Naive Bayes, Pipelining, GridSearh, ColumnTransformer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "tennis_data = pd.read_csv('https://raw.githubusercontent.com/edyoda/data-science-complete-tutorial/master/Data/tennis.csv.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>outlook</th>\n",
       "      <th>temp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windy</th>\n",
       "      <th>play</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sunny</td>\n",
       "      <td>hot</td>\n",
       "      <td>high</td>\n",
       "      <td>False</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sunny</td>\n",
       "      <td>hot</td>\n",
       "      <td>high</td>\n",
       "      <td>True</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>overcast</td>\n",
       "      <td>hot</td>\n",
       "      <td>high</td>\n",
       "      <td>False</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>rainy</td>\n",
       "      <td>mild</td>\n",
       "      <td>high</td>\n",
       "      <td>False</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>rainy</td>\n",
       "      <td>cool</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>rainy</td>\n",
       "      <td>cool</td>\n",
       "      <td>normal</td>\n",
       "      <td>True</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>overcast</td>\n",
       "      <td>cool</td>\n",
       "      <td>normal</td>\n",
       "      <td>True</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>sunny</td>\n",
       "      <td>mild</td>\n",
       "      <td>high</td>\n",
       "      <td>False</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>sunny</td>\n",
       "      <td>cool</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>rainy</td>\n",
       "      <td>mild</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>sunny</td>\n",
       "      <td>mild</td>\n",
       "      <td>normal</td>\n",
       "      <td>True</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>overcast</td>\n",
       "      <td>mild</td>\n",
       "      <td>high</td>\n",
       "      <td>True</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>overcast</td>\n",
       "      <td>hot</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>rainy</td>\n",
       "      <td>mild</td>\n",
       "      <td>high</td>\n",
       "      <td>True</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     outlook  temp humidity  windy play\n",
       "0      sunny   hot     high  False   no\n",
       "1      sunny   hot     high   True   no\n",
       "2   overcast   hot     high  False  yes\n",
       "3      rainy  mild     high  False  yes\n",
       "4      rainy  cool   normal  False  yes\n",
       "5      rainy  cool   normal   True   no\n",
       "6   overcast  cool   normal   True  yes\n",
       "7      sunny  mild     high  False   no\n",
       "8      sunny  cool   normal  False  yes\n",
       "9      rainy  mild   normal  False  yes\n",
       "10     sunny  mild   normal   True  yes\n",
       "11  overcast  mild     high   True  yes\n",
       "12  overcast   hot   normal  False  yes\n",
       "13     rainy  mild     high   True   no"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tennis_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "horror_data = pd.read_csv('https://raw.githubusercontent.com/edyoda/data-science-complete-tutorial/master/Data/horror-train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>text</th>\n",
       "      <th>author</th>\n",
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       "      <th>0</th>\n",
       "      <td>id26305</td>\n",
       "      <td>This process, however, afforded me no means of...</td>\n",
       "      <td>EAP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>id17569</td>\n",
       "      <td>It never once occurred to me that the fumbling...</td>\n",
       "      <td>HPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>id11008</td>\n",
       "      <td>In his left hand was a gold snuff box, from wh...</td>\n",
       "      <td>EAP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>id27763</td>\n",
       "      <td>How lovely is spring As we looked from Windsor...</td>\n",
       "      <td>MWS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>id12958</td>\n",
       "      <td>Finding nothing else, not even gold, the Super...</td>\n",
       "      <td>HPL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id                                               text author\n",
       "0  id26305  This process, however, afforded me no means of...    EAP\n",
       "1  id17569  It never once occurred to me that the fumbling...    HPL\n",
       "2  id11008  In his left hand was a gold snuff box, from wh...    EAP\n",
       "3  id27763  How lovely is spring As we looked from Windsor...    MWS\n",
       "4  id12958  Finding nothing else, not even gold, the Super...    HPL"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "horror_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add a coulmn representing number of words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "horror_data['numWords'] = horror_data.text.str.split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "horror_data['numWords'] = horror_data['numWords'].str.len()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "      <th>numWords</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>id01351</td>\n",
       "      <td>\"Abhorred monster Fiend that thou art The tort...</td>\n",
       "      <td>MWS</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2178</th>\n",
       "      <td>id14563</td>\n",
       "      <td>A very thin sprinkling of repellent looking yo...</td>\n",
       "      <td>HPL</td>\n",
       "      <td>24</td>\n",
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       "    <tr>\n",
