car_reviews.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AOAI - Car Reviews Assignment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modules import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\mcarciani\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\mcarciani\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package wordnet to\n",
      "[nltk_data]     C:\\Users\\mcarciani\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n",
      "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
      "[nltk_data]     C:\\Users\\mcarciani\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package averaged_perceptron_tagger is already up-to-\n",
      "[nltk_data]       date!\n"
     ]
    }
   ],
   "source": [
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.stem import PorterStemmer\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "import nltk\n",
    "nltk.download('punkt')\n",
    "nltk.download('stopwords')\n",
    "nltk.download('wordnet')\n",
    "nltk.download('averaged_perceptron_tagger')\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.preprocessing import FunctionTransformer\n",
    "from sklearn.feature_extraction import DictVectorizer\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer, TfidfTransformer, CountVectorizer\n",
    "from sklearn.naive_bayes import MultinomialNB, GaussianNB\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.pipeline import Pipeline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from nltk.corpus import stopwords\n",
    "from collections import Counter\n",
    "import seaborn as sns\n",
    "sns.set_theme()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('car-reviews.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data visual analysis\n",
    "Let's start by performing some visual analysis on the dataset: Specifically, I will plot the review length distribution.  \n",
    "This may help us identify possible malformed data. Additionally we can visually analyze the data to see if the review length may be an additional estimator for its sentiment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of characters of the shortest review = 488\n",
      "Number of characters of the longest review = 21172\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s = df['Review'].str.len()\n",
    "df_exploration = df.copy()\n",
    "df_exploration['Review_lenght'] = s\n",
    "print(f'Number of characters of the shortest review = {min(s)}')\n",
    "print(f'Number of characters of the longest review = {max(s)}')\n",
    "plt.figure(figsize=(16, 6))\n",
    "sns.histplot(df_exploration,x='Review_lenght',hue=\"Sentiment\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Let us plot the distribution of word count per sentiment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of words of the shortest review = 110\n",
      "Number of words of the longest review = 4254\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_exploration['Word Count'] = df['Review'].str.split(\" \").str.len()\n",
    "df_exploration['Word Count'].min()\n",
    "df_exploration['Word Count'].max()\n",
    "print(f\"Number of words of the shortest review = {df_exploration['Word Count'].min()}\")\n",
    "print(f\"Number of words of the longest review = {df_exploration['Word Count'].max()}\")\n",
    "plt.figure(figsize=(16, 6))\n",
    "sns.histplot(df_exploration,x='Word Count',hue=\"Sentiment\");\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From both distributions it is possible to infer that positive reviews tend to be shorter and have fewer words.  Though, there is no clear cut and it is unlikely word count or number of characters may play a role in determining the review’s sentiment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of characters of the shortest positive review = 488\n",
      "Number of characters of the longest positive review = 21172\n"
     ]
    }
   ],
   "source": [
    "s_pos =  df[df['Sentiment']=='Pos']['Review'].str.len()\n",
    "print(f'Number of characters of the shortest positive review = {min(s_pos)}')\n",
    "print(f'Number of characters of the longest positive review = {max(s_pos)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of characters of the shortest negative review = 507\n",
      "Number of characters of the longest negative review = 19600\n"
     ]
    }
   ],
   "source": [
    "s_neg = df[df['Sentiment']=='Neg']['Review'].str.len()\n",
    "print(f'Number of characters of the shortest negative review = {min(s_neg)}')\n",
    "print(f'Number of characters of the longest negative review = {max(s_neg)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data pre-processing \n",
    "\n",
    "Before training the classifier it is good practice and sometime strictly necessary to do preprocessing; More specifically\n",
    "- Conversion to lowercase characters: to make the input review case independent\n",
    "- Word tokenization: This is a necessary steps to use a bag of word approach for classification\n",
    "- Punctuation removal: Punctuation do not contribute to the level of information about the sentiment of the text. it's good practice to remove it.\n",
    "- Stop words removal: Stop words are those words which may be very frequent but carry very little to no information at all about the sentiment of the text\n",
    "- Stemming: it's good practice to transform multiple words having the same stem to the stem word. This improve the classification performance as well as reduces the feature sparsity and dimensionality.\n",
    "\n",
    "Finally the remaining words are joint together to form the processed review \n",
    "\n",
    "The preprocessing steps above are clustered into a processing pipeline by means of  **Sklearn Pipeline**  module\n",
    "\n",
    "The definition of the single function used for pre-processing can be found below\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def lower_case(X):\n",
    "    return [review.lower() for review in X]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tokenize(X):\n",
    "    return [[word for word in word_tokenize(review)]for review in X]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_punct(X):\n",
    "    punctuation = {\".\", \",\", \";\", \"?\", \"!\"}\n",
    "    return [[word for word in review if\n",
    "                    word not in punctuation] for review in X]\n",
    "    #return [[word for word in review if\n",
    "    #                word not in punctuation and word not in stopwords_set and word.isalpha()] for review in X]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_stopwords(X):\n",
    "    stopwords_set = set(stopwords.words('english'))\n",
    "    return [[word for word in review if\n",
    "                    word not in stopwords_set and word.isalpha()] for review in X]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def stem(X):\n",
    "    ps = PorterStemmer()\n",
    "    return [[ps.stem(word) for word in review] for review in X]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def stich_tokens(X):\n",
    "    return [\" \".join(review) for review in X]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sentiment_to_int(Y):\n",
    "    Y_int = (Y == 'Pos').array.astype(int)\n",
    "    return Y_int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "processing_pipe = Pipeline([('lower_case',FunctionTransformer(lower_case)),\n",
    "                            ('tokenize',FunctionTransformer(tokenize)),\n",
    "                            ('remove_punct',FunctionTransformer(remove_punct)),\n",
    "                            ('remove_stopwords',FunctionTransformer(remove_stopwords)),\n",
    "                            ('stem',FunctionTransformer(stem)),\n",
    "                            ('stich',FunctionTransformer(stich_tokens))])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demonstrating pre-processing steps\n",
    "I am now going to demonstrate the pre-processing steps one by one and show their intermediate outputs.\n",
    "\n",
    "### Make all reviews case insensitive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "review_lower = lower_case(df.Review)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original review set:\n",
      "\n",
      "0     In 1992 we bought a new Taurus and we really ...\n",
      "1     The last business trip  I drove to San Franci...\n",
      "2     My husband and I purchased a 1990 Ford F250 a...\n",
      "3     I feel I have a thorough opinion of this truc...\n",
      "4     AS a mother of 3  all of whom are still in ca...\n",
      "Name: Review, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(\"Original review set:\\n\")\n",
    "print(df.Review[0:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Case insensitive review set:\n",
      "\n",
      "0     in 1992 we bought a new taurus and we really ...\n",
      "1     the last business trip  i drove to san franci...\n",
      "2     my husband and i purchased a 1990 ford f250 a...\n",
      "3     i feel i have a thorough opinion of this truc...\n",
      "4     as a mother of 3  all of whom are still in ca...\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(\"Case insensitive review set:\\n\")\n",
    "print(pd.Series(review_lower[0:5]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove punctuation, stop words and any word which contains numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_token = tokenize(review_lower)\n",
    "X_no_punct = remove_punct(X_token)\n",
    "X_no_stopwords = remove_stopwords(X_no_punct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Tokenized reviews with no stop words and punctuation: \n",
      "\n"
     ]
    },
    {
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       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
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       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
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       "      <td>suvs</td>\n",
       "      <td>sobs</td>\n",
       "      <td>pms</td>\n",
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       "      <td>deadly</td>\n",
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       "      <td>old</td>\n",
       "      <td>mad</td>\n",
       "      <td>woman</td>\n",
       "      <td>cell</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>hey</td>\n",
       "      <td>everybody</td>\n",
       "      <td>dont</td>\n",
       "      <td>car</td>\n",
       "      <td>havent</td>\n",
       "      <td>ever</td>\n",
       "      <td>driven</td>\n",
       "      <td>car</td>\n",
       "      <td>opinions</td>\n",
       "      <td>might</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>bought</td>\n",
       "      <td>vehicle</td>\n",
       "      <td>safe</td>\n",
       "      <td>vehicle</td>\n",
       "      <td>transport</td>\n",
       "      <td>three</td>\n",
       "      <td>young</td>\n",
       "      <td>children</td>\n",
       "      <td>california</td>\n",
       "      <td>freeways</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>new</td>\n",
       "      <td>fad</td>\n",
       "      <td>mothers</td>\n",
       "      <td>drive</td>\n",
       "      <td>expeditions</td>\n",
       "      <td>suburbans</td>\n",
       "      <td>land</td>\n",
       "      <td>cruisers</td>\n",
       "      <td>excursions</td>\n",
       "      <td>ok</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>im</td>\n",
       "      <td>granola</td>\n",
       "      <td>munchin</td>\n",
       "      <td>hippie</td>\n",
       "      <td>beast</td>\n",
       "      <td>car</td>\n",
       "      <td>hazard</td>\n",
       "      <td>environment</td>\n",
       "      <td>everyone</td>\n",
       "      <td>else</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>going</td>\n",
       "      <td>start</td>\n",
       "      <td>telling</td>\n",
       "      <td>hate</td>\n",
       "      <td>big</td>\n",
       "      <td>cars</td>\n",
       "      <td>guzzle</td>\n",
       "      <td>gas</td>\n",
       "      <td>like</td>\n",
       "      <td>nobodys</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>drive</td>\n",
       "      <td>cars</td>\n",
       "      <td>dont</td>\n",
       "      <td>care</td>\n",
       "      <td>many</td>\n",
       "      <td>damn</td>\n",
       "      <td>children</td>\n",
       "      <td>many</td>\n",
       "      <td>boats</td>\n",
       "      <td>tow</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>big</td>\n",
       "      <td>search</td>\n",
       "      <td>larger</td>\n",
       "      <td>vehicle</td>\n",
       "      <td>started</td>\n",
       "      <td>found</td>\n",
       "      <td>expecting</td>\n",
       "      <td>another</td>\n",
       "      <td>child</td>\n",
       "      <td>time</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 2003 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        0          1          2          3            4            5     \\\n",
       "0     bought        new     taurus     really        loved      decided   \n",
       "1       last   business       trip      drove          san    francisco   \n",
       "2    husband  purchased       ford    nothing     problems        owned   \n",
       "3       feel   thorough    opinion      truck     compared         post   \n",
       "4     mother      still   carseats    logical        thing        trade   \n",
       "5       ford    winstar        car      would    recommend   purchasing   \n",
       "6     bought        van     rental      since        birth    initially   \n",
       "7     bought      focus      wagon      cargo        space        great   \n",
       "8   probably      heard      giant       ford    excursion        sport   \n",
       "9    thought        gmc  chevrolet   suburban          big         wait   \n",
       "10      went       test      drive  excursion        eddie        bauer   \n",
       "11   segment      added      later    appears       though         ford   \n",
       "12      suvs       sobs        pms      great       deadly  combination   \n",
       "13       hey  everybody       dont        car       havent         ever   \n",
       "14    bought    vehicle       safe    vehicle    transport        three   \n",
       "15       new        fad    mothers      drive  expeditions    suburbans   \n",
       "16        im    granola    munchin     hippie        beast          car   \n",
       "17     going      start    telling       hate          big         cars   \n",
       "18     drive       cars       dont       care         many         damn   \n",
       "19       big     search     larger    vehicle      started        found   \n",
       "\n",
       "           6              7           8            9     ...  1993  1994  \\\n",
       "0           try            new      taurus         care  ...  None  None   \n",
       "1          went          hertz     rentals          got  ...  None  None   \n",
       "2       vehicle  approximately         two        years  ...  None  None   \n",
       "3   evaluations       vehicles       truck        owned  ...  None  None   \n",
       "4       minivan        minivan         one      handful  ...  None  None   \n",
       "5        driven            van     several        times  ...  None  None   \n",
       "6         loved     everything     dreamed       plenty  ...  None  None   \n",
       "7       camping       hardware       store         ride  ...  None  None   \n",
       "8       utility        vehicle     carries  significant  ...  None  None   \n",
       "9          till            see        ford    excursion  ...  None  None   \n",
       "10         trim        package      friend      looking  ...  None  None   \n",
       "11      finally         coming       terms          suv  ...  None  None   \n",
       "12          old            mad       woman         cell  ...  None  None   \n",
       "13       driven            car    opinions        might  ...  None  None   \n",
       "14        young       children  california     freeways  ...  None  None   \n",
       "15         land       cruisers  excursions           ok  ...  None  None   \n",
       "16       hazard    environment    everyone         else  ...  None  None   \n",
       "17       guzzle            gas        like      nobodys  ...  None  None   \n",
       "18     children           many       boats          tow  ...  None  None   \n",
       "19    expecting        another       child         time  ...  None  None   \n",
       "\n",
       "    1995  1996  1997  1998  1999  2000  2001  2002  \n",
       "0   None  None  None  None  None  None  None  None  \n",
       "1   None  None  None  None  None  None  None  None  \n",
       "2   None  None  None  None  None  None  None  None  \n",
       "3   None  None  None  None  None  None  None  None  \n",
       "4   None  None  None  None  None  None  None  None  \n",
       "5   None  None  None  None  None  None  None  None  \n",
       "6   None  None  None  None  None  None  None  None  \n",
       "7   None  None  None  None  None  None  None  None  \n",
       "8   None  None  None  None  None  None  None  None  \n",
       "9   None  None  None  None  None  None  None  None  \n",
       "10  None  None  None  None  None  None  None  None  \n",
       "11  None  None  None  None  None  None  None  None  \n",
       "12  None  None  None  None  None  None  None  None  \n",
       "13  None  None  None  None  None  None  None  None  \n",
       "14  None  None  None  None  None  None  None  None  \n",
       "15  None  None  None  None  None  None  None  None  \n",
       "16  None  None  None  None  None  None  None  None  \n",
       "17  None  None  None  None  None  None  None  None  \n",
       "18  None  None  None  None  None  None  None  None  \n",
       "19  None  None  None  None  None  None  None  None  \n",
       "\n",
       "[20 rows x 2003 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"\\nTokenized reviews with no stop words and punctuation: \\n\")\n",
    "pd.DataFrame(X_no_stopwords)[0:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apply Stemming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tokenized reviews after applying stemming: \n",
      "\n"
     ]
    },
    {
     "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>1993</th>\n",
       "      <th>1994</th>\n",
       "      <th>1995</th>\n",
       "      <th>1996</th>\n",
       "      <th>1997</th>\n",
       "      <th>1998</th>\n",
       "      <th>1999</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>bought</td>\n",
       "      <td>new</td>\n",
       "      <td>tauru</td>\n",
       "      <td>realli</td>\n",
       "      <td>love</td>\n",
       "      <td>decid</td>\n",
       "      <td>tri</td>\n",
       "      <td>new</td>\n",
       "      <td>tauru</td>\n",
       "      <td>care</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>last</td>\n",
       "      <td>busi</td>\n",
       "      <td>trip</td>\n",
       "      <td>drove</td>\n",
       "      <td>san</td>\n",
       "      <td>francisco</td>\n",
       "      <td>went</td>\n",
       "      <td>hertz</td>\n",
       "      <td>rental</td>\n",
       "      <td>got</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>husband</td>\n",
       "      <td>purchas</td>\n",
       "      <td>ford</td>\n",
       "      <td>noth</td>\n",
       "      <td>problem</td>\n",
       "      <td>own</td>\n",
       "      <td>vehicl</td>\n",
       "      <td>approxim</td>\n",
       "      <td>two</td>\n",
       "      <td>year</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>feel</td>\n",
       "      <td>thorough</td>\n",
       "      <td>opinion</td>\n",
       "      <td>truck</td>\n",
       "      <td>compar</td>\n",
       "      <td>post</td>\n",
       "      <td>evalu</td>\n",
       "      <td>vehicl</td>\n",
       "      <td>truck</td>\n",
       "      <td>own</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>mother</td>\n",
       "      <td>still</td>\n",
       "      <td>carseat</td>\n",
       "      <td>logic</td>\n",
       "      <td>thing</td>\n",
       "      <td>trade</td>\n",
       "      <td>minivan</td>\n",
       "      <td>minivan</td>\n",
       "      <td>one</td>\n",
       "      <td>hand</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ford</td>\n",
       "      <td>winstar</td>\n",
       "      <td>car</td>\n",
       "      <td>would</td>\n",
