   \n",
       "3  5ece815ef46b2b34fccd1ae2  1.038975e+09   0   0   0      0   \n",
       "4  5ece8169f46b2b34fccf47ef  2.293366e+09   0   0   0      0   \n",
       "\n",
       "                                                text hour weekday month  \n",
       "0  @murphdog47 Sad to see hockey hat guy go but y...   22     Sat    02  \n",
       "1  You’re very welcome! Thank you for bringing ho...   22     Sun    02  \n",
       "2  Random #productivity hacks continued:\\n\\n🎽 On ...   21     Sat    02  \n",
       "3  Now they’ll say a few useless scripted platitu...   22     Sat    02  \n",
       "4  A simple exercise you must do everyday to keep...   22     Sat    02  "
      ]
     },
     "execution_count": 1027,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(M.shape)\n",
    "M.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1028,
   "metadata": {},
   "outputs": [],
   "source": [
    "del dfg['iid']\n",
    "M2=pd.merge(M, dfg, on='user.id', how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1029,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>iid</th>\n",
       "      <th>user.id</th>\n",
       "      <th>A1</th>\n",
       "      <th>A2</th>\n",
       "      <th>A3</th>\n",
       "      <th>truth</th>\n",
       "      <th>text</th>\n",
       "      <th>hour</th>\n",
       "      <th>weekday</th>\n",
       "      <th>month</th>\n",
       "      <th>...</th>\n",
       "      <th>30-39</th>\n",
       "      <th>&lt;=18</th>\n",
       "      <th>&gt;=40</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "      <th>is-org</th>\n",
       "      <th>non-org</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>org</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>5ece812df46b2b34fcc352ac</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>@murphdog47 Sad to see hockey hat guy go but y...</td>\n",
       "      <td>22</td>\n",
       "      <td>Sat</td>\n",
       "      <td>02</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5846</td>\n",
       "      <td>0.0242</td>\n",
       "      <td>0.2901</td>\n",
       "      <td>0.0182</td>\n",
       "      <td>0.9818</td>\n",
       "      <td>0.8298</td>\n",
       "      <td>0.1702</td>\n",
       "      <td>30-39</td>\n",
       "      <td>male</td>\n",
       "      <td>is-org</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5ece812ef46b2b34fcc3a0be</td>\n",
       "      <td>2.66098e+08</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>You’re very welcome! Thank you for bringing ho...</td>\n",
       "      <td>22</td>\n",
       "      <td>Sun</td>\n",
       "      <td>02</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2499</td>\n",
       "      <td>0.0388</td>\n",
       "      <td>0.0740</td>\n",
       "      <td>0.0011</td>\n",
       "      <td>0.9989</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>19-29</td>\n",
       "      <td>male</td>\n",
       "      <td>non-org</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5ece814af46b2b34fcc9528a</td>\n",
       "      <td>1.25515e+06</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Random #productivity hacks continued:\\n\\n🎽 On ...</td>\n",
       "      <td>21</td>\n",
       "      <td>Sat</td>\n",
       "      <td>02</td>\n",
       "      <td>...</td>\n",
       "      <td>0.9023</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>0.9998</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>30-39</td>\n",
       "      <td>female</td>\n",
       "      <td>non-org</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5ece815ef46b2b34fccd1ae2</td>\n",
       "      <td>1.03898e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Now they’ll say a few useless scripted platitu...</td>\n",
       "      <td>22</td>\n",
       "      <td>Sat</td>\n",
       "      <td>02</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0112</td>\n",
       "      <td>0.0073</td>\n",
       "      <td>0.9664</td>\n",
       "      <td>0.0017</td>\n",
       "      <td>0.9983</td>\n",
       "      <td>0.0028</td>\n",
       "      <td>0.9972</td>\n",
       "      <td>&gt;=40</td>\n",
       "      <td>male</td>\n",
       "      <td>non-org</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5ece8169f46b2b34fccf47ef</td>\n",
       "      <td>2.29337e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A simple exercise you must do everyday to keep...</td>\n",
       "      <td>22</td>\n",
       "      <td>Sat</td>\n",
       "      <td>02</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0677</td>\n",
       "      <td>0.0077</td>\n",
       "      <td>0.9071</td>\n",
       "      <td>0.1231</td>\n",
       "      <td>0.8769</td>\n",
       "      <td>0.7731</td>\n",
       "      <td>0.2269</td>\n",
       "      <td>&gt;=40</td>\n",
       "      <td>male</td>\n",
       "      <td>is-org</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        iid      user.id  A1  A2  A3  truth  \\\n",
       "0  5ece812df46b2b34fcc352ac  1.28003e+09   0   0   0      0   \n",
       "1  5ece812ef46b2b34fcc3a0be  2.66098e+08   0   0   0      0   \n",
       "2  5ece814af46b2b34fcc9528a  1.25515e+06   0   1   0      0   \n",
       "3  5ece815ef46b2b34fccd1ae2  1.03898e+09   0   0   0      0   \n",
       "4  5ece8169f46b2b34fccf47ef  2.29337e+09   0   0   0      0   \n",
       "\n",
       "                                                text hour weekday month  ...  \\\n",
       "0  @murphdog47 Sad to see hockey hat guy go but y...   22     Sat    02  ...   \n",
       "1  You’re very welcome! Thank you for bringing ho...   22     Sun    02  ...   \n",
       "2  Random #productivity hacks continued:\\n\\n🎽 On ...   21     Sat    02  ...   \n",
       "3  Now they’ll say a few useless scripted platitu...   22     Sat    02  ...   \n",
       "4  A simple exercise you must do everyday to keep...   22     Sat    02  ...   \n",
       "\n",
       "    30-39    <=18    >=40  female    male  is-org  non-org    age     sex  \\\n",
       "0  0.5846  0.0242  0.2901  0.0182  0.9818  0.8298   0.1702  30-39    male   \n",
       "1  0.2499  0.0388  0.0740  0.0011  0.9989  0.0000   1.0000  19-29    male   \n",
       "2  0.9023  0.0002  0.0010  0.9998  0.0002  0.0000   1.0000  30-39  female   \n",
       "3  0.0112  0.0073  0.9664  0.0017  0.9983  0.0028   0.9972   >=40    male   \n",
       "4  0.0677  0.0077  0.9071  0.1231  0.8769  0.7731   0.2269   >=40    male   \n",
       "\n",
       "       org  \n",
       "0   is-org  \n",
       "1  non-org  \n",
       "2  non-org  \n",
       "3  non-org  \n",
       "4   is-org  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 1029,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1030,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3457, 26)"
      ]
     },
     "execution_count": 1030,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1031,
   "metadata": {},
   "outputs": [],
   "source": [
    "# M2=M2[['iid','A1', 'A2', 'A3', 'age', 'sex', 'org','hour','weekday','text', 'month']]\n",
    "M2=M2[['iid','A1', 'A2', 'A3', 'age', 'sex', 'org','hour','weekday', 'month']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1032,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>weekday</th>\n",
       "      <th>month</th>\n",
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       "      <td>30-39</td>\n",
       "      <td>male</td>\n",
       "      <td>is-org</td>\n",
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       "      <td>Sat</td>\n",