       "      <th>14354</th>\n",
       "      <td>id06084</td>\n",
       "      <td>I saw that the crescent was designed to cross ...</td>\n",
       "      <td>EAP</td>\n",
       "      <td>14</td>\n",
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       "    <tr>\n",
       "      <th>11302</th>\n",
       "      <td>id17206</td>\n",
       "      <td>Each transmitted idea formed rapidly in my min...</td>\n",
       "      <td>HPL</td>\n",
       "      <td>36</td>\n",
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       "      <th>10738</th>\n",
       "      <td>id06683</td>\n",
       "      <td>South of the village one may still spy the cel...</td>\n",
       "      <td>HPL</td>\n",
       "      <td>45</td>\n",
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       "      <th>9695</th>\n",
       "      <td>id19880</td>\n",
       "      <td>In Washington Street there was a row of four o...</td>\n",
       "      <td>HPL</td>\n",
       "      <td>21</td>\n",
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       "    <tr>\n",
       "      <th>15741</th>\n",
       "      <td>id03859</td>\n",
       "      <td>I quaintly termed the domination of the Baron ...</td>\n",
       "      <td>EAP</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13630</th>\n",
       "      <td>id05319</td>\n",
       "      <td>There were the same luxurious smoothness of su...</td>\n",
       "      <td>EAP</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10547</th>\n",
       "      <td>id06675</td>\n",
       "      <td>One sheet of white covered it, while bitter re...</td>\n",
       "      <td>MWS</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9253</th>\n",
       "      <td>id27434</td>\n",
       "      <td>When there is no angle in other words, when th...</td>\n",
       "      <td>EAP</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "            id                                               text author  \\\n",
       "868    id01351  \"Abhorred monster Fiend that thou art The tort...    MWS   \n",
       "2178   id14563  A very thin sprinkling of repellent looking yo...    HPL   \n",
       "14354  id06084  I saw that the crescent was designed to cross ...    EAP   \n",
       "11302  id17206  Each transmitted idea formed rapidly in my min...    HPL   \n",
       "10738  id06683  South of the village one may still spy the cel...    HPL   \n",
       "9695   id19880  In Washington Street there was a row of four o...    HPL   \n",
       "15741  id03859  I quaintly termed the domination of the Baron ...    EAP   \n",
       "13630  id05319  There were the same luxurious smoothness of su...    EAP   \n",
       "10547  id06675  One sheet of white covered it, while bitter re...    MWS   \n",
       "9253   id27434  When there is no angle in other words, when th...    EAP   \n",
       "\n",
       "       numWords  \n",
       "868          18  \n",
       "2178         24  \n",
       "14354        14  \n",
       "11302        36  \n",
       "10738        45  \n",
       "9695         21  \n",
       "15741        21  \n",
       "13630        25  \n",
       "10547        24  \n",
       "9253         44  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "horror_data.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "horror_data.drop(columns=['id'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>numWords</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12954</th>\n",
       "      <td>The policy of insurance for my dwelling house ...</td>\n",
       "      <td>EAP</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18936</th>\n",
       "      <td>Think you that the groans of Clerval were musi...</td>\n",
       "      <td>MWS</td>\n",
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       "      <th>8818</th>\n",
       "      <td>Their benevolent disposition often made them e...</td>\n",
       "      <td>MWS</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10139</th>\n",
       "      <td>By Heaven the first tears I have shed since bo...</td>\n",
       "      <td>MWS</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9904</th>\n",
       "      <td>\"Common affection might have been satisfied wi...</td>\n",
       "      <td>MWS</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12526</th>\n",
       "      <td>Adrian suddenly vanished; and, by the time tha...</td>\n",
       "      <td>MWS</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18488</th>\n",
       "      <td>But such naive reports could mean very little,...</td>\n",
       "      <td>HPL</td>\n",
       "      <td>24</td>\n",
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       "    <tr>\n",
       "      <th>12497</th>\n",
       "      <td>At this juncture the shutter began to rattle i...</td>\n",
       "      <td>HPL</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5267</th>\n",
       "      <td>But assuredly if we had been brothers we must ...</td>\n",
       "      <td>EAP</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>Yet all these appearances have been given I be...</td>\n",
       "      <td>EAP</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    text author  numWords\n",
       "12954  The policy of insurance for my dwelling house ...    EAP        41\n",
       "18936  Think you that the groans of Clerval were musi...    MWS        12\n",
       "8818   Their benevolent disposition often made them e...    MWS        12\n",
       "10139  By Heaven the first tears I have shed since bo...    MWS        19\n",
       "9904   \"Common affection might have been satisfied wi...    MWS         9\n",
       "12526  Adrian suddenly vanished; and, by the time tha...    MWS        27\n",
       "18488  But such naive reports could mean very little,...    HPL        24\n",