       "      <td>recommend</td>\n",
       "      <td>purchas</td>\n",
       "      <td>driven</td>\n",
       "      <td>van</td>\n",
       "      <td>sever</td>\n",
       "      <td>time</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>bought</td>\n",
       "      <td>van</td>\n",
       "      <td>rental</td>\n",
       "      <td>sinc</td>\n",
       "      <td>birth</td>\n",
       "      <td>initi</td>\n",
       "      <td>love</td>\n",
       "      <td>everyth</td>\n",
       "      <td>dream</td>\n",
       "      <td>plenti</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>bought</td>\n",
       "      <td>focu</td>\n",
       "      <td>wagon</td>\n",
       "      <td>cargo</td>\n",
       "      <td>space</td>\n",
       "      <td>great</td>\n",
       "      <td>camp</td>\n",
       "      <td>hardwar</td>\n",
       "      <td>store</td>\n",
       "      <td>ride</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>probabl</td>\n",
       "      <td>heard</td>\n",
       "      <td>giant</td>\n",
       "      <td>ford</td>\n",
       "      <td>excurs</td>\n",
       "      <td>sport</td>\n",
       "      <td>util</td>\n",
       "      <td>vehicl</td>\n",
       "      <td>carri</td>\n",
       "      <td>signific</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>thought</td>\n",
       "      <td>gmc</td>\n",
       "      <td>chevrolet</td>\n",
       "      <td>suburban</td>\n",
       "      <td>big</td>\n",
       "      <td>wait</td>\n",
       "      <td>till</td>\n",
       "      <td>see</td>\n",
       "      <td>ford</td>\n",
       "      <td>excurs</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>went</td>\n",
       "      <td>test</td>\n",
       "      <td>drive</td>\n",
       "      <td>excurs</td>\n",
       "      <td>eddi</td>\n",
       "      <td>bauer</td>\n",
       "      <td>trim</td>\n",
       "      <td>packag</td>\n",
       "      <td>friend</td>\n",
       "      <td>look</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>segment</td>\n",
       "      <td>ad</td>\n",
       "      <td>later</td>\n",
       "      <td>appear</td>\n",
       "      <td>though</td>\n",
       "      <td>ford</td>\n",
       "      <td>final</td>\n",
       "      <td>come</td>\n",
       "      <td>term</td>\n",
       "      <td>suv</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>suv</td>\n",
       "      <td>sob</td>\n",
       "      <td>pm</td>\n",
       "      <td>great</td>\n",
       "      <td>deadli</td>\n",
       "      <td>combin</td>\n",
       "      <td>old</td>\n",
       "      <td>mad</td>\n",
       "      <td>woman</td>\n",
       "      <td>cell</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>hey</td>\n",
       "      <td>everybodi</td>\n",
       "      <td>dont</td>\n",
       "      <td>car</td>\n",
       "      <td>havent</td>\n",
       "      <td>ever</td>\n",
       "      <td>driven</td>\n",
       "      <td>car</td>\n",
       "      <td>opinion</td>\n",
       "      <td>might</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>bought</td>\n",
       "      <td>vehicl</td>\n",
       "      <td>safe</td>\n",
       "      <td>vehicl</td>\n",
       "      <td>transport</td>\n",
       "      <td>three</td>\n",
       "      <td>young</td>\n",
       "      <td>children</td>\n",
       "      <td>california</td>\n",
       "      <td>freeway</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>new</td>\n",
       "      <td>fad</td>\n",
       "      <td>mother</td>\n",
       "      <td>drive</td>\n",
       "      <td>expedit</td>\n",
       "      <td>suburban</td>\n",
       "      <td>land</td>\n",
       "      <td>cruiser</td>\n",
       "      <td>excurs</td>\n",
       "      <td>ok</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>im</td>\n",
       "      <td>granola</td>\n",
       "      <td>munchin</td>\n",
       "      <td>hippi</td>\n",
       "      <td>beast</td>\n",
       "      <td>car</td>\n",
       "      <td>hazard</td>\n",
       "      <td>environ</td>\n",
       "      <td>everyon</td>\n",
       "      <td>els</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>go</td>\n",
       "      <td>start</td>\n",
       "      <td>tell</td>\n",
       "      <td>hate</td>\n",
       "      <td>big</td>\n",
       "      <td>car</td>\n",
       "      <td>guzzl</td>\n",
       "      <td>ga</td>\n",
       "      <td>like</td>\n",
       "      <td>nobodi</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>drive</td>\n",
       "      <td>car</td>\n",
       "      <td>dont</td>\n",
       "      <td>care</td>\n",
       "      <td>mani</td>\n",
       "      <td>damn</td>\n",
       "      <td>children</td>\n",
       "      <td>mani</td>\n",
       "      <td>boat</td>\n",
       "      <td>tow</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>big</td>\n",
       "      <td>search</td>\n",
       "      <td>larger</td>\n",
       "      <td>vehicl</td>\n",
       "      <td>start</td>\n",
       "      <td>found</td>\n",
       "      <td>expect</td>\n",
       "      <td>anoth</td>\n",
       "      <td>child</td>\n",
       "      <td>time</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 2003 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       0          1          2         3          4          5         6     \\\n",
       "0    bought        new      tauru    realli       love      decid       tri   \n",
       "1      last       busi       trip     drove        san  francisco      went   \n",
       "2   husband    purchas       ford      noth    problem        own    vehicl   \n",
       "3      feel   thorough    opinion     truck     compar       post     evalu   \n",
       "4    mother      still    carseat     logic      thing      trade   minivan   \n",
       "5      ford    winstar        car     would  recommend    purchas    driven   \n",
       "6    bought        van     rental      sinc      birth      initi      love   \n",
       "7    bought       focu      wagon     cargo      space      great      camp   \n",
       "8   probabl      heard      giant      ford     excurs      sport      util   \n",
       "9   thought        gmc  chevrolet  suburban        big       wait      till   \n",
       "10     went       test      drive    excurs       eddi      bauer      trim   \n",
       "11  segment         ad      later    appear     though       ford     final   \n",
       "12      suv        sob         pm     great     deadli     combin       old   \n",
       "13      hey  everybodi       dont       car     havent       ever    driven   \n",
       "14   bought     vehicl       safe    vehicl  transport      three     young   \n",
       "15      new        fad     mother     drive    expedit   suburban      land   \n",
       "16       im    granola    munchin     hippi      beast        car    hazard   \n",
       "17       go      start       tell      hate        big        car     guzzl   \n",
       "18    drive        car       dont      care       mani       damn  children   \n",
       "19      big     search     larger    vehicl      start      found    expect   \n",
       "\n",
       "        7           8         9     ...  1993  1994  1995  1996  1997  1998  \\\n",
       "0        new       tauru      care  ...  None  None  None  None  None  None   \n",
       "1      hertz      rental       got  ...  None  None  None  None  None  None   \n",
       "2   approxim         two      year  ...  None  None  None  None  None  None   \n",
       "3     vehicl       truck       own  ...  None  None  None  None  None  None   \n",
       "4    minivan         one      hand  ...  None  None  None  None  None  None   \n",
       "5        van       sever      time  ...  None  None  None  None  None  None   \n",
       "6    everyth       dream    plenti  ...  None  None  None  None  None  None   \n",
       "7    hardwar       store      ride  ...  None  None  None  None  None  None   \n",
       "8     vehicl       carri  signific  ...  None  None  None  None  None  None   \n",
       "9        see        ford    excurs  ...  None  None  None  None  None  None   \n",
       "10    packag      friend      look  ...  None  None  None  None  None  None   \n",
       "11      come        term       suv  ...  None  None  None  None  None  None   \n",
       "12       mad       woman      cell  ...  None  None  None  None  None  None   \n",
       "13       car     opinion     might  ...  None  None  None  None  None  None   \n",
       "14  children  california   freeway  ...  None  None  None  None  None  None   \n",
       "15   cruiser      excurs        ok  ...  None  None  None  None  None  None   \n",
       "16   environ     everyon       els  ...  None  None  None  None  None  None   \n",
       "17        ga        like    nobodi  ...  None  None  None  None  None  None   \n",
       "18      mani        boat       tow  ...  None  None  None  None  None  None   \n",
       "19     anoth       child      time  ...  None  None  None  None  None  None   \n",
       "\n",
       "    1999  2000  2001  2002  \n",
       "0   None  None  None  None  \n",
       "1   None  None  None  None  \n",
       "2   None  None  None  None  \n",
       "3   None  None  None  None  \n",
       "4   None  None  None  None  \n",
       "5   None  None  None  None  \n",
       "6   None  None  None  None  \n",
       "7   None  None  None  None  \n",
       "8   None  None  None  None  \n",
       "9   None  None  None  None  \n",
       "10  None  None  None  None  \n",
       "11  None  None  None  None  \n",
       "12  None  None  None  None  \n",
       "13  None  None  None  None  \n",
       "14  None  None  None  None  \n",
       "15  None  None  None  None  \n",
       "16  None  None  None  None  \n",
       "17  None  None  None  None  \n",
       "18  None  None  None  None  \n",
       "19  None  None  None  None  \n",
       "\n",
       "[20 rows x 2003 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_stem = stem(X_no_stopwords)\n",
    "print(\"Tokenized reviews after applying stemming: \\n\")\n",
    "pd.DataFrame(X_stem)[0:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Helper function to merge each review dictionary in one global dictionary containing all words used amongst all reviews\n",
    "def merge_counter(*counter_args):\n",
    "    counter_args, = counter_args\n",
    "    c = Counter({})\n",
    "    for c1 in counter_args:\n",
    "        c.update(c1)\n",
    "    return c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Retriving word frequencies before stemming\n",
    "X_no_stem_counter = [Counter(review) for review in X_no_stopwords]\n",
    "all_reviews_no_stem_dict = merge_counter(X_no_stem_counter)\n",
    "xy_no_stem = [(tag,count) for tag, count in all_reviews_no_stem_dict.most_common(20)]\n",
    "df_no_stem = pd.DataFrame(xy_no_stem, columns = ['word','occurrences'])\n",
    "clrs_no_stem = ['red' if w=='drive' else 'yellow' if w=='seat' else 'green' if w=='get' else 'c' for w in df_no_stem.word]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Retriving word frequencies after stemming\n",
    "X_stem_counter = [Counter(review) for review in X_stem]\n",
    "all_reviews_stem_dict = merge_counter(X_stem_counter)\n",
    "xy_stem = [(tag,count) for tag, count in all_reviews_stem_dict.most_common(20)]\n",
    "df_stem = pd.DataFrame(xy_stem,columns = ['word','occurrences'])\n",
    "clrs_stem = ['red' if w=='drive' else 'yellow' if w=='seat' else 'green' if w=='get' else 'c' for w in df_stem.word]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting Most common words  before and after stemming¶\n",
    "\n",
    "The Barnhart demonstrates how applying stemming to the corpus increases the stem words frequency. For example words like **get**, **drive** and **seat** (being stem words) have a higher frequency after stemming as displayed by the green, red and yellow bars."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2,sharex=True, figsize=(16,8))\n",
    "axes[0].set_title('20 most commong words before stemming');\n",
    "axes[1].set_title('20 most commong words after stemming');\n",
    "sns.barplot(ax = axes[0],y='word',x='occurrences',data=df_no_stem, palette=clrs_no_stem);\n",
    "sns.barplot(ax = axes[1],y='word',x='occurrences',data=df_stem, palette=clrs_stem);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demonstrate stemming on words: Drive, Get, Seat\n",
    "I here demostrate that stemming is correctly applied by counting the occurrences of the word stem and its inflected variants."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>drive</th>\n",
       "      <th>driving</th>\n",
       "      <th>drives</th>\n",
       "      <th>get</th>\n",
       "      <th>gets</th>\n",
       "      <th>getting</th>\n",
       "      <th>seat</th>\n",
       "      <th>seating</th>\n",
       "      <th>seats</th>\n",
       "      <th>seated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1331</td>\n",
       "      <td>1078</td>\n",
       "      <td>146</td>\n",
       "      <td>1550</td>\n",
       "      <td>259</td>\n",
       "      <td>346</td>\n",
       "      <td>1193</td>\n",
       "      <td>137</td>\n",
       "      <td>1049</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2555</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2155</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2390</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   drive  driving  drives   get  gets  getting  seat  seating  seats  seated\n",
       "0   1331     1078     146  1550   259      346  1193      137   1049      11\n",
       "1   2555        0       0  2155     0        0  2390        0      0       0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "K = ['drive',\"driving\",\"drives\",\"get\",\"gets\",\"getting\",\"seat\",\"seating\",'seats','seated']\n",
    "l = []\n",
    "for i in range(2):\n",
    "    d = {}\n",
    "    for k in K:\n",
    "        if i == 0:\n",
    "            d[k] = all_reviews_no_stem_dict[k]\n",
    "        else:\n",
    "            d[k] = all_reviews_stem_dict[k]\n",
    "    l.append(d)\n",
    "pd.DataFrame(l)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extract feature vector from reviews\n",
    "\n",
    "To extract the feature vector from each review I have used the very handy CountVectorizer module by **Sklearn**.\n",
    "To give a little context, it returns a sparse matrix representation of the input where each row correspond to one review and the number of column is equal to the total number of unique words amongst all reviews. the element Xij is number of occurrences aka term frequency of the word j in the review i."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data input matrix:\n",
      "  (0, 919)\t2\n",
      "  (0, 5579)\t3\n",
      "  (0, 8474)\t3\n",
      "  (0, 6749)\t1\n",
      "  (0, 4942)\t1\n",
      "  (0, 2132)\t1\n",
      "  (0, 8880)\t1\n",
      "  (0, 1203)\t1\n",
      "  (0, 8216)\t3\n",
      "  (0, 5582)\t1\n",
      "  (0, 9339)\t1\n",
      "  (0, 349)\t1\n",
      "  (0, 4836)\t2\n",
      "  (0, 1195)\t1\n",
      "  (0, 3765)\t1\n",
      "  (0, 5455)\t2\n",
      "  (0, 5805)\t1\n",
      "  (0, 8574)\t1\n",
      "  (0, 2073)\t1\n",
      "  (0, 2146)\t1\n",
      "  (0, 8437)\t1\n",
      "  (0, 4929)\t1\n",
      "  (0, 7118)\t1\n",
      "  (0, 3112)\t1\n",
      "  (0, 7339)\t2\n",
      "  :\t:\n",
      "  (1381, 1284)\t1\n",
      "  (1381, 7862)\t1\n",
      "  (1381, 9)\t1\n",
      "  (1381, 1511)\t1\n",
      "  (1381, 6866)\t2\n",
      "  (1381, 8251)\t1\n",
      "  (1381, 4589)\t1\n",
      "  (1381, 4063)\t1\n",
      "  (1381, 1302)\t1\n",
      "  (1381, 1816)\t1\n",
      "  (1381, 68)\t1\n",
      "  (1381, 5560)\t1\n",
      "  (1381, 5559)\t1\n",
      "  (1381, 5538)\t1\n",
      "  (1381, 4341)\t1\n",
      "  (1381, 3436)\t1\n",
      "  (1381, 9809)\t2\n",
      "  (1381, 1270)\t1\n",
      "  (1381, 6740)\t1\n",
      "  (1381, 3378)\t1\n",
      "  (1381, 762)\t1\n",
      "  (1381, 6134)\t1\n",
      "  (1381, 626)\t1\n",
      "  (1381, 2177)\t1\n",
      "  (1381, 4312)\t1\n"
     ]
    }
   ],
   "source": [
    "X_processed = stich_tokens(X_stem)\n",
    "cv = CountVectorizer(binary=False)\n",
    "X_train_int = cv.fit_transform(X_processed);\n",
    "print(\"Training data input matrix:\")\n",
    "print(X_train_int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "column 919 corresponds to the stem word \"bought\"\n",
      "\"bought\" is present 2 times in review number 0\n",
      "\"bought\" is present 0 times in review number 1\n",
      "\"bought\" is present 1 times in review number 2\n",
      "\n",
      "\n",
      "column 3112 corresponds to the stem word \"find\"\n",
      "\"find\" is present 1 times in review number 0\n",
      "\"find\" is present 1 times in review number 1\n",
      "\"find\" is present 1 times in review number 2\n",
      "\n",
      "\n",
      "column 7212 corresponds to the stem word \"said\"\n",
      "\"said\" is present 0 times in review number 0\n",
      "\"said\" is present 0 times in review number 1\n",
      "\"said\" is present 3 times in review number 2\n",
      "\n",
      "\n",
      "column 8216 corresponds to the stem word \"style\"\n",
      "\"style\" is present 3 times in review number 0\n",
      "\"style\" is present 0 times in review number 1\n",
      "\"style\" is present 0 times in review number 2\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "review_number = [0,1,2]\n",
    "feature_columns = [919,3112,7212,8216]\n",
    "for n in feature_columns:\n",
    "    print(f\"column {n} corresponds to the stem word \\\"{cv.get_feature_names()[n]}\\\"\")\n",
    "    for nr in review_number:\n",
    "        print(f\"\\\"{cv.get_feature_names()[n]}\\\" is present {X_train_int[nr,n]} times in review number {nr}\")\n",
    "    print(\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that I have demonstrated the preprocessing pipeline and feature extraction step by step, it is time to dive into training/test data splitting and classifier tuning."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Splitting the data set into Training and Test data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I split the dataset in training and test in an 80%-20% proportion while maintaining a 50-50 balance in the number of positive and negative reviews in both sets.  \n",
    "**NOTE**: In order to maintain result consistency between runs the random seed is set to 0 when splitting the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separate the negative and positive reviews\n",
    "df_pos = df[df['Sentiment'] == 'Pos']\n",
    "df_neg = df[df['Sentiment'] == 'Neg']\n",
    "\n",
    "# Split both positive and negative set in a 80% 20% fashion\n",
    "train_pos, test_pos = train_test_split(df_pos,test_size=0.2,random_state=1)\n",
    "train_neg, test_neg = train_test_split(df_neg,test_size=0.2,random_state=1)\n",
    "\n",
    "# Create training set and test set\n",
    "train_data = pd.concat([train_pos,train_neg])\n",
    "train_data.sample(frac=1, random_state=1)   # shuffle the train data\n",
    "test_data = pd.concat([test_pos,test_neg])\n",
    "# No need to shuffle the test data\n",
    "\n",
    "# Extract reviews and labels for the train data\n",
    "X_train = train_data.Review \n",
    "Y_train = train_data.Sentiment\n",
    "\n",
    "# Extract reviews and lable for the test data\n",
    "X_test = test_data.Review\n",
    "Y_test = test_data.Sentiment\n",
    "\n",
    "data =  pd.concat([train_data,test_data])\n",
    "data.sample(frac=1, random_state=1);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a Multinomial Naive Bayes model\n",
    "\n",
    "In this first part I will be considering only a  Multinomial Naive Bayes classifier which is the standard Naive Bayes classifier for text classification tasks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CREATE THE MULTINOMIAL NAIVE BAYES MODEL\n",
    "MN_NaiveBayes = MultinomialNB()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a feature extraction + classifier pipeline\n",
    "It is worth mentioning that introducing the feature extraction step in the same pipeline as the classifier training removes any possibility of data leakage not only with respect to the test data but also during cross validation on the training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe1 = Pipeline([('feature_extraction',CountVectorizer(binary=False)),\n",