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       "    </tr>\n",
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       "      <th>1</th>\n",
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       "      <td>0</td>\n",
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       "      <td>19-29</td>\n",
       "      <td>male</td>\n",
       "      <td>non-org</td>\n",
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       "      <td>02</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5ece814af46b2b34fcc9528a</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30-39</td>\n",
       "      <td>female</td>\n",
       "      <td>non-org</td>\n",
       "      <td>21</td>\n",
       "      <td>Sat</td>\n",
       "      <td>02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5ece815ef46b2b34fccd1ae2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>&gt;=40</td>\n",
       "      <td>male</td>\n",
       "      <td>non-org</td>\n",
       "      <td>22</td>\n",
       "      <td>Sat</td>\n",
       "      <td>02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5ece8169f46b2b34fccf47ef</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>&gt;=40</td>\n",
       "      <td>male</td>\n",
       "      <td>is-org</td>\n",
       "      <td>22</td>\n",
       "      <td>Sat</td>\n",
       "      <td>02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        iid  A1  A2  A3    age     sex      org hour weekday  \\\n",
       "0  5ece812df46b2b34fcc352ac   0   0   0  30-39    male   is-org   22     Sat   \n",
       "1  5ece812ef46b2b34fcc3a0be   0   0   0  19-29    male  non-org   22     Sun   \n",
       "2  5ece814af46b2b34fcc9528a   0   1   0  30-39  female  non-org   21     Sat   \n",
       "3  5ece815ef46b2b34fccd1ae2   0   0   0   >=40    male  non-org   22     Sat   \n",
       "4  5ece8169f46b2b34fccf47ef   0   0   0   >=40    male   is-org   22     Sat   \n",
       "\n",
       "  month  \n",
       "0    02  \n",
       "1    02  \n",
       "2    02  \n",
       "3    02  \n",
       "4    02  "
      ]
     },
     "execution_count": 1032,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1033,
   "metadata": {},
   "outputs": [],
   "source": [
    "M2.to_csv(\"PA.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1034,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3457, 12)\n"
     ]
    },
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      "text/plain": [
       "                        iid  A1  A2  A3    age     sex      org hour weekday  \\\n",
       "0  5ece812df46b2b34fcc352ac   0   0   0  30-39    male   is-org   22     Sat   \n",
       "1  5ece812ef46b2b34fcc3a0be   0   0   0  19-29    male  non-org   22     Sun   \n",
       "2  5ece814af46b2b34fcc9528a   0   1   0  30-39  female  non-org   21     Sat   \n",
       "3  5ece815ef46b2b34fccd1ae2   0   0   0   >=40    male  non-org   22     Sat   \n",
       "4  5ece8169f46b2b34fccf47ef   0   0   0   >=40    male   is-org   22     Sat   \n",
       "\n",
       "  month  result  truth  \n",
       "0    02       0      0  \n",
       "1    02       0      0  \n",
       "2    02       0      0  \n",
       "3    02       0      0  \n",
       "4    02       0      0  "
      ]
     },
     "execution_count": 1034,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PAMVB= pd.read_csv('PA-MV-B.csv')\n",
    "final=pd.merge(M2, PAMVB, on='iid', how='inner')\n",
    "\n",
    "print(final.shape)\n",
    "final.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1035,
   "metadata": {},
   "outputs": [],
   "source": [
    "final.to_csv('PA-MV-LSTM.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------------------------------------\n",
    "\n",
    "### I need to generate a separate CSV file to be able to test the number of FN's between inference methods and ML models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1036,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
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       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
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       "            window._Plotly = Plotly;\n",
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       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import getpass\n",
    "import plotly.graph_objs as go\n",
    "from plotly.offline import iplot, init_notebook_mode\n",
    "import plotly.io as pio\n",
    "from plotly.graph_objs import *\n",
    "\n",
    "# for configuring connection \n",
    "import os\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "import os\n",
    "\n",
    "\n",
    "from sklearn import linear_model\n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "#configure the notebook for use in offline mode\n",
    "init_notebook_mode(connected=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1037,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "iid        0\n",
       "A1         0\n",
       "A2         0\n",
       "A3         0\n",
       "age        0\n",
       "sex        0\n",
       "org        0\n",
       "hour       0\n",
       "weekday    0\n",
       "month      0\n",
       "result     0\n",
       "truth      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 1037,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=final #we have the MV results too\n",
    "missing_values_count = df.isnull().sum()\n",
    "missing_values_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1038,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
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      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "<ipython-input-1038-29ca79868650>:3: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "<ipython-input-1038-29ca79868650>:4: SettingWithCopyWarning:\n",
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      "\n",
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      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "<ipython-input-1038-29ca79868650>:5: SettingWithCopyWarning:\n",
      "\n",
      "\n",
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      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "<ipython-input-1038-29ca79868650>:6: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "<ipython-input-1038-29ca79868650>:7: SettingWithCopyWarning:\n",
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      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "<ipython-input-1038-29ca79868650>:8: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "<ipython-input-1038-29ca79868650>:9: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "<ipython-input-1038-29ca79868650>:10: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "<ipython-input-1038-29ca79868650>:11: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "<ipython-input-1038-29ca79868650>:12: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n"
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      "text/plain": [
       "                        iid A1 A2 A3    age     sex      org  hour weekday  \\\n",
       "0  5ece812df46b2b34fcc352ac  0  0  0  30-39    male   is-org    22     Sat   \n",
       "1  5ece812ef46b2b34fcc3a0be  0  0  0  19-29    male  non-org    22     Sun   \n",
       "2  5ece814af46b2b34fcc9528a  0  1  0  30-39  female  non-org    21     Sat   \n",
       "3  5ece815ef46b2b34fccd1ae2  0  0  0   >=40    male  non-org    22     Sat   \n",
       "4  5ece8169f46b2b34fccf47ef  0  0  0   >=40    male   is-org    22     Sat   \n",
       "\n",