       "12497  At this juncture the shutter began to rattle i...    HPL        27\n",
       "5267   But assuredly if we had been brothers we must ...    EAP        47\n",
       "114    Yet all these appearances have been given I be...    EAP        25"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "horror_data.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import make_pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "pipeline_text = make_pipeline(\n",
    "    CountVectorizer(stop_words='english')\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "         steps=[('countvectorizer',\n",
       "                 CountVectorizer(analyzer='word', binary=False,\n",
       "                                 decode_error='strict',\n",
       "                                 dtype=<class 'numpy.int64'>, encoding='utf-8',\n",
       "                                 input='content', lowercase=True, max_df=1.0,\n",
       "                                 max_features=None, min_df=1,\n",
       "                                 ngram_range=(1, 1), preprocessor=None,\n",
       "                                 stop_words='english', strip_accents=None,\n",
       "                                 token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b',\n",
       "                                 tokenizer=None, vocabulary=None))],\n",
       "         verbose=False)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline_text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline_number = make_pipeline(\n",
    "    MinMaxScaler()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.04540163],\n",
       "       [0.01396973],\n",
       "       [0.03958091],\n",
       "       ...,\n",
       "       [0.01280559],\n",
       "       [0.01513388],\n",
       "       [0.02328289]])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline_number.fit_transform(horror_data[['numWords']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import make_column_transformer,ColumnTransformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_cols = ['numWords']\n",
    "text_cols = 'text'\n",
    "\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('text_proc', pipeline_text, text_cols),\n",
    "        ('num_proc', pipeline_number, num_cols)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "pipeline = make_pipeline(preprocessor,MultinomialNB())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "         steps=[('columntransformer',\n",
       "                 ColumnTransformer(n_jobs=None, remainder='drop',\n",
       "                                   sparse_threshold=0.3,\n",
       "                                   transformer_weights=None,\n",
       "                                   transformers=[('text_proc',\n",
       "                                                  Pipeline(memory=None,\n",
       "                                                           steps=[('countvectorizer',\n",
       "                                                                   CountVectorizer(analyzer='word',\n",
       "                                                                                   binary=False,\n",
       "                                                                                   decode_error='strict',\n",
       "                                                                                   dtype=<class 'numpy.int64'>,\n",
       "                                                                                   encoding='utf-8',\n",
       "                                                                                   input='content',\n",
       "                                                                                   lowerca...\n",
       "                                                                                   stop_words='english',\n",
       "                                                                                   strip_accents=None,\n",
       "                                                                                   token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b',\n",
       "                                                                                   tokenizer=None,\n",
       "                                                                                   vocabulary=None))],\n",
       "                                                           verbose=False),\n",
       "                                                  'text'),\n",
       "                                                 ('num_proc',\n",
       "                                                  Pipeline(memory=None,\n",
       "                                                           steps=[('minmaxscaler',\n",
       "                                                                   MinMaxScaler(copy=True,\n",
       "                                                                                feature_range=(0,\n",
       "                                                                                               1)))],\n",
       "                                                           verbose=False),\n",
       "                                                  ['numWords'])],\n",
       "                                   verbose=False)),\n",
       "                ('multinomialnb',\n",
       "                 MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True))],\n",
       "         verbose=False)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline.fit(horror_data,horror_data.author)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['EAP', 'EAP'], dtype='<U3')"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline.predict(horror_data[:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.21.2'"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sklearn.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('columntransformer',\n",