    "                ('clsf',MultinomialNB())])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert Pos to 1 and Neg to 0\n",
    "Y_train_int = sentiment_to_int(Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K-Fold validated grid search\n",
    "In order to tune the model hyper parameters, I used K-Fold validated Grid Search `GridSearchCV` by sklearn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the parameters to test during GridSearch\n",
    "param_grid1 = {\n",
    "    \"clsf__alpha\": np.logspace(-1, 1, num=10),\n",
    "}\n",
    "gsCV = GridSearchCV(pipe1, param_grid1, scoring= 'accuracy', cv=5, verbose=3, n_jobs=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 9.61 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Runt the data processing pipiline on the training data\n",
    "X_train_processed = processing_pipe.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
      "[CV] clsf__alpha=0.1 .................................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ..................... clsf__alpha=0.1, score=0.760, total=   0.3s\n",
      "[CV] clsf__alpha=0.1 .................................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.2s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ..................... clsf__alpha=0.1, score=0.751, total=   0.2s\n",
      "[CV] clsf__alpha=0.1 .................................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.4s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ..................... clsf__alpha=0.1, score=0.787, total=   0.3s\n",
      "[CV] clsf__alpha=0.1 .................................................\n",
      "[CV] ..................... clsf__alpha=0.1, score=0.733, total=   0.3s\n",
      "[CV] clsf__alpha=0.1 .................................................\n",
      "[CV] ..................... clsf__alpha=0.1, score=0.718, total=   0.3s\n",
      "[CV] clsf__alpha=0.16681005372000587 .................................\n",
      "[CV] ..... clsf__alpha=0.16681005372000587, score=0.769, total=   0.3s\n",
      "[CV] clsf__alpha=0.16681005372000587 .................................\n",
      "[CV] ..... clsf__alpha=0.16681005372000587, score=0.760, total=   0.3s\n",
      "[CV] clsf__alpha=0.16681005372000587 .................................\n",
      "[CV] ..... clsf__alpha=0.16681005372000587, score=0.792, total=   0.3s\n",
      "[CV] clsf__alpha=0.16681005372000587 .................................\n",
      "[CV] ..... clsf__alpha=0.16681005372000587, score=0.747, total=   0.3s\n",
      "[CV] clsf__alpha=0.16681005372000587 .................................\n",
      "[CV] ..... clsf__alpha=0.16681005372000587, score=0.723, total=   0.2s\n",
      "[CV] clsf__alpha=0.2782559402207124 ..................................\n",
      "[CV] ...... clsf__alpha=0.2782559402207124, score=0.774, total=   0.2s\n",
      "[CV] clsf__alpha=0.2782559402207124 ..................................\n",
      "[CV] ...... clsf__alpha=0.2782559402207124, score=0.756, total=   0.2s\n",
      "[CV] clsf__alpha=0.2782559402207124 ..................................\n",
      "[CV] ...... clsf__alpha=0.2782559402207124, score=0.792, total=   0.2s\n",
      "[CV] clsf__alpha=0.2782559402207124 ..................................\n",
      "[CV] ...... clsf__alpha=0.2782559402207124, score=0.765, total=   0.2s\n",
      "[CV] clsf__alpha=0.2782559402207124 ..................................\n",
      "[CV] ...... clsf__alpha=0.2782559402207124, score=0.718, total=   0.2s\n",
      "[CV] clsf__alpha=0.46415888336127786 .................................\n",
      "[CV] ..... clsf__alpha=0.46415888336127786, score=0.765, total=   0.2s\n",
      "[CV] clsf__alpha=0.46415888336127786 .................................\n",
      "[CV] ..... clsf__alpha=0.46415888336127786, score=0.765, total=   0.2s\n",
      "[CV] clsf__alpha=0.46415888336127786 .................................\n",
      "[CV] ..... clsf__alpha=0.46415888336127786, score=0.805, total=   0.2s\n",
      "[CV] clsf__alpha=0.46415888336127786 .................................\n",
      "[CV] ..... clsf__alpha=0.46415888336127786, score=0.778, total=   0.2s\n",
      "[CV] clsf__alpha=0.46415888336127786 .................................\n",
      "[CV] ..... clsf__alpha=0.46415888336127786, score=0.727, total=   0.2s\n",
      "[CV] clsf__alpha=0.774263682681127 ...................................\n",
      "[CV] ....... clsf__alpha=0.774263682681127, score=0.760, total=   0.2s\n",
      "[CV] clsf__alpha=0.774263682681127 ...................................\n",
      "[CV] ....... clsf__alpha=0.774263682681127, score=0.774, total=   0.2s\n",
      "[CV] clsf__alpha=0.774263682681127 ...................................\n",
      "[CV] ....... clsf__alpha=0.774263682681127, score=0.810, total=   0.2s\n",
      "[CV] clsf__alpha=0.774263682681127 ...................................\n",
      "[CV] ....... clsf__alpha=0.774263682681127, score=0.787, total=   0.2s\n",
      "[CV] clsf__alpha=0.774263682681127 ...................................\n",
      "[CV] ....... clsf__alpha=0.774263682681127, score=0.732, total=   0.2s\n",
      "[CV] clsf__alpha=1.291549665014884 ...................................\n",
      "[CV] ....... clsf__alpha=1.291549665014884, score=0.760, total=   0.3s\n",
      "[CV] clsf__alpha=1.291549665014884 ...................................\n",
      "[CV] ....... clsf__alpha=1.291549665014884, score=0.769, total=   0.2s\n",
      "[CV] clsf__alpha=1.291549665014884 ...................................\n",
      "[CV] ....... clsf__alpha=1.291549665014884, score=0.810, total=   0.2s\n",
      "[CV] clsf__alpha=1.291549665014884 ...................................\n",
      "[CV] ....... clsf__alpha=1.291549665014884, score=0.783, total=   0.2s\n",
      "[CV] clsf__alpha=1.291549665014884 ...................................\n",
      "[CV] ....... clsf__alpha=1.291549665014884, score=0.723, total=   0.2s\n",
      "[CV] clsf__alpha=2.1544346900318834 ..................................\n",
      "[CV] ...... clsf__alpha=2.1544346900318834, score=0.756, total=   0.3s\n",
      "[CV] clsf__alpha=2.1544346900318834 ..................................\n",
      "[CV] ...... clsf__alpha=2.1544346900318834, score=0.769, total=   0.3s\n",
      "[CV] clsf__alpha=2.1544346900318834 ..................................\n",
      "[CV] ...... clsf__alpha=2.1544346900318834, score=0.810, total=   0.2s\n",
      "[CV] clsf__alpha=2.1544346900318834 ..................................\n",
      "[CV] ...... clsf__alpha=2.1544346900318834, score=0.787, total=   0.2s\n",
      "[CV] clsf__alpha=2.1544346900318834 ..................................\n",
      "[CV] ...... clsf__alpha=2.1544346900318834, score=0.718, total=   0.3s\n",
      "[CV] clsf__alpha=3.593813663804626 ...................................\n",
      "[CV] ....... clsf__alpha=3.593813663804626, score=0.756, total=   0.2s\n",
      "[CV] clsf__alpha=3.593813663804626 ...................................\n",
      "[CV] ....... clsf__alpha=3.593813663804626, score=0.769, total=   0.2s\n",
      "[CV] clsf__alpha=3.593813663804626 ...................................\n",
      "[CV] ....... clsf__alpha=3.593813663804626, score=0.810, total=   0.2s\n",
      "[CV] clsf__alpha=3.593813663804626 ...................................\n",
      "[CV] ....... clsf__alpha=3.593813663804626, score=0.783, total=   0.3s\n",
      "[CV] clsf__alpha=3.593813663804626 ...................................\n",
      "[CV] ....... clsf__alpha=3.593813663804626, score=0.718, total=   0.3s\n",
      "[CV] clsf__alpha=5.994842503189409 ...................................\n",
      "[CV] ....... clsf__alpha=5.994842503189409, score=0.756, total=   0.2s\n",
      "[CV] clsf__alpha=5.994842503189409 ...................................\n",
      "[CV] ....... clsf__alpha=5.994842503189409, score=0.769, total=   0.2s\n",
      "[CV] clsf__alpha=5.994842503189409 ...................................\n",
      "[CV] ....... clsf__alpha=5.994842503189409, score=0.796, total=   0.2s\n",
      "[CV] clsf__alpha=5.994842503189409 ...................................\n",
      "[CV] ....... clsf__alpha=5.994842503189409, score=0.774, total=   0.2s\n",
      "[CV] clsf__alpha=5.994842503189409 ...................................\n",
      "[CV] ....... clsf__alpha=5.994842503189409, score=0.723, total=   0.2s\n",
      "[CV] clsf__alpha=10.0 ................................................\n",
      "[CV] .................... clsf__alpha=10.0, score=0.733, total=   0.3s\n",
      "[CV] clsf__alpha=10.0 ................................................\n",
      "[CV] .................... clsf__alpha=10.0, score=0.774, total=   0.2s\n",
      "[CV] clsf__alpha=10.0 ................................................\n",
      "[CV] .................... clsf__alpha=10.0, score=0.801, total=   0.2s\n",
      "[CV] clsf__alpha=10.0 ................................................\n",
      "[CV] .................... clsf__alpha=10.0, score=0.751, total=   0.2s\n",
      "[CV] clsf__alpha=10.0 ................................................\n",
      "[CV] .................... clsf__alpha=10.0, score=0.727, total=   0.2s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:   12.6s finished\n"
     ]
    }
   ],
   "source": [
    "# Run GridSearchCV\n",
    "gsCV.fit(X_train_processed, Y_train_int);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get the best model\n",
    "\n",
    "I now retrieve the best performing model along with its score. This will be the baseline reference model whose accuracy I'll try to improve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Model = Pipeline(steps=[('feature_extraction', CountVectorizer()),\n",
      "                ('clsf', MultinomialNB(alpha=0.774263682681127))])\n",
      "Alpha ={'clsf__alpha': 0.774263682681127}\n",
      "Accuracy score = 0.772607980255039\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# GET THE BEST MODEL\n",
    "best_param_baseline = gsCV.best_params_\n",
    "best_score_baseline = gsCV.best_score_\n",
    "best_model_baseline = gsCV.best_estimator_\n",
    "print(f'Best Model = {best_model_baseline}\\nAlpha ={best_param_baseline}\\nAccuracy score = {best_score_baseline}\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We obtained a 5-fold cross-validated accuracy of 77.26%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average accuracy = 0.8057553956834532\n",
      "F1 score = 0.8098591549295774\n",
      "[[109  30]\n",
      " [ 24 115]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test =  True\n",
    "if test:\n",
    "    Y_test_int = sentiment_to_int(Y_test)\n",
    "    X_test_processed = processing_pipe.transform(X_test)\n",
    "    Y_test_pred = best_model_baseline.predict(X_test_processed)\n",
    "    cnf_matrix = confusion_matrix(Y_test_int, Y_test_pred)\n",
    "    avg_acc = accuracy_score(Y_test_int, Y_test_pred)\n",
    "    f1 = f1_score(Y_test_int, Y_test_pred)\n",
    "    print(f\"Average accuracy = {avg_acc}\")\n",
    "    print(f\"F1 score = {f1}\")\n",
    "    print(cnf_matrix)\n",
    "    ax = sns.heatmap(cnf_matrix, annot=True, fmt=\"d\",xticklabels=[\"Pos\",\"Neg\"],yticklabels=[\"Pos\",\"Neg\"],cmap=\"YlGnBu\");\n",
    "    plt.xlabel('True sentiments')\n",
    "    plt.ylabel('Predicted sentiments')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 2 - improve classifiction perfomances\n",
    "\n",
    "From here onward I will attempt to improve the classification performances I obtained just above.\n",
    "I will be carrying out few different experiments, more specifically:\n",
    "\n",
    "**Experiment 1**\n",
    "- Binary vs full-word-count feature vector\n",
    "- tf vs tf-idf\n",
    "\n",
    "**Experiment 2**\n",
    "- Fixed window negation handling\n",
    "\n",
    "**Experiment 3**\n",
    "- Dependency parser negation handling\n",
    "\n",
    "**Experiment 4**\n",
    "- Dense feature vector representation by means of word embedding.\n",
    "\n",
    "In the first 3 experiments 3 classifiers are compared against each other, more specifically, Multinomial Naive Bayes, Logistic regression and SVC. In the last experiment (Expertiment4) only Logistic Regression and SVC are used as the feature vector might contain negative values which are not supported by Multinomial Naive Bayes  \n",
    "All models are compared against each other using GridSearchCV by sklearn\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Experiment 1\n",
    "In part 1, each review feature vector was composed by the term frequency component (tf) of each word in the review. It is important to specify that the length of the feature vector may be much longer than the number of unique words in each review as it contains all unique words amongst all reviews in the training data.\n",
    "In this experiment I will explore another technique to represent the feature vectors.\n",
    "By considering term frequency, terms which appear often in the reviews can carry a big weight but low information about the sentiment of the review itself.\n",
    "To take care of this, it is possible to standardize every column of the feature vector by multiply each value by the inverse of term frequency amongst all reviews. This is called **tf-idf**. This is indeed a form of feature normalization where the most frequent terms are penalized the most effectively scaling every term to a value between 0 and 1.\n",
    "For both **tf** an  **tf-idf** the **tf** part can either be the term count or the term existence (binary) in the review. \n",
    "In the section below I will explore all 4 approaches:\n",
    "- tf  (count)\n",
    "- tf  (binary)\n",
    "- tf-idf (count)\n",
    "- tf-idf (binary)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I created a pipeline `pipe_exp1` in which the first step is the feature extraction and the second step is the classifier training. \n",
    "I am going to use GridSearchCV to test the two type of vectorizers with different values for the `binary` argument. When the binary argument is set to true the resulting feature vector will only contain the information about the existence of words rather than their count."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create feature extraction and classifier training pipeline\n",
    "pipe_exp1 = Pipeline([('vectorizer',None),\n",
    "                 ('clsf',MultinomialNB())])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the parameters to change during the cross validated grid search\n",
    "param_grid_exp1 = [{'vectorizer': [CountVectorizer(),TfidfVectorizer()],\n",
    "                'vectorizer__binary' : [True, False],\n",
    "               'clsf__alpha': np.logspace(-1, 1, num=10)}]\n",
    "gsCV_exp1 = GridSearchCV(pipe_exp1, param_grid_exp1, scoring= 'accuracy', cv=5, verbose=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pre-process the training data: lables and features\n",
    "Y_train_int = sentiment_to_int(Y_train)\n",
    "X_train_processed = processing_pipe.transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 40 candidates, totalling 200 fits\n",
      "[CV] clsf__alpha=0.1, vectorizer=CountVectorizer(), vectorizer__binary=True \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  clsf__alpha=0.1, vectorizer=CountVectorizer(), vectorizer__binary=True, score=0.769, total=   0.3s\n",
      "[CV] clsf__alpha=0.1, vectorizer=CountVectorizer(), vectorizer__binary=True \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.2s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
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      "[CV]  clsf__alpha=0.1, vectorizer=CountVectorizer(), vectorizer__binary=True, score=0.751, total=   0.2s\n",
      "[CV] clsf__alpha=0.1, vectorizer=CountVectorizer(), vectorizer__binary=True \n"
     ]
    },
    {
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     "text": [
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.4s remaining:    0.0s\n"
     ]
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      "[CV]  clsf__alpha=0.1, vectorizer=CountVectorizer(), vectorizer__binary=True, score=0.796, total=   0.3s\n",
      "[CV] clsf__alpha=0.1, vectorizer=CountVectorizer(), vectorizer__binary=True \n",
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      "[CV] clsf__alpha=0.1, vectorizer=CountVectorizer(), vectorizer__binary=False \n",
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      "[CV] clsf__alpha=0.1, vectorizer=CountVectorizer(), vectorizer__binary=False \n",
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      "[CV] clsf__alpha=0.1, vectorizer=CountVectorizer(), vectorizer__binary=False \n",
      "[CV]  clsf__alpha=0.1, vectorizer=CountVectorizer(), vectorizer__binary=False, score=0.718, total=   0.3s\n",
      "[CV] clsf__alpha=0.1, vectorizer=TfidfVectorizer(), vectorizer__binary=True \n",
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      "[CV] clsf__alpha=0.16681005372000587, vectorizer=CountVectorizer(), vectorizer__binary=True \n",
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      "[CV] clsf__alpha=0.16681005372000587, vectorizer=TfidfVectorizer(), vectorizer__binary=False \n",
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      "[CV] clsf__alpha=0.16681005372000587, vectorizer=TfidfVectorizer(), vectorizer__binary=False \n",
      "[CV]  clsf__alpha=0.16681005372000587, vectorizer=TfidfVectorizer(), vectorizer__binary=False, score=0.778, total=   0.2s\n",
      "[CV] clsf__alpha=0.16681005372000587, vectorizer=TfidfVectorizer(), vectorizer__binary=False \n",
      "[CV]  clsf__alpha=0.16681005372000587, vectorizer=TfidfVectorizer(), vectorizer__binary=False, score=0.810, total=   0.2s\n",
      "[CV] clsf__alpha=0.16681005372000587, vectorizer=TfidfVectorizer(), vectorizer__binary=False \n",
      "[CV]  clsf__alpha=0.16681005372000587, vectorizer=TfidfVectorizer(), vectorizer__binary=False, score=0.765, total=   0.2s\n",
      "[CV] clsf__alpha=0.16681005372000587, vectorizer=TfidfVectorizer(), vectorizer__binary=False \n",
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      "[CV] clsf__alpha=0.2782559402207124, vectorizer=CountVectorizer(), vectorizer__binary=True \n",
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     ]
    },
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      "[CV]  clsf__alpha=0.2782559402207124, vectorizer=CountVectorizer(), vectorizer__binary=True, score=0.787, total=   0.2s\n",
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      "[CV] clsf__alpha=0.2782559402207124, vectorizer=TfidfVectorizer(), vectorizer__binary=True \n",
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      "[CV] clsf__alpha=0.46415888336127786, vectorizer=CountVectorizer(), vectorizer__binary=True \n",
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      "[CV] clsf__alpha=0.46415888336127786, vectorizer=TfidfVectorizer(), vectorizer__binary=True \n",
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      "[CV] clsf__alpha=0.46415888336127786, vectorizer=TfidfVectorizer(), vectorizer__binary=False \n",
      "[CV]  clsf__alpha=0.46415888336127786, vectorizer=TfidfVectorizer(), vectorizer__binary=False, score=0.801, total=   0.4s\n",
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      "[CV] clsf__alpha=0.46415888336127786, vectorizer=TfidfVectorizer(), vectorizer__binary=False \n",
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      "[CV] clsf__alpha=0.774263682681127, vectorizer=CountVectorizer(), vectorizer__binary=True \n",
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     "text": [
      "[CV]  clsf__alpha=0.774263682681127, vectorizer=CountVectorizer(), vectorizer__binary=True, score=0.801, total=   0.2s\n",
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      "[CV] clsf__alpha=0.774263682681127, vectorizer=TfidfVectorizer(), vectorizer__binary=True \n",
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      "[CV] clsf__alpha=0.774263682681127, vectorizer=TfidfVectorizer(), vectorizer__binary=False \n",
      "[CV]  clsf__alpha=0.774263682681127, vectorizer=TfidfVectorizer(), vectorizer__binary=False, score=0.787, total=   0.3s\n",
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      "[CV]  clsf__alpha=0.774263682681127, vectorizer=TfidfVectorizer(), vectorizer__binary=False, score=0.723, total=   0.3s\n",
      "[CV] clsf__alpha=1.291549665014884, vectorizer=CountVectorizer(), vectorizer__binary=True \n",
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      "[CV] clsf__alpha=1.291549665014884, vectorizer=CountVectorizer(), vectorizer__binary=False \n",