       "   month result truth  \n",
       "0      2      0     0  \n",
       "1      2      0     0  \n",
       "2      2      0     0  \n",
       "3      2      0     0  \n",
       "4      2      0     0  "
      ]
     },
     "execution_count": 1038,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df= df[df['hour']!='dummy']\n",
    "df['weekday']=df['weekday'].astype(\"category\")\n",
    "df['hour']=df['hour'].astype(\"int\")\n",
    "df['month']=df['month'].astype(\"int\")\n",
    "df['A1']=df['A1'].astype(\"category\")\n",
    "df['A2']=df['A2'].astype(\"category\")\n",
    "df['A3']=df['A3'].astype(\"category\")\n",
    "df['sex']=df['sex'].astype(\"category\")\n",
    "df['age']=df['age'].astype(\"category\")\n",
    "df['org']=df['org'].astype(\"category\")\n",
    "df['truth']=df['truth'].astype(\"category\")\n",
    "df['result']=df['result'].astype(\"category\")\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1039,
   "metadata": {},
   "outputs": [],
   "source": [
    "df= df[df['truth']!=5]\n",
    "df= df[df['A1']!=5]\n",
    "df= df[df['A2']!=5]\n",
    "df= df[df['A3']!=5]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1040,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1]\n",
       "Categories (2, int64): [0, 1]"
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     "execution_count": 1040,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['truth'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1041,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df['truth']=np.where(df['truth'] ==1, 'YY', df['truth'])\n",
    "# df['truth']=np.where(df['truth'] =='2', 'YN', df['truth'])\n",
    "# df['truth']=np.where(df['truth'] =='3', 'NY', df['truth'])\n",
    "# df['truth']=np.where(df['truth'] =='4', 'NN', df['truth'])\n",
    "# #df['truth']=np.where(df['truth'] =='5', 'NC', df['truth'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1042,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>iid</th>\n",
       "      <th>hour</th>\n",
       "      <th>month</th>\n",
       "      <th>truth</th>\n",
       "      <th>A1_1</th>\n",
       "      <th>A2_1</th>\n",
       "      <th>A3_1</th>\n",
       "      <th>age_30-39</th>\n",
       "      <th>age_&lt;=18</th>\n",
       "      <th>age_&gt;=40</th>\n",
       "      <th>sex_male</th>\n",
       "      <th>org_non-org</th>\n",
       "      <th>weekday_Mon</th>\n",
       "      <th>weekday_Sat</th>\n",
       "      <th>weekday_Sun</th>\n",
       "      <th>weekday_Thu</th>\n",
       "      <th>weekday_Tue</th>\n",
       "      <th>weekday_Wed</th>\n",
       "      <th>result_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>3</th>\n",
       "      <td>5ece815ef46b2b34fccd1ae2</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        iid  hour  month truth  A1_1  A2_1  A3_1  age_30-39  \\\n",
       "0  5ece812df46b2b34fcc352ac    22      2     0     0     0     0          1   \n",
       "1  5ece812ef46b2b34fcc3a0be    22      2     0     0     0     0          0   \n",
       "2  5ece814af46b2b34fcc9528a    21      2     0     0     1     0          1   \n",
       "3  5ece815ef46b2b34fccd1ae2    22      2     0     0     0     0          0   \n",
       "4  5ece8169f46b2b34fccf47ef    22      2     0     0     0     0          0   \n",
       "\n",
       "   age_<=18  age_>=40  sex_male  org_non-org  weekday_Mon  weekday_Sat  \\\n",
       "0         0         0         1            0            0            1   \n",
       "1         0         0         1            1            0            0   \n",
       "2         0         0         0            1            0            1   \n",
       "3         0         1         1            1            0            1   \n",
       "4         0         1         1            0            0            1   \n",
       "\n",
       "   weekday_Sun  weekday_Thu  weekday_Tue  weekday_Wed  result_1  \n",
       "0            0            0            0            0         0  \n",
       "1            1            0            0            0         0  \n",
       "2            0            0            0            0         0  \n",
       "3            0            0            0            0         0  \n",
       "4            0            0            0            0         0  "
      ]
     },
     "execution_count": 1042,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.get_dummies(df, prefix=['A1', 'A2', 'A3',  'age', 'sex', 'org', 'weekday', 'result'], columns=['A1', 'A2', 'A3', 'age', 'sex', 'org', 'weekday', 'result'], drop_first=True)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1043,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['iid', 'hour', 'month', 'truth', 'A1_1', 'A2_1', 'A3_1', 'age_30-39',\n",
       "       'age_<=18', 'age_>=40', 'sex_male', 'org_non-org', 'weekday_Mon',\n",
       "       'weekday_Sat', 'weekday_Sun', 'weekday_Thu', 'weekday_Tue',\n",
       "       'weekday_Wed', 'result_1'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 1043,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1044,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "regex = re.compile(r\"\\[|\\]|<|>\", re.IGNORECASE)\n",
    "df=df[['iid','hour', 'month', 'truth', 'A1_1', 'A2_1', 'A3_1', 'age_30-39',\n",
    "       'age_<=18', 'age_>=40', 'sex_male', 'org_non-org', 'weekday_Mon',\n",
    "       'weekday_Sat', 'weekday_Sun', 'weekday_Thu', 'weekday_Tue',\n",
    "       'weekday_Wed', 'result_1']]\n",
    " \n",
    "df.columns = [regex.sub(\"_\", col) if any(x in str(col) for x in set(('[', ']', '<', '>'))) else col for col in df.columns.values]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1045,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['iid', 'hour', 'month', 'truth', 'A1_1', 'A2_1', 'A3_1', 'age_30-39',\n",
       "       'age__=18', 'age__=40', 'sex_male', 'org_non-org', 'weekday_Mon',\n",
       "       'weekday_Sat', 'weekday_Sun', 'weekday_Thu', 'weekday_Tue',\n",
       "       'weekday_Wed', 'result_1'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 1045,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1046,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3442, 19)\n",
      "                           iid  hour  month truth  A1_1  A2_1  A3_1  \\\n",
      "172   5ecea647f46b2b34fc1b2558    15      3     1     0     0     0   \n",
      "2565  5d0ef5c644578a1480caae35    18      6     0     0     0     0   \n",
      "2750  5d21373344578a14a8d02947    15      7     0     1     0     0   \n",
      "2511  5d0ec98344578a1480ea52c7    14      5     0     0     0     0   \n",
      "576   5ec2d5b5388f6a36d4a5e2ce    17      4     1     1     1     1   \n",
      "\n",
      "      age_30-39  age__=18  age__=40  sex_male  org_non-org  weekday_Mon  \\\n",
      "172           0         0         1         0            1            0   \n",
      "2565          1         0         0         1            0            0   \n",
      "2750          0         0         0         0            0            0   \n",
      "2511          0         0         0         1            1            0   \n",
      "576           1         0         0         0            1            0   \n",
      "\n",
      "      weekday_Sat  weekday_Sun  weekday_Thu  weekday_Tue  weekday_Wed  \\\n",
      "172             0            0            0            0            0   \n",
      "2565            0            0            0            0            0   \n",
      "2750            0            0            0            0            0   \n",