       " ColumnTransformer(n_jobs=None, remainder='drop', sparse_threshold=0.3,\n",
       "                   transformer_weights=None,\n",
       "                   transformers=[('num_proc',\n",
       "                                  Pipeline(memory=None,\n",
       "                                           steps=[('minmaxscaler',\n",
       "                                                   MinMaxScaler(copy=True,\n",
       "                                                                feature_range=(0,\n",
       "                                                                               1)))],\n",
       "                                           verbose=False),\n",
       "                                  ['numWords'])],\n",
       "                   verbose=False))"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline.steps[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('text_proc', Pipeline(memory=None,\n",
       "          steps=[('countvectorizer',\n",
       "                  CountVectorizer(analyzer='word', binary=False,\n",
       "                                  decode_error='strict',\n",
       "                                  dtype=<class 'numpy.int64'>, encoding='utf-8',\n",
       "                                  input='content', lowercase=True, max_df=1.0,\n",
       "                                  max_features=None, min_df=1,\n",
       "                                  ngram_range=(1, 1), preprocessor=None,\n",
       "                                  stop_words='english', strip_accents=None,\n",
       "                                  token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b',\n",
       "                                  tokenizer=None, vocabulary=None))],\n",
       "          verbose=False), 'text')"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline.steps[0][1].transformers[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {'columntransformer__text_proc__countvectorizer__ngram_range':[[1,1],[1,2]],}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "gs = GridSearchCV(pipeline, param_grid=params, cv=5, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "             estimator=Pipeline(memory=None,\n",
       "                                steps=[('columntransformer',\n",
       "                                        ColumnTransformer(n_jobs=None,\n",
       "                                                          remainder='drop',\n",
       "                                                          sparse_threshold=0.3,\n",
       "                                                          transformer_weights=None,\n",
       "                                                          transformers=[('text_proc',\n",
       "                                                                         Pipeline(memory=None,\n",
       "                                                                                  steps=[('countvectorizer',\n",
       "                                                                                          CountVectorizer(analyzer='word',\n",
       "                                                                                                          binary=False,\n",
       "                                                                                                          decode_error='strict',\n",
       "                                                                                                          dtype=<...\n",
       "                                                                                                       feature_range=(0,\n",
       "                                                                                                                      1)))],\n",
       "                                                                                  verbose=False),\n",
       "                                                                         ['numWords'])],\n",
       "                                                          verbose=False)),\n",
       "                                       ('multinomialnb',\n",
       "                                        MultinomialNB(alpha=1.0,\n",
       "                                                      class_prior=None,\n",
       "                                                      fit_prior=True))],\n",
       "                                verbose=False),\n",
       "             iid='warn', n_jobs=-1,\n",
       "             param_grid={'columntransformer__text_proc__countvectorizer__ngram_range': [[1,\n",
       "                                                                                         1],\n",
       "                                                                                        [1,\n",
       "                                                                                         2]]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=0)"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs.fit(horror_data, horror_data.author)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'columntransformer__text_proc__countvectorizer__ngram_range': [1, 2]}"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8336482966443639"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict Survived Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('https://raw.githubusercontent.com/edyoda/data-science-complete-tutorial/master/Data/titanic-train.csv.txt', index_col='PassengerId')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hints\n",
    "1. Imputer for Missing Values\n",
    "2. Pipelines for similar data\n",
    "3. Column Transformer\n",
    "4. Grid Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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