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      "[CV] clsf__alpha=1.291549665014884, vectorizer=TfidfVectorizer(), vectorizer__binary=True \n",
      "[CV]  clsf__alpha=1.291549665014884, vectorizer=TfidfVectorizer(), vectorizer__binary=True, score=0.810, total=   0.3s\n",
      "[CV] clsf__alpha=1.291549665014884, vectorizer=TfidfVectorizer(), vectorizer__binary=True \n",
      "[CV]  clsf__alpha=1.291549665014884, vectorizer=TfidfVectorizer(), vectorizer__binary=True, score=0.751, total=   0.2s\n",
      "[CV] clsf__alpha=1.291549665014884, vectorizer=TfidfVectorizer(), vectorizer__binary=True \n",
      "[CV]  clsf__alpha=1.291549665014884, vectorizer=TfidfVectorizer(), vectorizer__binary=True, score=0.810, total=   0.2s\n",
      "[CV] clsf__alpha=1.291549665014884, vectorizer=TfidfVectorizer(), vectorizer__binary=True \n",
      "[CV]  clsf__alpha=1.291549665014884, vectorizer=TfidfVectorizer(), vectorizer__binary=True, score=0.801, total=   0.3s\n",
      "[CV] clsf__alpha=1.291549665014884, vectorizer=TfidfVectorizer(), vectorizer__binary=True \n",
      "[CV]  clsf__alpha=1.291549665014884, vectorizer=TfidfVectorizer(), vectorizer__binary=True, score=0.759, total=   0.3s\n",
      "[CV] clsf__alpha=1.291549665014884, vectorizer=TfidfVectorizer(), vectorizer__binary=False \n",
      "[CV]  clsf__alpha=1.291549665014884, vectorizer=TfidfVectorizer(), vectorizer__binary=False, score=0.792, total=   0.3s\n",
      "[CV] clsf__alpha=1.291549665014884, vectorizer=TfidfVectorizer(), vectorizer__binary=False \n",
      "[CV]  clsf__alpha=1.291549665014884, vectorizer=TfidfVectorizer(), vectorizer__binary=False, score=0.769, total=   0.3s\n",
      "[CV] clsf__alpha=1.291549665014884, vectorizer=TfidfVectorizer(), vectorizer__binary=False \n",
      "[CV]  clsf__alpha=1.291549665014884, vectorizer=TfidfVectorizer(), vectorizer__binary=False, score=0.810, total=   0.3s\n",
      "[CV] clsf__alpha=1.291549665014884, vectorizer=TfidfVectorizer(), vectorizer__binary=False \n",
      "[CV]  clsf__alpha=1.291549665014884, vectorizer=TfidfVectorizer(), vectorizer__binary=False, score=0.769, total=   0.2s\n",
      "[CV] clsf__alpha=1.291549665014884, vectorizer=TfidfVectorizer(), vectorizer__binary=False \n",
      "[CV]  clsf__alpha=1.291549665014884, vectorizer=TfidfVectorizer(), vectorizer__binary=False, score=0.723, total=   0.2s\n",
      "[CV] clsf__alpha=2.1544346900318834, vectorizer=CountVectorizer(), vectorizer__binary=True \n",
      "[CV]  clsf__alpha=2.1544346900318834, vectorizer=CountVectorizer(), vectorizer__binary=True, score=0.787, total=   0.2s\n",
      "[CV] clsf__alpha=2.1544346900318834, vectorizer=CountVectorizer(), vectorizer__binary=True \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  clsf__alpha=2.1544346900318834, vectorizer=CountVectorizer(), vectorizer__binary=True, score=0.756, total=   0.3s\n",
      "[CV] clsf__alpha=2.1544346900318834, vectorizer=CountVectorizer(), vectorizer__binary=True \n",
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      "[CV] clsf__alpha=2.1544346900318834, vectorizer=CountVectorizer(), vectorizer__binary=False \n",
      "[CV]  clsf__alpha=2.1544346900318834, vectorizer=CountVectorizer(), vectorizer__binary=False, score=0.756, total=   0.3s\n",
      "[CV] clsf__alpha=2.1544346900318834, vectorizer=CountVectorizer(), vectorizer__binary=False \n",
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      "[CV] clsf__alpha=2.1544346900318834, vectorizer=CountVectorizer(), vectorizer__binary=False \n",
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      "[CV] clsf__alpha=2.1544346900318834, vectorizer=CountVectorizer(), vectorizer__binary=False \n",
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      "[CV] clsf__alpha=2.1544346900318834, vectorizer=CountVectorizer(), vectorizer__binary=False \n",
      "[CV]  clsf__alpha=2.1544346900318834, vectorizer=CountVectorizer(), vectorizer__binary=False, score=0.718, total=   0.3s\n",
      "[CV] clsf__alpha=2.1544346900318834, vectorizer=TfidfVectorizer(), vectorizer__binary=True \n",
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      "[CV] clsf__alpha=2.1544346900318834, vectorizer=TfidfVectorizer(), vectorizer__binary=False \n",
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      "[CV] clsf__alpha=2.1544346900318834, vectorizer=TfidfVectorizer(), vectorizer__binary=False \n",
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      "[CV] clsf__alpha=2.1544346900318834, vectorizer=TfidfVectorizer(), vectorizer__binary=False \n",
      "[CV]  clsf__alpha=2.1544346900318834, vectorizer=TfidfVectorizer(), vectorizer__binary=False, score=0.814, total=   0.3s\n",
      "[CV] clsf__alpha=2.1544346900318834, vectorizer=TfidfVectorizer(), vectorizer__binary=False \n",
      "[CV]  clsf__alpha=2.1544346900318834, vectorizer=TfidfVectorizer(), vectorizer__binary=False, score=0.778, total=   0.3s\n",
      "[CV] clsf__alpha=2.1544346900318834, vectorizer=TfidfVectorizer(), vectorizer__binary=False \n",
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      "[CV] clsf__alpha=3.593813663804626, vectorizer=CountVectorizer(), vectorizer__binary=True \n",
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      "[CV] clsf__alpha=3.593813663804626, vectorizer=CountVectorizer(), vectorizer__binary=True \n",
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      "[CV] clsf__alpha=3.593813663804626, vectorizer=CountVectorizer(), vectorizer__binary=True \n",
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      "[CV] clsf__alpha=3.593813663804626, vectorizer=CountVectorizer(), vectorizer__binary=False \n",
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      "[CV] clsf__alpha=3.593813663804626, vectorizer=CountVectorizer(), vectorizer__binary=False \n",
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      "[CV] clsf__alpha=3.593813663804626, vectorizer=CountVectorizer(), vectorizer__binary=False \n",
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      "[CV] clsf__alpha=3.593813663804626, vectorizer=CountVectorizer(), vectorizer__binary=False \n",
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      "[CV] clsf__alpha=3.593813663804626, vectorizer=TfidfVectorizer(), vectorizer__binary=True \n",
      "[CV]  clsf__alpha=3.593813663804626, vectorizer=TfidfVectorizer(), vectorizer__binary=True, score=0.792, total=   0.2s\n",
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      "[CV] clsf__alpha=3.593813663804626, vectorizer=TfidfVectorizer(), vectorizer__binary=True \n",
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      "[CV] clsf__alpha=3.593813663804626, vectorizer=TfidfVectorizer(), vectorizer__binary=True \n",
      "[CV]  clsf__alpha=3.593813663804626, vectorizer=TfidfVectorizer(), vectorizer__binary=True, score=0.796, total=   0.3s\n",
      "[CV] clsf__alpha=3.593813663804626, vectorizer=TfidfVectorizer(), vectorizer__binary=True \n",
      "[CV]  clsf__alpha=3.593813663804626, vectorizer=TfidfVectorizer(), vectorizer__binary=True, score=0.750, total=   0.2s\n",
      "[CV] clsf__alpha=3.593813663804626, vectorizer=TfidfVectorizer(), vectorizer__binary=False \n",
      "[CV]  clsf__alpha=3.593813663804626, vectorizer=TfidfVectorizer(), vectorizer__binary=False, score=0.765, total=   0.3s\n",
      "[CV] clsf__alpha=3.593813663804626, vectorizer=TfidfVectorizer(), vectorizer__binary=False \n",
      "[CV]  clsf__alpha=3.593813663804626, vectorizer=TfidfVectorizer(), vectorizer__binary=False, score=0.774, total=   0.2s\n",
      "[CV] clsf__alpha=3.593813663804626, vectorizer=TfidfVectorizer(), vectorizer__binary=False \n",
      "[CV]  clsf__alpha=3.593813663804626, vectorizer=TfidfVectorizer(), vectorizer__binary=False, score=0.810, total=   0.2s\n",
      "[CV] clsf__alpha=3.593813663804626, vectorizer=TfidfVectorizer(), vectorizer__binary=False \n",
      "[CV]  clsf__alpha=3.593813663804626, vectorizer=TfidfVectorizer(), vectorizer__binary=False, score=0.778, total=   0.3s\n",
      "[CV] clsf__alpha=3.593813663804626, vectorizer=TfidfVectorizer(), vectorizer__binary=False \n",
      "[CV]  clsf__alpha=3.593813663804626, vectorizer=TfidfVectorizer(), vectorizer__binary=False, score=0.718, total=   0.2s\n",
      "[CV] clsf__alpha=5.994842503189409, vectorizer=CountVectorizer(), vectorizer__binary=True \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  clsf__alpha=5.994842503189409, vectorizer=CountVectorizer(), vectorizer__binary=True, score=0.783, total=   0.3s\n",
      "[CV] clsf__alpha=5.994842503189409, vectorizer=CountVectorizer(), vectorizer__binary=True \n",
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      "[CV] clsf__alpha=5.994842503189409, vectorizer=CountVectorizer(), vectorizer__binary=True \n",
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      "[CV] clsf__alpha=5.994842503189409, vectorizer=CountVectorizer(), vectorizer__binary=True \n",
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      "[CV] clsf__alpha=5.994842503189409, vectorizer=CountVectorizer(), vectorizer__binary=True \n",
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      "[CV] clsf__alpha=5.994842503189409, vectorizer=CountVectorizer(), vectorizer__binary=False \n",
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      "[CV] clsf__alpha=5.994842503189409, vectorizer=CountVectorizer(), vectorizer__binary=False \n",
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      "[CV] clsf__alpha=5.994842503189409, vectorizer=CountVectorizer(), vectorizer__binary=False \n",
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      "[CV] clsf__alpha=5.994842503189409, vectorizer=CountVectorizer(), vectorizer__binary=False \n",
      "[CV]  clsf__alpha=5.994842503189409, vectorizer=CountVectorizer(), vectorizer__binary=False, score=0.774, total=   0.2s\n",
      "[CV] clsf__alpha=5.994842503189409, vectorizer=CountVectorizer(), vectorizer__binary=False \n",
      "[CV]  clsf__alpha=5.994842503189409, vectorizer=CountVectorizer(), vectorizer__binary=False, score=0.723, total=   0.3s\n",
      "[CV] clsf__alpha=5.994842503189409, vectorizer=TfidfVectorizer(), vectorizer__binary=True \n",
      "[CV]  clsf__alpha=5.994842503189409, vectorizer=TfidfVectorizer(), vectorizer__binary=True, score=0.787, total=   0.2s\n",
      "[CV] clsf__alpha=5.994842503189409, vectorizer=TfidfVectorizer(), vectorizer__binary=True \n",
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      "[CV]  clsf__alpha=5.994842503189409, vectorizer=TfidfVectorizer(), vectorizer__binary=True, score=0.805, total=   0.2s\n",
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      "[CV]  clsf__alpha=5.994842503189409, vectorizer=TfidfVectorizer(), vectorizer__binary=True, score=0.745, total=   0.2s\n",
      "[CV] clsf__alpha=5.994842503189409, vectorizer=TfidfVectorizer(), vectorizer__binary=False \n",
      "[CV]  clsf__alpha=5.994842503189409, vectorizer=TfidfVectorizer(), vectorizer__binary=False, score=0.756, total=   0.2s\n",
      "[CV] clsf__alpha=5.994842503189409, vectorizer=TfidfVectorizer(), vectorizer__binary=False \n",
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      "[CV] clsf__alpha=5.994842503189409, vectorizer=TfidfVectorizer(), vectorizer__binary=False \n",
      "[CV]  clsf__alpha=5.994842503189409, vectorizer=TfidfVectorizer(), vectorizer__binary=False, score=0.805, total=   0.2s\n",
      "[CV] clsf__alpha=5.994842503189409, vectorizer=TfidfVectorizer(), vectorizer__binary=False \n",
      "[CV]  clsf__alpha=5.994842503189409, vectorizer=TfidfVectorizer(), vectorizer__binary=False, score=0.778, total=   0.2s\n",
      "[CV] clsf__alpha=5.994842503189409, vectorizer=TfidfVectorizer(), vectorizer__binary=False \n",
      "[CV]  clsf__alpha=5.994842503189409, vectorizer=TfidfVectorizer(), vectorizer__binary=False, score=0.718, total=   0.3s\n",
      "[CV] clsf__alpha=10.0, vectorizer=CountVectorizer(), vectorizer__binary=True \n",
      "[CV]  clsf__alpha=10.0, vectorizer=CountVectorizer(), vectorizer__binary=True, score=0.774, total=   0.3s\n",
      "[CV] clsf__alpha=10.0, vectorizer=CountVectorizer(), vectorizer__binary=True \n",
      "[CV]  clsf__alpha=10.0, vectorizer=CountVectorizer(), vectorizer__binary=True, score=0.765, total=   0.2s\n",
      "[CV] clsf__alpha=10.0, vectorizer=CountVectorizer(), vectorizer__binary=True \n",
      "[CV]  clsf__alpha=10.0, vectorizer=CountVectorizer(), vectorizer__binary=True, score=0.819, total=   0.3s\n",
      "[CV] clsf__alpha=10.0, vectorizer=CountVectorizer(), vectorizer__binary=True \n",
      "[CV]  clsf__alpha=10.0, vectorizer=CountVectorizer(), vectorizer__binary=True, score=0.805, total=   0.2s\n",
      "[CV] clsf__alpha=10.0, vectorizer=CountVectorizer(), vectorizer__binary=True \n",
      "[CV]  clsf__alpha=10.0, vectorizer=CountVectorizer(), vectorizer__binary=True, score=0.745, total=   0.2s\n",
      "[CV] clsf__alpha=10.0, vectorizer=CountVectorizer(), vectorizer__binary=False \n",
      "[CV]  clsf__alpha=10.0, vectorizer=CountVectorizer(), vectorizer__binary=False, score=0.733, total=   0.2s\n",
      "[CV] clsf__alpha=10.0, vectorizer=CountVectorizer(), vectorizer__binary=False \n",
      "[CV]  clsf__alpha=10.0, vectorizer=CountVectorizer(), vectorizer__binary=False, score=0.774, total=   0.2s\n",
      "[CV] clsf__alpha=10.0, vectorizer=CountVectorizer(), vectorizer__binary=False \n",
      "[CV]  clsf__alpha=10.0, vectorizer=CountVectorizer(), vectorizer__binary=False, score=0.801, total=   0.2s\n",
      "[CV] clsf__alpha=10.0, vectorizer=CountVectorizer(), vectorizer__binary=False \n",
      "[CV]  clsf__alpha=10.0, vectorizer=CountVectorizer(), vectorizer__binary=False, score=0.751, total=   0.2s\n",
      "[CV] clsf__alpha=10.0, vectorizer=CountVectorizer(), vectorizer__binary=False \n",
      "[CV]  clsf__alpha=10.0, vectorizer=CountVectorizer(), vectorizer__binary=False, score=0.727, total=   0.5s\n",
      "[CV] clsf__alpha=10.0, vectorizer=TfidfVectorizer(), vectorizer__binary=True \n",
      "[CV]  clsf__alpha=10.0, vectorizer=TfidfVectorizer(), vectorizer__binary=True, score=0.778, total=   0.5s\n",
      "[CV] clsf__alpha=10.0, vectorizer=TfidfVectorizer(), vectorizer__binary=True \n",
      "[CV]  clsf__alpha=10.0, vectorizer=TfidfVectorizer(), vectorizer__binary=True, score=0.751, total=   0.4s\n",
      "[CV] clsf__alpha=10.0, vectorizer=TfidfVectorizer(), vectorizer__binary=True \n",
      "[CV]  clsf__alpha=10.0, vectorizer=TfidfVectorizer(), vectorizer__binary=True, score=0.796, total=   0.2s\n",
      "[CV] clsf__alpha=10.0, vectorizer=TfidfVectorizer(), vectorizer__binary=True \n",
      "[CV]  clsf__alpha=10.0, vectorizer=TfidfVectorizer(), vectorizer__binary=True, score=0.783, total=   0.3s\n",
      "[CV] clsf__alpha=10.0, vectorizer=TfidfVectorizer(), vectorizer__binary=True \n",
      "[CV]  clsf__alpha=10.0, vectorizer=TfidfVectorizer(), vectorizer__binary=True, score=0.755, total=   0.3s\n",
      "[CV] clsf__alpha=10.0, vectorizer=TfidfVectorizer(), vectorizer__binary=False \n",
      "[CV]  clsf__alpha=10.0, vectorizer=TfidfVectorizer(), vectorizer__binary=False, score=0.747, total=   0.3s\n",
      "[CV] clsf__alpha=10.0, vectorizer=TfidfVectorizer(), vectorizer__binary=False \n",
      "[CV]  clsf__alpha=10.0, vectorizer=TfidfVectorizer(), vectorizer__binary=False, score=0.765, total=   0.2s\n",
      "[CV] clsf__alpha=10.0, vectorizer=TfidfVectorizer(), vectorizer__binary=False \n",
      "[CV]  clsf__alpha=10.0, vectorizer=TfidfVectorizer(), vectorizer__binary=False, score=0.801, total=   0.3s\n",
      "[CV] clsf__alpha=10.0, vectorizer=TfidfVectorizer(), vectorizer__binary=False \n",
      "[CV]  clsf__alpha=10.0, vectorizer=TfidfVectorizer(), vectorizer__binary=False, score=0.774, total=   0.2s\n",
      "[CV] clsf__alpha=10.0, vectorizer=TfidfVectorizer(), vectorizer__binary=False \n",
      "[CV]  clsf__alpha=10.0, vectorizer=TfidfVectorizer(), vectorizer__binary=False, score=0.727, total=   0.3s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 200 out of 200 | elapsed:   56.3s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[('vectorizer', None),\n",
       "                                       ('clsf', MultinomialNB())]),\n",
       "             param_grid=[{'clsf__alpha': array([ 0.1       ,  0.16681005,  0.27825594,  0.46415888,  0.77426368,\n",
       "        1.29154967,  2.15443469,  3.59381366,  5.9948425 , 10.        ]),\n",
       "                          'vectorizer': [CountVectorizer(),\n",
       "                                         TfidfVectorizer(binary=True)],\n",
       "                          'vectorizer__binary': [True, False]}],\n",
       "             scoring='accuracy', verbose=3)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run Cross Validated Grid Search CV\n",
    "gsCV_exp1.fit(X_train_processed,Y_train_int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Model = Pipeline(steps=[('vectorizer', TfidfVectorizer(binary=True)),\n",
      "                ('clsf', MultinomialNB(alpha=0.774263682681127))])\n",
      "Alpha ={'clsf__alpha': 0.774263682681127, 'vectorizer': TfidfVectorizer(binary=True), 'vectorizer__binary': True}\n",
      "Accuracy score = 0.7889263677498971\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Get the best performing model\n",
    "best_param_exp1 = gsCV_exp1.best_params_\n",
    "best_score_exp1 = gsCV_exp1.best_score_\n",
    "best_model_exp1 = gsCV_exp1.best_estimator_\n",
    "print(f'Best Model = {best_model_exp1}\\nAlpha ={best_param_exp1}\\nAccuracy score = {best_score_exp1}\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TfidfVectorizer with the property `binary = True` gives the best cross validation performance. In other word, the presence of specific words seems to be more important than their frequencies.  \n",
    "I am now going to fix these settings for the feature extraction step and perform a GridSearchCV using three different classifiers:\n",
    "- Multinomial Naive Bayes\n",
    "- Logistic regression\n",
    "- SVC\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating the feature extraction and classifier pipeline \n",
    "pipe_exp1 = Pipeline([('vectorizer',TfidfVectorizer(binary=True)),\n",
    "                 ('clsf',MultinomialNB())])\n",
    "# Set the parametes to change during GridSearch\n",
    "param_grid_exp1 = [{'clsf': [MultinomialNB()],\n",
    "              'clsf__alpha': np.logspace(-1, 1, num=10)},\n",
    "              {'clsf': [LogisticRegression()],\n",
    "              'clsf__C': [0.16, 0.32, 0.64, 1.28, 2.56, 3.84, 5.12,5.76,6.36, 7,7.5,7.8,8,8.2,8.5,9]},\n",
    "              {'clsf': [SVC()],\n",
    "              'clsf__C': [0.64, 1.28, 2.56, 3.84, 5.12]}]\n",