      "2511            1            0            0            0            0   \n",
      "576             1            0            0            0            0   \n",
      "\n",
      "      result_1  \n",
      "172          0  \n",
      "2565         0  \n",
      "2750         0  \n",
      "2511         0  \n",
      "576          1  \n",
      "(850, 19)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "#df= pd.read_csv(\"PA-MV-LSTM.csv\", sep=',')\n",
    "print(df.shape)\n",
    "train= df.sample(n=2592)\n",
    "train.to_csv(\"PA-Truth-train.csv\", sep=',', encoding='utf-8')\n",
    "print(train.head())\n",
    "test=df[~df.iid.isin(train.iid)]\n",
    "test.shape\n",
    "test.to_csv(\"PA-Truth-test.csv\", sep=',', encoding='utf-8')\n",
    "print(test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1047,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 1047,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().values.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1048,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['iid', 'hour', 'month', 'truth', 'A1_1', 'A2_1', 'A3_1', 'age_30-39',\n",
       "       'age__=18', 'age__=40', 'sex_male', 'org_non-org', 'weekday_Mon',\n",
       "       'weekday_Sat', 'weekday_Sun', 'weekday_Thu', 'weekday_Tue',\n",
       "       'weekday_Wed', 'result_1'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 1048,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1049,
   "metadata": {},
   "outputs": [],
   "source": [
    "del train['iid']\n",
    "del test ['iid']\n",
    "X=df.drop('truth', 1)\n",
    "y=df['truth']\n",
    "df_cols = list(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1050,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['hour', 'month', 'truth', 'A1_1', 'A2_1', 'A3_1', 'age_30-39',\n",
       "       'age__=18', 'age__=40', 'sex_male', 'org_non-org', 'weekday_Mon',\n",
       "       'weekday_Sat', 'weekday_Sun', 'weekday_Thu', 'weekday_Tue',\n",
       "       'weekday_Wed', 'result_1'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 1050,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1051,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    502\n",
       "1    348\n",
       "Name: truth, dtype: int64"
      ]
     },
     "execution_count": 1051,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.truth.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1053,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop_indices = np.random.choice(train.index, 154, replace=False)\n",
    "# train = train.drop(train[train['truth']==0].sample(544).index)\n",
    "#test = test.drop(test[test['truth']==0].sample(120).index)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1055,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1560\n",
       "1    1032\n",
       "Name: truth, dtype: int64"
      ]
     },
     "execution_count": 1055,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.truth.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----------------------------\n",
    "\n",
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1056,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/zahrashakeri/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning:\n",
      "\n",
      "lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "\n",
      "/Users/zahrashakeri/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:209: FutureWarning:\n",
      "\n",
      "From version 0.24, get_params will raise an AttributeError if a parameter cannot be retrieved as an instance attribute. Previously it would return None.\n",
      "\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8246575342465754\n",
      "f-score: 0.8041095890410959\n",
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "          0       0.90      0.70      0.91      0.79      0.80      0.63       382\n",
      "          1       0.74      0.91      0.70      0.82      0.80      0.66       348\n",
      "\n",
      "avg / total       0.82      0.80      0.81      0.80      0.80      0.64       730\n",
      "\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Length of values does not match length of index",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1056-9e096cc85d67>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     70\u001b[0m \u001b[0my_test1\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m \u001b[0mpredictions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my_test1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m \u001b[0mpredictions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'prob'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my_prob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     73\u001b[0m \u001b[0mpredictions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'y_pred'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__setitem__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   2936\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2937\u001b[0m             \u001b[0;31m# set column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2938\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_item\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2939\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2940\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_setitem_slice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_set_item\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   2998\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2999\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ensure_valid_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3000\u001b[0;31m         \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sanitize_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3001\u001b[0m         \u001b[0mNDFrame\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_item\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3002\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_sanitize_column\u001b[0;34m(self, key, value, broadcast)\u001b[0m\n\u001b[1;32m   3634\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3635\u001b[0m             \u001b[0;31m# turn me into an ndarray\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3636\u001b[0;31m             \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msanitize_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3637\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIndex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3638\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/pandas/core/internals/construction.py\u001b[0m in \u001b[0;36msanitize_index\u001b[0;34m(data, index, copy)\u001b[0m\n\u001b[1;32m    609\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    610\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 611\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Length of values does not match length of index\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    612\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    613\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mABCIndexClass\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Length of values does not match length of index"
     ]
    }
   ],
   "source": [
    "# from sklearn.model_selection import KFold\n",
    "# from sklearn import preprocessing\n",
    "# from sklearn.metrics import f1_score\n",
    "# from yellowbrick.classifier import ROCAUC\n",
    "# from sklearn.linear_model import LogisticRegression\n",
    "# from imblearn.metrics import classification_report_imbalanced\n",
    "# # classes=['YY','YN','NY','NN', 'NC']\n",
    "# classes=['N', 'Y']\n",
    "\n",
    "# kf = KFold(n_splits=2)\n",
    "\n",
    "# for fold, (train_index, test_index) in enumerate(kf.split(X), 1):\n",
    "#     X_train = X.iloc[train_index]\n",
    "#     y_train = y.iloc[train_index]  # Based on your code, you might need a ravel call here, but I would look into how you're generating your y\n",
    "#     X_test = X.iloc[test_index]\n",
    "#     y_test = y.iloc[test_index]  # See comment on ravel and  y_train\n",
    "    \n",
    "    \n",
    "#     model = linear_model.LogisticRegression(C= 2.5,max_iter= 66,tol= 6.75e-05) \n",
    "#     model.fit(X_train, y_train)  \n",
    "#     y_pred = model.predict(X_test)\n",
    "#     visualizer = ROCAUC(model, classes=classes)\n",
    "#     visualizer.fit(X_train, y_train)  # Fit the training data to the visualizer\n",
    "#     visualizer.score(X_test, y_test)  # Evaluate the model on the test data\n",
    "#     visualizer.poof() \n",
    "#     print(f'For fold {fold}:')\n",
    "#     print(f'Accuracy: {model.score(X_test, y_test)}')\n",
    "#     f1=f1_score(y_test, y_pred, average='micro')\n",
    "#     print(f'f-score: {f1}')\n",
    "#     print(classification_report_imbalanced(y_test, y_pred))\n",
    "       \n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn import preprocessing\n",
    "from sklearn.metrics import f1_score\n",
    "from yellowbrick.classifier import ROCAUC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from imblearn.metrics import classification_report_imbalanced\n",
    "# classes=['YY','YN','NY','NN', 'NC']\n",
    "classes=['N', 'Y']\n",
    "\n",
    "\n",
    "X_train = train.drop('truth', 1)\n",
    "y_train = train['truth']  # Based on your code, you might need a ravel call here, but I would look into how you're generating your y\n",
    "X_test = test.drop('truth', 1)\n",
    "y_test = test['truth']  # See comment on ravel and  y_train\n",
    "\n",
    "\n",
    "model = linear_model.LogisticRegression(C= 3,max_iter= 66,tol= 6.75e-05, class_weight='balanced') \n",
    "model.fit(X_train, y_train)  \n",
    "\n",
    "threshold = 0.32\n",
    "\n",
    "y_proba = model.predict_proba(X_test)\n",
    "y_pred = (y_proba [:,1] >= threshold).astype('int')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# y_pred = model.predict(X_test)\n",
    "# y_prob = model.predict_proba(X_test.values)\n",
    "visualizer = ROCAUC(model, classes=classes)\n",
    "visualizer.fit(X_train, y_train)  # Fit the training data to the visualizer\n",
    "visualizer.score(X_test, y_test)  # Evaluate the model on the test data\n",
    "visualizer.poof() \n",
    "\n",
    "print(f'Accuracy: {model.score(X_test, y_test)}')\n",
    "f1=f1_score(y_test, y_pred, average='micro')\n",
    "print(f'f-score: {f1}')\n",
    "print(classification_report_imbalanced(y_test, y_pred))\n",
    "y_test1= y_test.to_frame()\n",
    "predictions=y_test1.copy()\n",
    "predictions['prob']=y_prob.max(axis=1)\n",
    "predictions['y_pred']= y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_out = pd.merge(predictions[['prob', 'y_pred']],test,how = 'left',left_index = True, right_index = True)\n",
    "df_out.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1057,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "con1=(df_out.truth == 0) & (df_out.y_pred== 1)\n",
    "\n",
    "test1=df_out[~(con1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1058,
   "metadata": {},
   "outputs": [],
   "source": [
    "test1.shape\n",
    "del test1['prob']\n",
    "del test1['y_pred']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1059,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Threshold=0.042359,recall=1.000\n",
      "Best Threshold=0.163693, F-Score=0.861\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",
    "#------------------Only the positive class----------\n",
    "y_pred1 = y_proba[:, 1]\n",
    "precision, recall, thresholds = precision_recall_curve(y_test, y_pred1)\n",
    "# plot the roc curve for the model\n",
    "b=1.5\n",
    "fscore = (1+b*b) * ((precision * recall) / (((b*b)*precision) + recall))\n",
    "# locate the index of the largest f score\n",
    "ix = argmax(fscore)\n",
    "iy = argmax(recall)\n",
    "print('Best Threshold=%f,recall=%.3f' % (thresholds[iy], recall[iy]))\n",
    "print('Best Threshold=%f, F-Score=%.3f' % (thresholds[ix], fscore[ix]))\n",
    "# plot the roc curve for the model\n",
    "no_skill = len(y_test[y_test==1]) / len(y_test)\n",
    "pyplot.plot([0,1], [no_skill,no_skill], linestyle='--', label='No Skill')\n",
    "pyplot.plot(recall, precision, marker='.', label='Logistic')\n",
    "pyplot.scatter(recall[ix], precision[ix], marker='o', color='black', label='Best')\n",
    "# axis labels\n",
    "pyplot.xlabel('Recall')\n",
    "pyplot.ylabel('Precision')\n",
    "pyplot.legend()\n",
    "# show the plot\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1060,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_out.to_csv(\"Predictions-LR.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1061,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8463540096906383"
      ]
     },
     "execution_count": 1061,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision, recall, _ = precision_recall_curve(y_test, y_pred)\n",
    "auc_score = auc(recall, precision)\n",
    "auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1062,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.       , 0.9137931, 0.       ])"
      ]
     },
     "execution_count": 1062,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1063,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn import metrics\n",
    "# preds = model.predict_proba(X_test)\n",
    "# tpr, tpr, thresholds = metrics.roc_curve(y_test,preds[:,1])\n",
    "# print (thresholds)\n",
    "\n",
    "# accuracy_ls = []\n",
    "# for thres in thresholds:\n",
    "#     y_pred = np.where(preds[:,1]>thres,1,0)\n",
    "#     # Apply desired utility function to y_preds, for example accuracy.\n",
    "#     accuracy_ls.append(metrics.accuracy_score(y_test, y_pred, normalize=True))\n",
    "#     #print(thres,\"******\",metrics.accuracy_score(y_test, y_pred) )\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "from yellowbrick.classifier import DiscriminationThreshold\n",
    "from yellowbrick.datasets import load_spam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1064,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# visualizer = DiscriminationThreshold(model, fbetafloat=1)\n",
    "\n",
    "# visualizer.fit(X_train, y_train)        # Fit the data to the visualizer\n",
    "# visualizer.show()           # Finalize and render the figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1065,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8089907925618344"
      ]
     },
     "execution_count": 1065,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_auc = roc_auc_score(y_test, y_pred)\n",
    "lr_auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1066,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8029787886824624"
      ]
     },
     "execution_count": 1066,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import fbeta_score\n",
    "fbeta_score(y_test, y_pred, average='macro', beta=1.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1067,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "from sklearn.metrics import confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "import itertools\n",