    "gsCV_exp1 = GridSearchCV(pipe_exp1, param_grid_exp1, scoring= 'accuracy', cv=5, verbose=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 31 candidates, totalling 155 fits\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.1 ...........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.1, score=0.810, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.1 ...........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.2s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.1, score=0.747, total=   0.2s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.1 ...........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.4s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.1, score=0.824, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.1 ...........................\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.1, score=0.774, total=   0.2s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.1 ...........................\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.1, score=0.723, total=   0.2s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.16681005372000587 ...........\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.16681005372000587, score=0.814, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.16681005372000587 ...........\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.16681005372000587, score=0.751, total=   0.2s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.16681005372000587 ...........\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.16681005372000587, score=0.819, total=   0.2s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.16681005372000587 ...........\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.16681005372000587, score=0.778, total=   0.2s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.16681005372000587 ...........\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.16681005372000587, score=0.741, total=   0.2s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.2782559402207124 ............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.2782559402207124, score=0.810, total=   0.2s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.2782559402207124 ............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.2782559402207124, score=0.751, total=   0.2s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.2782559402207124 ............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.2782559402207124, score=0.833, total=   0.2s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.2782559402207124 ............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.2782559402207124, score=0.792, total=   0.2s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.2782559402207124 ............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.2782559402207124, score=0.745, total=   0.2s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.46415888336127786 ...........\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.46415888336127786, score=0.810, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.46415888336127786 ...........\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.46415888336127786, score=0.760, total=   0.2s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.46415888336127786 ...........\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.46415888336127786, score=0.828, total=   0.2s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.46415888336127786 ...........\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.46415888336127786, score=0.783, total=   0.2s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.46415888336127786 ...........\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.46415888336127786, score=0.755, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.774263682681127 .............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.774263682681127, score=0.810, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.774263682681127 .............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.774263682681127, score=0.760, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.774263682681127 .............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.774263682681127, score=0.819, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.774263682681127 .............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.774263682681127, score=0.792, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.774263682681127 .............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.774263682681127, score=0.764, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=1.291549665014884 .............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=1.291549665014884, score=0.810, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=1.291549665014884 .............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=1.291549665014884, score=0.751, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=1.291549665014884 .............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=1.291549665014884, score=0.810, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=1.291549665014884 .............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=1.291549665014884, score=0.801, total=   0.2s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=1.291549665014884 .............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=1.291549665014884, score=0.759, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=2.1544346900318834 ............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=2.1544346900318834, score=0.801, total=   0.5s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=2.1544346900318834 ............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=2.1544346900318834, score=0.760, total=   0.8s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=2.1544346900318834 ............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=2.1544346900318834, score=0.810, total=   0.4s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=2.1544346900318834 ............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=2.1544346900318834, score=0.801, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=2.1544346900318834 ............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=2.1544346900318834, score=0.759, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=3.593813663804626 .............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=3.593813663804626, score=0.792, total=   0.4s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=3.593813663804626 .............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=3.593813663804626, score=0.751, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=3.593813663804626 .............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=3.593813663804626, score=0.810, total=   0.5s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=3.593813663804626 .............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=3.593813663804626, score=0.796, total=   0.4s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=3.593813663804626 .............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=3.593813663804626, score=0.750, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=5.994842503189409 .............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=5.994842503189409, score=0.787, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=5.994842503189409 .............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=5.994842503189409, score=0.747, total=   0.6s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=5.994842503189409 .............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=5.994842503189409, score=0.805, total=   0.4s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=5.994842503189409 .............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=5.994842503189409, score=0.796, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=5.994842503189409 .............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=5.994842503189409, score=0.745, total=   0.5s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=10.0 ..........................\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=10.0, score=0.778, total=   0.5s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=10.0 ..........................\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=10.0, score=0.751, total=   0.4s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=10.0 ..........................\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=10.0, score=0.796, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=10.0 ..........................\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=10.0, score=0.783, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=10.0 ..........................\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=10.0, score=0.755, total=   0.5s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=0.16 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=0.16, score=0.792, total=   0.7s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=0.16 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=0.16, score=0.778, total=   0.6s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=0.16 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=0.16, score=0.805, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=0.16 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=0.16, score=0.824, total=   0.6s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=0.16 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=0.16, score=0.732, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=0.32 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=0.32, score=0.796, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=0.32 .........................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  clsf=LogisticRegression(), clsf__C=0.32, score=0.787, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=0.32 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=0.32, score=0.805, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=0.32 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=0.32, score=0.824, total=   0.3s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=0.32 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=0.32, score=0.741, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=0.64 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=0.64, score=0.805, total=   0.3s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=0.64 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=0.64, score=0.792, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=0.64 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=0.64, score=0.810, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=0.64 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=0.64, score=0.819, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=0.64 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=0.64, score=0.750, total=   0.3s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=1.28 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=1.28, score=0.819, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=1.28 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=1.28, score=0.787, total=   0.3s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=1.28 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=1.28, score=0.824, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=1.28 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=1.28, score=0.814, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=1.28 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=1.28, score=0.759, total=   0.5s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=2.56 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=2.56, score=0.824, total=   0.5s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=2.56 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=2.56, score=0.787, total=   1.0s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=2.56 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=2.56, score=0.819, total=   0.5s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=2.56 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=2.56, score=0.805, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=2.56 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=2.56, score=0.768, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=3.84 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=3.84, score=0.824, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=3.84 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=3.84, score=0.801, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=3.84 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=3.84, score=0.814, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=3.84 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=3.84, score=0.801, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=3.84 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=3.84, score=0.768, total=   0.5s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=5.12 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=5.12, score=0.819, total=   0.9s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=5.12 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=5.12, score=0.801, total=   1.2s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=5.12 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=5.12, score=0.814, total=   0.5s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=5.12 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=5.12, score=0.801, total=   0.7s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=5.12 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=5.12, score=0.768, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=5.76 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=5.76, score=0.819, total=   0.5s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=5.76 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=5.76, score=0.805, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=5.76 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=5.76, score=0.814, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=5.76 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=5.76, score=0.787, total=   0.6s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=5.76 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=5.76, score=0.768, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=6.36 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=6.36, score=0.824, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=6.36 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=6.36, score=0.801, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=6.36 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=6.36, score=0.814, total=   0.5s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=6.36 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=6.36, score=0.783, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=6.36 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=6.36, score=0.773, total=   0.5s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=7 ............................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=7, score=0.824, total=   0.5s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=7 ............................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=7, score=0.792, total=   0.7s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=7 ............................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=7, score=0.810, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=7 ............................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=7, score=0.783, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=7 ............................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=7, score=0.773, total=   0.3s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=7.5 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=7.5, score=0.819, total=   0.3s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=7.5 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=7.5, score=0.792, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=7.5 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=7.5, score=0.805, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=7.5 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=7.5, score=0.783, total=   0.5s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=7.5 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=7.5, score=0.773, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=7.8 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=7.8, score=0.819, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=7.8 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=7.8, score=0.792, total=   0.5s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=7.8 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=7.8, score=0.801, total=   0.6s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=7.8 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=7.8, score=0.783, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=7.8 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=7.8, score=0.768, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=8 ............................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=8, score=0.819, total=   0.3s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=8 ............................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=8, score=0.792, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=8 ............................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=8, score=0.801, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=8 ............................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  clsf=LogisticRegression(), clsf__C=8, score=0.783, total=   0.6s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=8 ............................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=8, score=0.768, total=   0.5s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=8.2 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=8.2, score=0.819, total=   0.5s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=8.2 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=8.2, score=0.792, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=8.2 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=8.2, score=0.801, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=8.2 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=8.2, score=0.783, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=8.2 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=8.2, score=0.773, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=8.5 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=8.5, score=0.819, total=   0.3s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=8.5 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=8.5, score=0.792, total=   0.3s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=8.5 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=8.5, score=0.801, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=8.5 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=8.5, score=0.783, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=8.5 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=8.5, score=0.773, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=9 ............................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=9, score=0.819, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=9 ............................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=9, score=0.792, total=   0.3s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=9 ............................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=9, score=0.801, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=9 ............................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=9, score=0.783, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=9 ............................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=9, score=0.777, total=   0.4s\n",
      "[CV] clsf=SVC(), clsf__C=0.64 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=0.64, score=0.787, total=   1.6s\n",
      "[CV] clsf=SVC(), clsf__C=0.64 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=0.64, score=0.769, total=   2.0s\n",
      "[CV] clsf=SVC(), clsf__C=0.64 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=0.64, score=0.810, total=   1.9s\n",
      "[CV] clsf=SVC(), clsf__C=0.64 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=0.64, score=0.810, total=   2.5s\n",
      "[CV] clsf=SVC(), clsf__C=0.64 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=0.64, score=0.755, total=   1.8s\n",
      "[CV] clsf=SVC(), clsf__C=1.28 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=1.28, score=0.824, total=   2.0s\n",
      "[CV] clsf=SVC(), clsf__C=1.28 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=1.28, score=0.801, total=   1.6s\n",
      "[CV] clsf=SVC(), clsf__C=1.28 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=1.28, score=0.824, total=   1.8s\n",
      "[CV] clsf=SVC(), clsf__C=1.28 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=1.28, score=0.814, total=   1.6s\n",
      "[CV] clsf=SVC(), clsf__C=1.28 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=1.28, score=0.768, total=   2.0s\n",
      "[CV] clsf=SVC(), clsf__C=2.56 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=2.56, score=0.819, total=   2.2s\n",
      "[CV] clsf=SVC(), clsf__C=2.56 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=2.56, score=0.810, total=   2.1s\n",
      "[CV] clsf=SVC(), clsf__C=2.56 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=2.56, score=0.819, total=   1.6s\n",
      "[CV] clsf=SVC(), clsf__C=2.56 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=2.56, score=0.810, total=   1.7s\n",
      "[CV] clsf=SVC(), clsf__C=2.56 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=2.56, score=0.773, total=   1.6s\n",
      "[CV] clsf=SVC(), clsf__C=3.84 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=3.84, score=0.819, total=   1.6s\n",
      "[CV] clsf=SVC(), clsf__C=3.84 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=3.84, score=0.810, total=   1.5s\n",
      "[CV] clsf=SVC(), clsf__C=3.84 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=3.84, score=0.819, total=   1.6s\n",