    "matrix = confusion_matrix(y_test,y_pred)\n",
    "class_names=['0','1']\n",
    "# class_names = ['NN', 'NY', 'YN', 'YY']\n",
    "plt.clf()\n",
    "\n",
    "# place labels at the top\n",
    "plt.gca().xaxis.tick_top()\n",
    "plt.gca().xaxis.set_label_position('top')\n",
    "\n",
    "# plot the matrix per se\n",
    "plt.imshow(matrix, interpolation='nearest', cmap=plt.cm.Blues)\n",
    "\n",
    "# plot colorbar to the right\n",
    "plt.colorbar()\n",
    "\n",
    "fmt = 'd'\n",
    "\n",
    "# write the number of predictions in each bucket\n",
    "thresh = matrix.max() / 2.\n",
    "for i, j in itertools.product(range(matrix.shape[0]), range(matrix.shape[1])):\n",
    "\n",
    "    # if background is dark, use a white number, and vice-versa\n",
    "    plt.text(j, i, format(matrix[i, j], fmt),\n",
    "         horizontalalignment=\"center\",\n",
    "         color=\"white\" if matrix[i, j] > thresh else \"black\")\n",
    "\n",
    "tick_marks = np.arange(len(class_names))\n",
    "plt.xticks(tick_marks, class_names, rotation=45)\n",
    "plt.yticks(tick_marks, class_names)\n",
    "plt.tight_layout()\n",
    "plt.ylabel('True label',size=14)\n",
    "plt.xlabel('Predicted label',size=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1019,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/zahrashakeri/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:209: FutureWarning:\n",
      "\n",
      "From version 0.24, get_params will raise an AttributeError if a parameter cannot be retrieved as an instance attribute. Previously it would return None.\n",
      "\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8411764705882353\n",
      "f-score: 0.8447058823529412\n",
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "          0       0.86      0.89      0.78      0.87      0.83      0.70       509\n",
      "          1       0.83      0.78      0.89      0.80      0.83      0.68       341\n",
      "\n",
      "avg / total       0.84      0.84      0.82      0.84      0.83      0.69       850\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# from sklearn.model_selection import KFold\n",
    "# from sklearn import preprocessing\n",
    "# from sklearn.metrics import f1_score\n",
    "# from yellowbrick.classifier import ROCAUC\n",
    "# from sklearn.linear_model import LogisticRegression\n",
    "# from imblearn.metrics import classification_report_imbalanced\n",
    "# from sklearn.ensemble import RandomForestClassifier\n",
    "# classes=['YY','YN','NY','NN', 'NC']\n",
    "\n",
    "\n",
    "# kf = KFold(n_splits=2)\n",
    "\n",
    "# for fold, (train_index, test_index) in enumerate(kf.split(X), 1):\n",
    "#     X_train = X.iloc[train_index]\n",
    "#     y_train = y.iloc[train_index]  # Based on your code, you might need a ravel call here, but I would look into how you're generating your y\n",
    "#     X_test = X.iloc[test_index]\n",
    "#     y_test = y.iloc[test_index]  # See comment on ravel and  y_train\n",
    "\n",
    "\n",
    "\n",
    "#     model = RandomForestClassifier(n_estimators=400, max_depth= 30, max_features=0.5, criterion='gini', min_samples_split=20) \n",
    "#     model.fit(X_train, y_train)  \n",
    "#     y_pred = model.predict(X_test)\n",
    "#     visualizer = ROCAUC(model, classes=classes)\n",
    "#     visualizer.fit(X_train, y_train)  # Fit the training data to the visualizer\n",
    "#     visualizer.score(X_test, y_test)  # Evaluate the model on the test data\n",
    "#     visualizer.poof(\"RF_Unbalanced_{}.pdf\".format(fold), clear_figure=True) \n",
    "#     print(f'For fold {fold}:')\n",
    "#     print(f'Accuracy: {model.score(X_test, y_test)}')\n",
    "#     f1=f1_score(y_test, y_pred, average='micro')\n",
    "#     print(f'f-score: {f1}')\n",
    "#     print(classification_report_imbalanced(y_test, y_pred))\n",
    "\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn import preprocessing\n",
    "from sklearn.metrics import f1_score\n",
    "from yellowbrick.classifier import ROCAUC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from imblearn.metrics import classification_report_imbalanced\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "classes=['N', 'Y']\n",
    "\n",
    "\n",
    "X_train = train.drop('truth', 1)\n",
    "y_train = train['truth']  # Based on your code, you might need a ravel call here, but I would look into how you're generating your y\n",
    "X_test = test.drop('truth', 1)\n",
    "y_test = test['truth']  # See comment on ravel and  y_train\n",
    "\n",
    "\n",
    "model = RandomForestClassifier(n_estimators=100, max_depth= 30, max_features=.75, criterion='entropy', min_samples_split=15, class_weight='balanced')  \n",
    "model.fit(X_train, y_train)  \n",
    "\n",
    "threshold = .52\n",
    "\n",
    "y_proba = model.predict_proba(X_test)\n",
    "y_pred = (y_proba [:,1] >= threshold).astype('int')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# y_pred = model.predict(X_test)\n",
    "# y_prob = model.predict_proba(X_test.values)\n",
    "visualizer = ROCAUC(model, classes=classes)\n",
    "visualizer.fit(X_train, y_train)  # Fit the training data to the visualizer\n",
    "visualizer.score(X_test, y_test)  # Evaluate the model on the test data\n",
    "visualizer.poof() \n",
    "\n",
    "print(f'Accuracy: {model.score(X_test, y_test)}')\n",
    "f1=f1_score(y_test, y_pred, average='micro')\n",
    "print(f'f-score: {f1}')\n",
    "print(classification_report_imbalanced(y_test, y_pred))\n",
    "y_test1= y_test.to_frame()\n",
    "predictions=y_test1.copy()\n",
    "predictions['prob']=y_prob.max(axis=1)\n",
    "predictions['y_pred']= y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1020,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Threshold=0.000000,recall=1.000\n",
      "Best Threshold=0.339008, F-Score=0.808\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#------------------Only the positive class----------\n",
    "y_pred1 = y_proba[:, 1]\n",
    "precision, recall, thresholds = precision_recall_curve(y_test, y_pred1)\n",
    "# plot the roc curve for the model\n",
    "b=1.1\n",
    "fscore = (1+b*b) * ((precision * recall) / (((b*b)*precision) + recall))\n",
    "# locate the index of the largest f score\n",
    "ix = argmax(fscore)\n",
    "iy = argmax(recall)\n",
    "print('Best Threshold=%f,recall=%.3f' % (thresholds[iy], recall[iy]))\n",
    "print('Best Threshold=%f, F-Score=%.3f' % (thresholds[ix], fscore[ix]))\n",
    "# plot the roc curve for the model\n",
    "no_skill = len(y_test[y_test==1]) / len(y_test)\n",
    "pyplot.plot([0,1], [no_skill,no_skill], linestyle='--', label='No Skill')\n",
    "pyplot.plot(recall, precision, marker='.', label='Logistic')\n",
    "pyplot.scatter(recall[ix], precision[ix], marker='o', color='black', label='Best')\n",
    "# axis labels\n",
    "pyplot.xlabel('Recall')\n",
    "pyplot.ylabel('Precision')\n",
    "pyplot.legend()\n",
    "# show the plot\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1021,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "from sklearn.metrics import confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "import itertools\n",
    "matrix = confusion_matrix(y_test,y_pred)\n",
    "class_names=['0','1']\n",
    "# class_names = ['NN', 'NY', 'YN', 'YY']\n",
    "plt.clf()\n",
    "\n",
    "# place labels at the top\n",
    "plt.gca().xaxis.tick_top()\n",
    "plt.gca().xaxis.set_label_position('top')\n",
    "\n",
    "# plot the matrix per se\n",
    "plt.imshow(matrix, interpolation='nearest', cmap=plt.cm.Blues)\n",
    "\n",
    "# plot colorbar to the right\n",
    "plt.colorbar()\n",
    "\n",
    "fmt = 'd'\n",
    "\n",
    "# write the number of predictions in each bucket\n",
    "thresh = matrix.max() / 2.\n",
    "for i, j in itertools.product(range(matrix.shape[0]), range(matrix.shape[1])):\n",
    "\n",
    "    # if background is dark, use a white number, and vice-versa\n",
    "    plt.text(j, i, format(matrix[i, j], fmt),\n",
    "         horizontalalignment=\"center\",\n",
    "         color=\"white\" if matrix[i, j] > thresh else \"black\")\n",
    "\n",
    "tick_marks = np.arange(len(class_names))\n",
    "plt.xticks(tick_marks, class_names, rotation=45)\n",
    "plt.yticks(tick_marks, class_names)\n",
    "plt.tight_layout()\n",
    "plt.ylabel('True label',size=14)\n",
    "plt.xlabel('Predicted label',size=14)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------------------\n",
    "\n",
    "## XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1001,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[01:38:39] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:516: \n",
      "Parameters: { class_weight } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/zahrashakeri/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:209: FutureWarning:\n",
      "\n",
      "From version 0.24, get_params will raise an AttributeError if a parameter cannot be retrieved as an instance attribute. Previously it would return None.\n",
      "\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8105882352941176\n",
      "f-score: 0.7823529411764706\n",
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "          0       0.86      0.76      0.82      0.81      0.79      0.62       509\n",
      "          1       0.69      0.82      0.76      0.75      0.79      0.62       341\n",
      "\n",
      "avg / total       0.79      0.78      0.79      0.78      0.79      0.62       850\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# from sklearn.model_selection import KFold\n",
    "# from sklearn import preprocessing\n",
    "# from sklearn.metrics import f1_score\n",
    "# from yellowbrick.classifier import ROCAUC\n",
    "# from xgboost import XGBClassifier\n",
    "# from imblearn.metrics import classification_report_imbalanced\n",
    "# from sklearn.ensemble import RandomForestClassifier\n",
    "# classes=['YY','YN','NY','NN', 'NC']\n",
    "\n",
    "\n",
    "# kf = KFold(n_splits=2)\n",
    "\n",
    "# for fold, (train_index, test_index) in enumerate(kf.split(X), 1):\n",
    "#     X_train = X.iloc[train_index]\n",
    "#     y_train = y.iloc[train_index]  # Based on your code, you might need a ravel call here, but I would look into how you're generating your y\n",
    "#     X_test = X.iloc[test_index]\n",
    "#     y_test = y.iloc[test_index]  # See comment on ravel and  y_train\n",
    "\n",
    "#     model = XGBClassifier(max_depth=8, gamma=0.063, colsample_bytree=0.71)\n",
    "#     model.fit(X_train, y_train)  \n",
    "#     y_pred = model.predict(X_test)\n",
    "#     visualizer = ROCAUC(model, classes=classes)\n",
    "#     visualizer.fit(X_train, y_train)  # Fit the training data to the visualizer\n",
    "#     visualizer.score(X_test, y_test)  # Evaluate the model on the test data\n",
    "#     visualizer.poof(\"XGB_Unbalanced_{}.pdf\".format(fold), clear_figure=True) \n",
    "#     print(f'For fold {fold}:')\n",
    "#     print(f'Accuracy: {model.score(X_test, y_test)}')\n",
    "#     f1=f1_score(y_test, y_pred, average='micro')\n",
    "#     print(f'f-score: {f1}')\n",
    "#     print(classification_report_imbalanced(y_test, y_pred))\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.dummy import DummyClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "from sklearn.metrics import auc\n",
    "\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn import preprocessing\n",
    "from sklearn.metrics import auc\n",
    "from sklearn.metrics import f1_score\n",
    "from yellowbrick.classifier import ROCAUC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from imblearn.metrics import classification_report_imbalanced\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "classes=['N', 'Y']\n",
    "\n",
    "\n",
    "X_train = train.drop('truth', 1)\n",
    "y_train = train['truth']  # Based on your code, you might need a ravel call here, but I would look into how you're generating your y\n",
    "X_test = test.drop('truth', 1)\n",
    "y_test = test['truth']  # See comment on ravel and  y_train\n",
    "\n",
    "\n",
    "model = XGBClassifier(max_depth=8, gamma=0.03, colsample_bytree=0.71, class_weight='balanced') \n",
    "model.fit(X_train, y_train)  \n",
    "threshold = 0.2\n",
    "\n",
    "y_proba = model.predict_proba(X_test)\n",
    "y_pred = (y_proba [:,1] >= threshold).astype('int')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# y_pred = model.predict(X_test)\n",
    "# y_prob = model.predict_proba(X_test.values)\n",
    "visualizer = ROCAUC(model, classes=classes)\n",
    "visualizer.fit(X_train, y_train)  # Fit the training data to the visualizer\n",
    "visualizer.score(X_test, y_test)  # Evaluate the model on the test data\n",
    "visualizer.poof() \n",
    "\n",
    "print(f'Accuracy: {model.score(X_test, y_test)}')\n",
    "f1=f1_score(y_test, y_pred, average='micro')\n",
    "print(f'f-score: {f1}')\n",
    "print(classification_report_imbalanced(y_test, y_pred))\n",
    "y_test1= y_test.to_frame()\n",
    "predictions=y_test1.copy()\n",
    "predictions['prob']=y_prob.max(axis=1)\n",
    "predictions['y_pred']= y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1002,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "from sklearn.metrics import confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "import itertools\n",
    "matrix = confusion_matrix(y_test,y_pred)\n",
    "class_names=['0','1']\n",
    "# class_names = ['NN', 'NY', 'YN', 'YY']\n",
    "plt.clf()\n",
    "\n",
    "# place labels at the top\n",
    "plt.gca().xaxis.tick_top()\n",
    "plt.gca().xaxis.set_label_position('top')\n",
    "\n",
    "# plot the matrix per se\n",
    "plt.imshow(matrix, interpolation='nearest', cmap=plt.cm.Blues)\n",
    "\n",
    "# plot colorbar to the right\n",
    "plt.colorbar()\n",
    "\n",
    "fmt = 'd'\n",
    "\n",
    "# write the number of predictions in each bucket\n",
    "thresh = matrix.max() / 2.\n",
    "for i, j in itertools.product(range(matrix.shape[0]), range(matrix.shape[1])):\n",
    "\n",
    "    # if background is dark, use a white number, and vice-versa\n",
    "    plt.text(j, i, format(matrix[i, j], fmt),\n",
    "         horizontalalignment=\"center\",\n",
    "         color=\"white\" if matrix[i, j] > thresh else \"black\")\n",
    "\n",
    "tick_marks = np.arange(len(class_names))\n",
    "plt.xticks(tick_marks, class_names, rotation=45)\n",
    "plt.yticks(tick_marks, class_names)\n",
    "plt.tight_layout()\n",
    "plt.ylabel('True label',size=14)\n",
    "plt.xlabel('Predicted label',size=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 456,
   "metadata": {},
   "outputs": [],
   "source": [
    "precision, recall, thresholds = precision_recall_curve(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 560,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Threshold=0.390996, F-Score=0.807\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = RandomForestClassifier(n_estimators=100, max_depth= 30, max_features=.75, criterion='entropy', min_samples_split=15, class_weight='balanced')  \n",