      "[CV] clsf=SVC(), clsf__C=3.84 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=3.84, score=0.810, total=   1.7s\n",
      "[CV] clsf=SVC(), clsf__C=3.84 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=3.84, score=0.773, total=   1.6s\n",
      "[CV] clsf=SVC(), clsf__C=5.12 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=5.12, score=0.819, total=   1.7s\n",
      "[CV] clsf=SVC(), clsf__C=5.12 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=5.12, score=0.810, total=   1.6s\n",
      "[CV] clsf=SVC(), clsf__C=5.12 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=5.12, score=0.819, total=   1.6s\n",
      "[CV] clsf=SVC(), clsf__C=5.12 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=5.12, score=0.810, total=   1.8s\n",
      "[CV] clsf=SVC(), clsf__C=5.12 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=5.12, score=0.773, total=   1.7s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 155 out of 155 | elapsed:  1.6min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[('vectorizer',\n",
       "                                        TfidfVectorizer(binary=True)),\n",
       "                                       ('clsf', MultinomialNB())]),\n",
       "             param_grid=[{'clsf': [MultinomialNB()],\n",
       "                          'clsf__alpha': array([ 0.1       ,  0.16681005,  0.27825594,  0.46415888,  0.77426368,\n",
       "        1.29154967,  2.15443469,  3.59381366,  5.9948425 , 10.        ])},\n",
       "                         {'clsf': [LogisticRegression()],\n",
       "                          'clsf__C': [0.16, 0.32, 0.64, 1.28, 2.56, 3.84, 5.12,\n",
       "                                      5.76, 6.36, 7, 7.5, 7.8, 8, 8.2, 8.5,\n",
       "                                      9]},\n",
       "                         {'clsf': [SVC(C=2.56)],\n",
       "                          'clsf__C': [0.64, 1.28, 2.56, 3.84, 5.12]}],\n",
       "             scoring='accuracy', verbose=3)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run GridSearchSV\n",
    "gsCV_exp1.fit(X_train_processed,Y_train_int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Model = Pipeline(steps=[('vectorizer', TfidfVectorizer(binary=True)),\n",
      "                ('clsf', SVC(C=2.56))])\n",
      "Alpha ={'clsf': SVC(C=2.56), 'clsf__C': 2.56}\n",
      "Accuracy score = 0.8061291649526943\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# GET THE BEST MODEL\n",
    "best_param_exp1 = gsCV_exp1.best_params_\n",
    "best_score_exp1 = gsCV_exp1.best_score_\n",
    "best_model_exp1 = gsCV_exp1.best_estimator_\n",
    "print(f'Best Model = {best_model_exp1}\\nAlpha ={best_param_exp1}\\nAccuracy score = {best_score_exp1}\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the cell above shows, by using tf-idf with binary tf term and SVC classifier, the 5-fold average accuracy has improved to a value of 80.6% from 78.89% of the baseline model.\n",
    "This improvement can be partially due to the feature scaling by using tf-idf and the superior performances of CSV with respect to Multinomial Naive Bayes. In fact generative classifiers (like Naive Bayes) usually present lower performances with respect to Discriminative classifier (Like Logistic regression and SVC)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Experiment 2 - Negation Handling\n",
    "The term negation handling refers to the action of appending a suffix (e.g. \"not_\") to words which follow negations like *don't, not, never, etc*. This \"trick\" can help in better distinguish between words that are used in a negative context with respect to the same words used otherwise.\n",
    "\n",
    "For example, let's us analyses two simple phrases \"I don't like black pudding (who does?), but I don't dislike bake beans\" and \"I like sushi but I dislike sashimi\".\n",
    "In the first instance the word like is used in a negative context and the word dislike communicate a  positive sentiment On the other hand in the second phrase the word \"like\" is used in a negative context and the word \"dislike\" communicates a negative sentiment\n",
    "Bag of Words techniques are not able to capture the context in which words are used, therefore it would be impossible to distinguish between the negative and positive sentiment the word \"like\" as well as \"dislike\" may be used for. This would result in a loss of accuracy during the classification phase.\n",
    "We can somehow give a context to words by for example differentiating between \"like\" used in a positive context from the same word like used in a negative context, the same is valid for the word \"dislike\". This can be achieved by appending a suffix \"not_\" to all those words which follow a negation.\n",
    "With this simple trick, the examples I showed before would become: \"I don't not_like black pudding, but I don't not_dislike baked beans\" and the second phrase would remain unaltered as no negation is present. By doing that we have created two new word \"not_like\" and \"not_dislike\" which identify a negative and positive sentiment respectively in contrast to the words like and dislike which identify a positive and negative sentiment respectively.\n",
    "We can see now how the classifier would now be able to better distinguish the sentiment each word is used into.\n",
    "The drawback of such technique is that the feature vector dimensionality may grow substantially resulting in an even sparser input matrix. \n",
    "\n",
    "The are different approaches on how to apply negation handling:\n",
    "The simplest approach is to negate a constant number of words after each negation.\n",
    "One of the most common is to negate all words which follow a negation until a punctuation is encountered.\n",
    "A more sophisticated way of handling negation is to perform a grammatical dependency parsing of the phrase and negate only those words which are in the scope of the negation.\n",
    "\n",
    "In this experiment, since no punctuation is present in the original dataset, I have adopted the first and third approach:\n",
    "\n",
    "- Fixed window negation handling\n",
    "- Dependency parser based Negation Handling\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fixed window Negation Handling\n",
    "I am negating the first 3 words which follow a negation. I have tested different window lengths, the number which gave me the best performances is 3.\n",
    "The negation step is added in the preprocessing pipeline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Helper function which negates\n",
    "def fixed_window_negation(X, window=3):\n",
    "    negationSet = {\"not\", \"never\", \"n\\'t\", \"wasnt\", \"cant\", \"cannot\",\n",
    "                  \"isnt\", \"didnt\", \"dont\", \"shouldnt\", \"wouldnt\", \"nt\"}    \n",
    "    for r_idx, review in enumerate(X):\n",
    "        for w_idx, word in enumerate(review):\n",
    "            if word in negationSet:\n",
    "                for k in range(1,window+1):\n",
    "                    if w_idx < len(review)-(k+1):\n",
    "                        review[w_idx+k] = \"NOT_\" + review[w_idx+k]\n",
    "    return X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new pre-processing pipeline where after tokenize the reviews I use fixed window negation handling.\n",
    "processing_pipe_neg = Pipeline([('lower_case',FunctionTransformer(lower_case)),\n",
    "                            ('tokenize',FunctionTransformer(tokenize)),\n",
    "                            ('fix_wind_negation',FunctionTransformer(fixed_window_negation)),\n",
    "                            ('remove_punct',FunctionTransformer(remove_punct)),\n",
    "                            ('remove_stopwords',FunctionTransformer(remove_stopwords)),\n",
    "                            ('stem',FunctionTransformer(stem)),\n",
    "                            ('stich',FunctionTransformer(stich_tokens))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_train_int = sentiment_to_int(Y_train)\n",
    "X_train_processed = processing_pipe_neg.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initilize the feature_extrantion and classifer pipeline for this experiment\n",
    "pipe_exp_2 = Pipeline([('tfidfVectorize',TfidfVectorizer(binary=True)),\n",
    "                 ('clsf',MultinomialNB())])\n",
    "# Set the parameters to test during GridSearchCV\n",
    "param_grid_exp_2 = [{'clsf': [MultinomialNB()],\n",
    "              'clsf__alpha': np.logspace(-1, 1, num=10)},\n",
    "              {'clsf': [LogisticRegression()],\n",
    "              'clsf__C': [0.16, 0.32, 0.64, 1.28, 2.56, 3.84, 5.12,5.76,6.36, 7,7.5,7.8,8,8.2,8.5,9]},\n",
    "               {'clsf': [SVC()],\n",
    "               'clsf__C': [0.64, 1.28, 2.56, 3.84, 5.12]}]\n",
    "gsCV_exp2= GridSearchCV(pipe_exp_2, param_grid_exp_2, scoring= 'accuracy', cv=5, verbose=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 31 candidates, totalling 155 fits\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.1 ...........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.1, score=0.819, total=   0.5s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.1 ...........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.4s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.1, score=0.760, total=   0.4s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.1 ...........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.9s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.1, score=0.814, total=   0.7s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.1 ...........................\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.1, score=0.774, total=   0.5s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.1 ...........................\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.1, score=0.745, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.16681005372000587 ...........\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.16681005372000587, score=0.814, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.16681005372000587 ...........\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.16681005372000587, score=0.765, total=   0.5s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.16681005372000587 ...........\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.16681005372000587, score=0.814, total=   0.5s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.16681005372000587 ...........\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.16681005372000587, score=0.778, total=   0.4s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.16681005372000587 ...........\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.16681005372000587, score=0.745, total=   0.6s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.2782559402207124 ............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.2782559402207124, score=0.814, total=   0.7s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.2782559402207124 ............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.2782559402207124, score=0.769, total=   0.5s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.2782559402207124 ............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.2782559402207124, score=0.824, total=   0.4s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.2782559402207124 ............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.2782559402207124, score=0.778, total=   0.8s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.2782559402207124 ............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.2782559402207124, score=0.755, total=   0.8s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.46415888336127786 ...........\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.46415888336127786, score=0.810, total=   1.0s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.46415888336127786 ...........\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.46415888336127786, score=0.765, total=   0.7s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.46415888336127786 ...........\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.46415888336127786, score=0.828, total=   0.6s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.46415888336127786 ...........\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.46415888336127786, score=0.792, total=   0.7s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.46415888336127786 ...........\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.46415888336127786, score=0.750, total=   0.9s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.774263682681127 .............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.774263682681127, score=0.814, total=   0.8s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.774263682681127 .............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.774263682681127, score=0.778, total=   1.0s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.774263682681127 .............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.774263682681127, score=0.828, total=   0.9s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.774263682681127 .............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.774263682681127, score=0.792, total=   0.7s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.774263682681127 .............\n",
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      "[CV] clsf=MultinomialNB(), clsf__alpha=1.291549665014884 .............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=1.291549665014884, score=0.805, total=   0.8s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=1.291549665014884 .............\n",
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      "[CV] clsf=MultinomialNB(), clsf__alpha=1.291549665014884 .............\n",
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      "[CV] clsf=MultinomialNB(), clsf__alpha=1.291549665014884 .............\n",
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      "[CV] clsf=MultinomialNB(), clsf__alpha=1.291549665014884 .............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=1.291549665014884, score=0.750, total=   1.1s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=2.1544346900318834 ............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=2.1544346900318834, score=0.801, total=   1.0s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=2.1544346900318834 ............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=2.1544346900318834, score=0.765, total=   1.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=2.1544346900318834 ............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=2.1544346900318834, score=0.828, total=   1.2s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=2.1544346900318834 ............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=2.1544346900318834, score=0.783, total=   1.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=2.1544346900318834 ............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=2.1544346900318834, score=0.745, total=   0.7s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=3.593813663804626 .............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=3.593813663804626, score=0.796, total=   0.9s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=3.593813663804626 .............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=3.593813663804626, score=0.765, total=   0.8s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=3.593813663804626 .............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=3.593813663804626, score=0.828, total=   1.5s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=3.593813663804626 .............\n",
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      "[CV] clsf=MultinomialNB(), clsf__alpha=3.593813663804626 .............\n",
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      "[CV] clsf=MultinomialNB(), clsf__alpha=5.994842503189409 .............\n",
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      "[CV] clsf=MultinomialNB(), clsf__alpha=5.994842503189409 .............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=5.994842503189409, score=0.756, total=   1.0s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=5.994842503189409 .............\n",
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      "[CV] clsf=MultinomialNB(), clsf__alpha=5.994842503189409 .............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=5.994842503189409, score=0.769, total=   1.1s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=5.994842503189409 .............\n",
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      "[CV] clsf=MultinomialNB(), clsf__alpha=10.0 ..........................\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=10.0, score=0.787, total=   1.4s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=10.0 ..........................\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=10.0, score=0.760, total=   1.1s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=10.0 ..........................\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=10.0, score=0.796, total=   2.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=10.0 ..........................\n",
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      "[CV] clsf=MultinomialNB(), clsf__alpha=10.0 ..........................\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=10.0, score=0.745, total=   2.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=0.16 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=0.16, score=0.787, total=   3.6s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=0.16 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=0.16, score=0.796, total=   0.7s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=0.16 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=0.16, score=0.828, total=   2.0s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=0.16 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=0.16, score=0.828, total=   1.8s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=0.16 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=0.16, score=0.741, total=   0.8s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=0.32 .........................\n",
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      "[CV] clsf=LogisticRegression(), clsf__C=0.32 .........................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  clsf=LogisticRegression(), clsf__C=0.32, score=0.801, total=   0.8s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=0.32 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=0.32, score=0.824, total=   1.0s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=0.32 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=0.32, score=0.833, total=   0.7s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=0.32 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=0.32, score=0.750, total=   0.5s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=0.64 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=0.64, score=0.805, total=   0.7s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=0.64 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=0.64, score=0.801, total=   0.6s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=0.64 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=0.64, score=0.828, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=0.64 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=0.64, score=0.837, total=   0.6s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=0.64 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=0.64, score=0.759, total=   0.3s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=1.28 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=1.28, score=0.828, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=1.28 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=1.28, score=0.796, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=1.28 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=1.28, score=0.837, total=   0.5s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=1.28 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=1.28, score=0.819, total=   0.5s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=1.28 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=1.28, score=0.782, total=   0.5s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=2.56 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=2.56, score=0.837, total=   0.3s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=2.56 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=2.56, score=0.814, total=   0.5s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=2.56 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=2.56, score=0.837, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=2.56 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=2.56, score=0.819, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=2.56 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=2.56, score=0.786, total=   0.3s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=3.84 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=3.84, score=0.837, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=3.84 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=3.84, score=0.810, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=3.84 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=3.84, score=0.837, total=   0.3s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=3.84 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=3.84, score=0.819, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=3.84 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=3.84, score=0.795, total=   0.3s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=5.12 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=5.12, score=0.837, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=5.12 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=5.12, score=0.814, total=   0.5s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=5.12 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=5.12, score=0.828, total=   0.6s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=5.12 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=5.12, score=0.824, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=5.12 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=5.12, score=0.791, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=5.76 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=5.76, score=0.833, total=   0.5s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=5.76 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=5.76, score=0.814, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=5.76 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=5.76, score=0.828, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=5.76 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=5.76, score=0.824, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=5.76 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=5.76, score=0.795, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=6.36 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=6.36, score=0.828, total=   0.5s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=6.36 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=6.36, score=0.814, total=   0.6s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=6.36 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=6.36, score=0.828, total=   0.7s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=6.36 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=6.36, score=0.824, total=   1.2s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=6.36 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=6.36, score=0.795, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=7 ............................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=7, score=0.828, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=7 ............................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=7, score=0.819, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=7 ............................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=7, score=0.828, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=7 ............................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=7, score=0.824, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=7 ............................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=7, score=0.800, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=7.5 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=7.5, score=0.828, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=7.5 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=7.5, score=0.819, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=7.5 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=7.5, score=0.828, total=   0.5s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=7.5 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=7.5, score=0.824, total=   0.6s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=7.5 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=7.5, score=0.800, total=   0.5s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=7.8 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=7.8, score=0.828, total=   0.6s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=7.8 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=7.8, score=0.819, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=7.8 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=7.8, score=0.824, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=7.8 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=7.8, score=0.824, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=7.8 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=7.8, score=0.795, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=8 ............................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=8, score=0.828, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=8 ............................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=8, score=0.819, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=8 ............................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=8, score=0.824, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=8 ............................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  clsf=LogisticRegression(), clsf__C=8, score=0.824, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=8 ............................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=8, score=0.795, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=8.2 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=8.2, score=0.828, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=8.2 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=8.2, score=0.819, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=8.2 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=8.2, score=0.824, total=   0.5s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=8.2 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=8.2, score=0.824, total=   0.5s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=8.2 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=8.2, score=0.795, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=8.5 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=8.5, score=0.828, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=8.5 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=8.5, score=0.819, total=   0.5s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=8.5 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=8.5, score=0.824, total=   0.5s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=8.5 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=8.5, score=0.814, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=8.5 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=8.5, score=0.795, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=9 ............................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=9, score=0.833, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=9 ............................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=9, score=0.824, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=9 ............................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=9, score=0.824, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=9 ............................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=9, score=0.814, total=   0.5s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=9 ............................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=9, score=0.795, total=   0.5s\n",
      "[CV] clsf=SVC(), clsf__C=0.64 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=0.64, score=0.796, total=   1.7s\n",
      "[CV] clsf=SVC(), clsf__C=0.64 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=0.64, score=0.801, total=   1.7s\n",
      "[CV] clsf=SVC(), clsf__C=0.64 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=0.64, score=0.814, total=   2.0s\n",
      "[CV] clsf=SVC(), clsf__C=0.64 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=0.64, score=0.801, total=   1.5s\n",
      "[CV] clsf=SVC(), clsf__C=0.64 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=0.64, score=0.750, total=   1.5s\n",
      "[CV] clsf=SVC(), clsf__C=1.28 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=1.28, score=0.828, total=   1.7s\n",
      "[CV] clsf=SVC(), clsf__C=1.28 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=1.28, score=0.814, total=   1.6s\n",
      "[CV] clsf=SVC(), clsf__C=1.28 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=1.28, score=0.846, total=   1.7s\n",
      "[CV] clsf=SVC(), clsf__C=1.28 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=1.28, score=0.824, total=   2.9s\n",
      "[CV] clsf=SVC(), clsf__C=1.28 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=1.28, score=0.777, total=   2.3s\n",
      "[CV] clsf=SVC(), clsf__C=2.56 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=2.56, score=0.833, total=   2.8s\n",
      "[CV] clsf=SVC(), clsf__C=2.56 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=2.56, score=0.833, total=   2.0s\n",
      "[CV] clsf=SVC(), clsf__C=2.56 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=2.56, score=0.837, total=   1.8s\n",
      "[CV] clsf=SVC(), clsf__C=2.56 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=2.56, score=0.824, total=   2.0s\n",
      "[CV] clsf=SVC(), clsf__C=2.56 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=2.56, score=0.786, total=   1.8s\n",
      "[CV] clsf=SVC(), clsf__C=3.84 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=3.84, score=0.833, total=   1.6s\n",
      "[CV] clsf=SVC(), clsf__C=3.84 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=3.84, score=0.833, total=   3.5s\n",
      "[CV] clsf=SVC(), clsf__C=3.84 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=3.84, score=0.837, total=   3.1s\n",
      "[CV] clsf=SVC(), clsf__C=3.84 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=3.84, score=0.824, total=   1.8s\n",
      "[CV] clsf=SVC(), clsf__C=3.84 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=3.84, score=0.786, total=   2.0s\n",
      "[CV] clsf=SVC(), clsf__C=5.12 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=5.12, score=0.833, total=   1.6s\n",
      "[CV] clsf=SVC(), clsf__C=5.12 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=5.12, score=0.833, total=   1.6s\n",
      "[CV] clsf=SVC(), clsf__C=5.12 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=5.12, score=0.837, total=   2.3s\n",
      "[CV] clsf=SVC(), clsf__C=5.12 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=5.12, score=0.824, total=   1.7s\n",
      "[CV] clsf=SVC(), clsf__C=5.12 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=5.12, score=0.786, total=   1.6s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 155 out of 155 | elapsed:  2.4min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[('tfidfVectorize',\n",
       "                                        TfidfVectorizer(binary=True)),\n",
       "                                       ('clsf', MultinomialNB())]),\n",
       "             param_grid=[{'clsf': [MultinomialNB()],\n",
       "                          'clsf__alpha': array([ 0.1       ,  0.16681005,  0.27825594,  0.46415888,  0.77426368,\n",
       "        1.29154967,  2.15443469,  3.59381366,  5.9948425 , 10.        ])},\n",
       "                         {'clsf': [LogisticRegression()],\n",
       "                          'clsf__C': [0.16, 0.32, 0.64, 1.28, 2.56, 3.84, 5.12,\n",
       "                                      5.76, 6.36, 7, 7.5, 7.8, 8, 8.2, 8.5,\n",
       "                                      9]},\n",
       "                         {'clsf': [SVC(C=2.56)],\n",
       "                          'clsf__C': [0.64, 1.28, 2.56, 3.84, 5.12]}],\n",
       "             scoring='accuracy', verbose=3)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run GridSearchCV\n",
    "gsCV_exp2.fit(X_train_processed,Y_train_int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Model = Pipeline(steps=[('tfidfVectorize', TfidfVectorizer(binary=True)),\n",
      "                ('clsf', SVC(C=2.56))])\n",
      "Alpha ={'clsf': SVC(C=2.56), 'clsf__C': 2.56}\n",
      "Accuracy score = 0.8224310983134512\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# GET THE BEST MODEL\n",
    "best_param_exp2 = gsCV_exp2.best_params_\n",
    "best_score_exp2 = gsCV_exp2.best_score_\n",
    "best_model_exp2 = gsCV_exp2.best_estimator_\n",
    "print(f'Best Model = {best_model_exp2}\\nAlpha ={best_param_exp2}\\nAccuracy score = {best_score_exp2}\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By simply appending a suffix (not_) to a fixed number of words after the detected negation the average accuracy after 5-fold cross validation increased to 82.24% from 80.61 obtained in the previous experiment. It is worth noting that the only difference between the two experiments is the word negations, the same vectorizer and same classifiers set were used."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Experiment 3 - Dependency parser based Negation Handling\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fixed window negation handling, although of simple application, might turn out to be inaccurate in the choice of words to negate. To overcome this I am experimenting with a more sophisticated approach which attempts to only negate words in the scope of the negation. In other to do this I need to use a grammatical dependency parser and represent the outcome in a tree structure. I have used the NLP package by Stanford University called Stanza to analyses the phrase sintax constituency dependency. The parser returns a tree structure of the input text. I have then develop an algorithm to traverse the tree structure , identify the negation words and negate (namely append suffix \"_not\") all those words which share the same parent node with the negation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Uncomment the line below to install stanza:\n",
    "# Stanza is a nlp python mudule by Standford University. \n",
    "#!python -m pip install -U stanza"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ae6348f8bf784e65b3e75c02c21fb782",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Downloading https://raw.githubusercontent.com/stanfordnlp/stanza-resources/main/res…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-12-13 11:16:43 INFO: Downloading default packages for language: en (English)...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-12-13 11:16:45 INFO: File exists: C:\\Users\\mcarciani\\stanza_resources\\en\\default.zip.\n",
      "2021-12-13 11:16:49 INFO: Finished downloading models and saved to C:\\Users\\mcarciani\\stanza_resources.\n"
     ]
    }
   ],
   "source": [
    "import stanza\n",
    "from nltk.tree import Tree\n",
    "import copy\n",
    "import time\n",
    "stanza.download('en');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-12-13 11:16:49 INFO: Loading these models for language: en (English):\n",
      "===========================\n",
      "| Processor    | Package  |\n",
      "---------------------------\n",
      "| tokenize     | combined |\n",
      "| pos          | combined |\n",
      "| constituency | wsj      |\n",
      "===========================\n",
      "\n",
      "2021-12-13 11:16:49 INFO: Use device: cpu\n",
      "2021-12-13 11:16:49 INFO: Loading: tokenize\n",
      "2021-12-13 11:16:49 INFO: Loading: pos\n",
      "2021-12-13 11:16:50 INFO: Loading: constituency\n",
      "2021-12-13 11:16:51 INFO: Done loading processors!\n"
     ]
    }
   ],
   "source": [
    "nlp = stanza.Pipeline(lang='en', processors='tokenize,pos,constituency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Helper  function to traverse the dependency tree and apply the suffix not_ to all words following a negation which share the \n",
    "# same parent node\n",
    "def negation_tree_parsing(tree, negate=False):\n",
    "    negate_start = negate\n",
    "    if tree.children[0].is_leaf() and negate:\n",
    "        tree.children[0].label = \"not_\" + tree.children[0].label\n",
    "        return negate\n",
    "    if tree.children[0].is_leaf() and tree.children[0].label in {\"not\", \"never\", \"n\\'t\", \"wasnt\", \"cant\", \"cannot\",\n",
    "                                                                 \"isnt\", \"didnt\", \"dont\", \"shouldnt\", \"wouldnt\", \"nt\"}:\n",
    "        return not negate\n",
    "\n",
    "    if tree.children[0].is_leaf():\n",
    "        return negate\n",
    "\n",
    "    chldr = tree.children\n",
    "\n",
    "    for ch in chldr:\n",
    "        negate = negation_tree_parsing(ch, negate)\n",
    "\n",
    "    if negate_start != negate:\n",
    "        return negate_start\n",
    "    return negate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the cell below there is an example on how the dependecy parser negation handling process works."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original sentece: I don't like broccoly but I don't dislike cabbage\n",
      "Negated sentence: I do n't not_like not_broccoly but I do n't not_dislike not_cabbage\n"
     ]
    }
   ],
   "source": [
    "# EXAMPLE\n",
    "text = \"I don't like broccoly but I don't dislike cabbage\"\n",
    "doc = nlp(text)\n",
    "for sentence in doc.sentences:\n",
    "    tree = sentence.constituency\n",
    "    negation_tree_parsing(tree)\n",
    "    print(f\"Original sentece: {text}\")\n",
    "    text_out = \" \".join(tree.leaf_labels())\n",
    "    print(f\"Negated sentence: {text_out}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analyzing the reviews syntax dependencies and apply negation handling by tree traversal could be incorporated in the preprocessing pipeline. This would considerably increase the notebook runtime and it is mainly for this reason  I have already preprocessed all reviews and saved the results on one additional file *car-reviews-negated.csv* which can be found in the same zip folder as this notebook. The cell below contains the code which produces such file. It can be run by setting `do_from_scratch = True`.\n",
    "All negated reviews are automatically loaded from the csv if `do_from_scratch = False`  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "do_from_scratch = False\n",
    "if do_from_scratch:\n",
    "# parse the reviews dependecies and apply negation handling by traversing the resulting tree.\n",
    "    import copy\n",
    "    reviews_negated = []\n",
    "    nlp_failed_on = []\n",
    "    i = 0\n",
    "    t_start = time.process_time()\n",
    "    for rev_idx, rev in enumerate(df.Review):\n",
    "        try:\n",
    "            # parse dependency\n",
    "            doc = nlp(rev)\n",
    "        except:\n",
    "            reviews_negated.append(rev)\n",
    "            nlp_failed_on.append(rev_idx)\n",
    "            continue\n",
    "        sentences_negated = []\n",
    "        for sentence in doc.sentences:\n",
    "            tree = sentence.constituency\n",
    "            # traverse the tree and apply negation\n",
    "            negation_tree_parsing(tree)\n",
    "            # rejoin all tree leaf nodes to form the negated review\n",
    "            sentences_negated.append(\" \".join(tree.leaf_labels()))\n",
    "        reviews_negated.append(\" \".join(sentences_negated))\n",
    "        if len(reviews_negated)>2:\n",
    "            # check that everything is working fine\n",
    "            assert reviews_negated[-1] != reviews_negated[-2]\n",
    "        i +=1\n",
    "    t_elapsed = time.process_time() - t_start\n",
    "    print(f\"Processing time = {t_elapsed}\")\n",
    "    \n",
    "    df_reviews_negated = pd.DataFrame(df.Sentiment)\n",
    "    df_reviews_negated['Review'] = pd.DataFrame(reviews_negated)\n",
    "    # save the negated reviews to a csv file\n",