    "clf=model.fit(X_train, y_train)  \n",
    "y_pred = clf.predict_proba(X_test)\n",
    "# keep probabilities for the positive outcome only\n",
    "y_pred = y_pred[:, 1]\n",
    "# calculate pr-curve\n",
    "precision, recall, thresholds = precision_recall_curve(y_test, y_pred)\n",
    "# plot the roc curve for the model\n",
    "b=1\n",
    "fscore = (1+b*b) * ((precision * recall) / (((b*b)*precision) + recall))\n",
    "# locate the index of the largest f score\n",
    "ix = argmax(fscore)\n",
    "print('Best Threshold=%f, F-Score=%.3f' % (thresholds[ix], fscore[ix]))\n",
    "# plot the roc curve for the model\n",
    "no_skill = len(y_test[y_test==1]) / len(y_test)\n",
    "pyplot.plot([0,1], [no_skill,no_skill], linestyle='--', label='No Skill')\n",
    "pyplot.plot(recall, precision, marker='.', label='Logistic')\n",
    "pyplot.scatter(recall[ix], precision[ix], marker='o', color='black', label='Best')\n",
    "# axis labels\n",
    "pyplot.xlabel('Recall')\n",
    "pyplot.ylabel('Precision')\n",
    "pyplot.legend()\n",
    "# show the plot\n",
    "pyplot.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 445,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Threshold=0.474505, F-Score=0.787\n"
     ]
    }
   ],
   "source": [
    "from numpy import argmax\n",
    "fscore = (2 * precision * recall) / (precision + recall)\n",
    "# locate the index of the largest f score\n",
    "ix = argmax(fscore)\n",
    "print('Best Threshold=%f, F-Score=%.3f' % (thresholds[ix], fscore[ix]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 538,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1       0\n",
      "13      0\n",
      "14      0\n",
      "20      0\n",
      "22      0\n",
      "       ..\n",
      "3435    0\n",
      "3445    1\n",
      "3448    0\n",
      "3451    1\n",
      "3454    0\n",
      "Name: truth, Length: 850, dtype: category\n",
      "Categories (2, int64): [0, 1]\n"
     ]
    }
   ],
   "source": [
    "print(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 542,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 542,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------------------------------------------\n",
    "\n",
    "## Other Truth Inference Techniques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 724,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(850, 4)"
      ]
     },
     "execution_count": 724,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "from sklearn.metrics import f1_score\n",
    "from yellowbrick.classifier import ROCAUC\n",
    "from imblearn.metrics import classification_report_imbalanced\n",
    "\n",
    "df= pd.read_csv(\"Inference Methods/PA-MV-test.csv\", sep=',')\n",
    "df = df.drop_duplicates(subset=['question'])\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 725,
   "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>question</th>\n",
       "      <th>result</th>\n",
       "      <th>truth</th>\n",
       "      <th>dif</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5cf83c1d44578a0664ea03ba</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5cf83d3e44578a066414a4bc</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5cf83fdf44578a06646846b3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5cf845e544578a0664750f34</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5cf8476344578a0664a2ac0e</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   question  result  truth  dif\n",
       "0  5cf83c1d44578a0664ea03ba       0      0    0\n",
       "1  5cf83d3e44578a066414a4bc       0      0    0\n",
       "2  5cf83fdf44578a06646846b3       1      1    0\n",
       "3  5cf845e544578a0664750f34       1      1    0\n",
       "4  5cf8476344578a0664a2ac0e       1      1    0"
      ]
     },
     "execution_count": 725,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 726,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test= df['truth']\n",
    "y_pred= df['result']\n",
    "\n",
    "f1=f1_score(y_test, y_pred, average='micro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 727,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f-score: 0.8494117647058823\n",
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "          0       0.84      0.93      0.72      0.88      0.82      0.69       517\n",
      "          1       0.88      0.72      0.93      0.79      0.82      0.66       333\n",
      "\n",
      "avg / total       0.85      0.85      0.80      0.85      0.82      0.67       850\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(f'f-score: {f1}')\n",
    "print(classification_report_imbalanced(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 728,
   "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>result</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>truth</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>483</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>94</td>\n",
       "      <td>239</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "result    0    1\n",
       "truth           \n",
       "0       483   34\n",
       "1        94  239"
      ]
     },
     "execution_count": 728,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_confusion = pd.crosstab(y_test, y_pred)\n",
    "df_confusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 729,
   "metadata": {},
   "outputs": [],
   "source": [
    "precision, recall, _ = precision_recall_curve(y_test, y_pred)\n",
    "auc_score = auc(recall, precision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 730,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8518819142348555"
      ]
     },
     "execution_count": 730,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 731,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.39176471, 0.87545788, 1.        ])"
      ]
     },
     "execution_count": 731,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 732,
   "metadata": {},
   "outputs": [],
   "source": [
    "precision, recall, thresholds = precision_recall_curve(y_test, y_pred)\n",
    "b=2\n",
    "fscore = (1+b*b) * ((precision * recall) / (((b*b)*precision) + recall))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 734,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.76306141, 0.74454829, 0.        ])"
      ]
     },
     "execution_count": 734,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fscore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [],
   "source": [
    "f1 = f1_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7887788778877889"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot\n",
    "pyplot.plot(recall, precision, marker='.', label='Logistic')\n",
    "# axis labels\n",
    "pyplot.xlabel('Recall')\n",
    "pyplot.ylabel('Precision')\n",
    "# show the legend\n",
    "pyplot.legend()\n",
    "# show the plot\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8259768472534429"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "lr_auc = roc_auc_score(y_test, y_pred)\n",
    "lr_auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_fpr, lr_tpr, _ = roc_curve(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pyplot.plot(lr_fpr, lr_tpr, marker='.', label='Logistic')\n",
    "# axis labels\n",
    "pyplot.xlabel('False Positive Rate')\n",
    "pyplot.ylabel('True Positive Rate')\n",
    "# show the legend\n",
    "pyplot.legend()\n",
    "# show the plot\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}