    "    df_reviews_negated.to_csv('car-reviews-negated.csv',index=False)\n",
    "else:\n",
    "    # load the already negated reviews from the csv file.\n",
    "    df_reviews_negated = pd.read_csv('car-reviews-negated.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'You may find the same thing happening to your comfort Now seating comfort may not not_be not_important not_to not_you but it is to me The fuel usage was fine the car did get us there with no problems but it was such an uncomfortable ride for both of us The last business trip I drove to San Francisco I went to Hertz Rentals and got a 1999 Ford Taurus thinking it looked comfortable and professional I found the seating to be uncomfortable for myself as well as for my passenger It offers comforts that the Taurus simply does not not_have not_If not_you not_are not_planning not_a not_long not_trip not_or not_to not_buy not_a not_car not_that not_you not_intend not_to not_be not_comfortably not_seated not_in not_check not_out not_the not_Taurus not_on not_a not_longer not_trial not_basis not_than not_you not_might not_another not_car How important is comfort to you On a long trip being an uncomfortable driver is distracting to me and it made the trip seem unnecessarily longer than it should have felt I make this trip a lot The Saturn would have been a better choice for me as I have rented this model quite often It is not not_as not_though not_I not_am not_hard not_to not_fit not_into not_a not_car not_I not_am not_5 not_5 not_weigh not_115 not_pounds not_and not_I not_am not_usually not_quite not_comfortable not_in not_most not_any not_car not_But not_the not_Taurus not_seemed not_especially not_uncomfortable not_For not_anyone not_who not_is not_thinking not_of not_renting not_a not_car not_for not_a not_long not_trip not_I not_would not_suggest not_that not_the not_Ford not_Taurus not_1999 not_not not_be not_on not_the not_list not_of not_be not_sure not_to not_get not_this not_There not_was not_a not_large not_trunk not_that not_allowed not_us not_to not_pack not_a not_lot not_of not_materials not_needed not_for not_the not_business not_end not_of not_the not_trip not_Driving not_it not_was not_a not_breeze not_and not_everything not_worked not_right not_I not_have not_no not_complaints not_about not_the not_standard not_making not_of not_the not_car not_except not_in not_the not_comfort not_area'"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# example of a review where dependecy parsing negation handling has been applied.\n",
    "df_reviews_negated.Review[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since all negated reviews are taken from a cvs file I now need re-split the negated reviews in the exactly the same way I did for the original dataset.  \n",
    " **NOTE**: The random state is set to the same value as previously to assure the training and test contains the same reviews as in the original splitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separate the negative and positive reviews\n",
    "df_pos_rn = df_reviews_negated[df_reviews_negated['Sentiment'] == 'Pos']\n",
    "df_neg_rn = df_reviews_negated[df_reviews_negated['Sentiment'] == 'Neg']\n",
    "\n",
    "# split both positive and negative set in a 80% 20% fashion\n",
    "train_pos_rn, test_pos_rn = train_test_split(df_pos_rn,test_size=0.2,random_state=1)\n",
    "train_neg_rn, test_neg_rn = train_test_split(df_neg_rn,test_size=0.2,random_state=1)\n",
    "\n",
    "# Create train set and test set\n",
    "train_data_rn = pd.concat([train_pos_rn,train_neg_rn])\n",
    "train_data_rn.sample(frac=1, random_state=1)\n",
    "test_data_rn = pd.concat([test_pos_rn,test_neg_rn])\n",
    "# No need to shuffle the test data\n",
    "\n",
    "# Extract reviews and lable for the train data\n",
    "X_train_rn = train_data_rn.Review\n",
    "Y_train_rn = train_data_rn.Sentiment\n",
    "\n",
    "# Extract reviews and lable for the test data\n",
    "X_test_rn = test_data_rn.Review\n",
    "Y_test_rn = test_data_rn.Sentiment\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I now apply the same preprocessing to the negated review dataset as I did for the non-negated reviews."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_train_int = sentiment_to_int(Y_train_rn)\n",
    "X_train_processed =processing_pipe.transform(X_train_rn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initilize the feature_extrantion and classifer pipeline for this experiment\n",
    "pipe_exp3 = Pipeline([('tfidfVectorize',TfidfVectorizer(binary=False)),\n",
    "                 ('clsf',MultinomialNB())])\n",
    "# Set the parameters to test during GridSearchCV\n",
    "param_grid_exp3 = [{'clsf': [MultinomialNB()],\n",
    "              'clsf__alpha': np.logspace(-2, 1, num=20)},\n",
    "              {'clsf': [LogisticRegression()],\n",
    "              'clsf__C': [0.16, 0.32, 0.64, 1.28, 2.56, 3.84, 5.12,5.76,6.36, 7,7.5,7.8,8,8.2,8.5,9]},\n",
    "              {'clsf': [SVC()],\n",
    "               'clsf__C': [0.64, 1.28, 2.56, 3.84, 5.12]}]\n",
    "gsCV_exp3 = GridSearchCV(pipe_exp3, param_grid_exp3, scoring= 'accuracy', cv=5, verbose=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 41 candidates, totalling 205 fits\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.01 ..........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.01, score=0.778, total=   0.4s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.01 ..........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.3s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.01, score=0.742, total=   0.2s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.01 ..........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.5s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.01, score=0.792, total=   0.2s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.01 ..........................\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.01, score=0.756, total=   0.2s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.01 ..........................\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.01, score=0.745, total=   0.2s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.01438449888287663 ...........\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.01438449888287663, score=0.778, total=   0.2s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.01438449888287663 ...........\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.01438449888287663, score=0.760, total=   0.2s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.01438449888287663 ...........\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.01438449888287663, score=0.792, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.01438449888287663 ...........\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.01438449888287663, score=0.769, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.01438449888287663 ...........\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.01438449888287663, score=0.759, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.0206913808111479 ............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.0206913808111479, score=0.769, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.0206913808111479 ............\n",
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      "[CV] clsf=MultinomialNB(), clsf__alpha=0.0206913808111479 ............\n",
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      "[CV] clsf=MultinomialNB(), clsf__alpha=0.0206913808111479 ............\n",
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      "[CV] clsf=MultinomialNB(), clsf__alpha=0.0206913808111479 ............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.0206913808111479, score=0.759, total=   0.2s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.029763514416313176 ..........\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.029763514416313176, score=0.778, total=   0.2s\n",
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      "[CV] clsf=MultinomialNB(), clsf__alpha=0.029763514416313176 ..........\n",
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      "[CV] clsf=MultinomialNB(), clsf__alpha=0.08858667904100823 ...........\n",
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      "[CV] clsf=MultinomialNB(), clsf__alpha=0.12742749857031335 ...........\n",
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      "[CV] clsf=MultinomialNB(), clsf__alpha=0.18329807108324356 ...........\n",
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      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.18329807108324356, score=0.805, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.18329807108324356 ...........\n",
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      "[CV] clsf=MultinomialNB(), clsf__alpha=0.26366508987303583 ...........\n",
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      "[CV] clsf=MultinomialNB(), clsf__alpha=0.26366508987303583 ...........\n",
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      "[CV] clsf=MultinomialNB(), clsf__alpha=0.26366508987303583 ...........\n",
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      "[CV] clsf=MultinomialNB(), clsf__alpha=0.26366508987303583 ...........\n",
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      "[CV] clsf=MultinomialNB(), clsf__alpha=0.26366508987303583 ...........\n",
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      "[CV] clsf=MultinomialNB(), clsf__alpha=0.37926901907322497 ...........\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.37926901907322497, score=0.787, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.37926901907322497 ...........\n",
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      "[CV] clsf=MultinomialNB(), clsf__alpha=0.37926901907322497 ...........\n",
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      "[CV] clsf=MultinomialNB(), clsf__alpha=0.37926901907322497 ...........\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.37926901907322497, score=0.750, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.5455594781168517 ............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.5455594781168517, score=0.783, total=   0.4s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.5455594781168517 ............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.5455594781168517, score=0.778, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.5455594781168517 ............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.5455594781168517, score=0.814, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.5455594781168517 ............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.5455594781168517, score=0.769, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.5455594781168517 ............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.5455594781168517, score=0.736, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.7847599703514611 ............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.7847599703514611, score=0.783, total=   0.4s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.7847599703514611 ............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.7847599703514611, score=0.774, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.7847599703514611 ............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.7847599703514611, score=0.814, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.7847599703514611 ............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.7847599703514611, score=0.769, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=0.7847599703514611 ............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=0.7847599703514611, score=0.727, total=   0.2s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=1.1288378916846884 ............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=1.1288378916846884, score=0.778, total=   0.2s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=1.1288378916846884 ............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=1.1288378916846884, score=0.774, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=1.1288378916846884 ............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=1.1288378916846884, score=0.814, total=   0.2s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=1.1288378916846884 ............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=1.1288378916846884, score=0.769, total=   0.2s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=1.1288378916846884 ............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=1.1288378916846884, score=0.727, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=1.623776739188721 .............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=1.623776739188721, score=0.765, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=1.623776739188721 .............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=1.623776739188721, score=0.765, total=   0.4s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=1.623776739188721 .............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=1.623776739188721, score=0.819, total=   0.4s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=1.623776739188721 .............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=1.623776739188721, score=0.769, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=1.623776739188721 .............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=1.623776739188721, score=0.727, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=2.3357214690901213 ............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=2.3357214690901213, score=0.760, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=2.3357214690901213 ............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=2.3357214690901213, score=0.774, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=2.3357214690901213 ............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=2.3357214690901213, score=0.819, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=2.3357214690901213 ............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=2.3357214690901213, score=0.765, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=2.3357214690901213 ............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=2.3357214690901213, score=0.718, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=3.359818286283781 .............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=3.359818286283781, score=0.738, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=3.359818286283781 .............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=3.359818286283781, score=0.774, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=3.359818286283781 .............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=3.359818286283781, score=0.819, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=3.359818286283781 .............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=3.359818286283781, score=0.760, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=3.359818286283781 .............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=3.359818286283781, score=0.709, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=4.832930238571752 .............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=4.832930238571752, score=0.738, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=4.832930238571752 .............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=4.832930238571752, score=0.760, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=4.832930238571752 .............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=4.832930238571752, score=0.810, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=4.832930238571752 .............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=4.832930238571752, score=0.765, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=4.832930238571752 .............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=4.832930238571752, score=0.709, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=6.951927961775605 .............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=6.951927961775605, score=0.724, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=6.951927961775605 .............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=6.951927961775605, score=0.756, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=6.951927961775605 .............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=6.951927961775605, score=0.810, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=6.951927961775605 .............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=6.951927961775605, score=0.765, total=   0.4s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=6.951927961775605 .............\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=6.951927961775605, score=0.718, total=   0.4s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=10.0 ..........................\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=10.0, score=0.738, total=   0.5s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=10.0 ..........................\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=10.0, score=0.751, total=   0.4s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=10.0 ..........................\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=10.0, score=0.796, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=10.0 ..........................\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=10.0, score=0.756, total=   0.3s\n",
      "[CV] clsf=MultinomialNB(), clsf__alpha=10.0 ..........................\n",
      "[CV]  clsf=MultinomialNB(), clsf__alpha=10.0, score=0.714, total=   0.3s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=0.16 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=0.16, score=0.733, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=0.16 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=0.16, score=0.796, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=0.16 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=0.16, score=0.801, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=0.16 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=0.16, score=0.774, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=0.16 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=0.16, score=0.723, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=0.32 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=0.32, score=0.751, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=0.32 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=0.32, score=0.796, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=0.32 .........................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  clsf=LogisticRegression(), clsf__C=0.32, score=0.824, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=0.32 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=0.32, score=0.783, total=   0.3s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=0.32 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=0.32, score=0.723, total=   0.6s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=0.64 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=0.64, score=0.760, total=   0.6s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=0.64 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=0.64, score=0.792, total=   0.6s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=0.64 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=0.64, score=0.851, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=0.64 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=0.64, score=0.792, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=0.64 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=0.64, score=0.723, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=1.28 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=1.28, score=0.774, total=   0.5s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=1.28 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=1.28, score=0.787, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=1.28 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=1.28, score=0.846, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=1.28 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=1.28, score=0.828, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=1.28 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=1.28, score=0.755, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=2.56 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=2.56, score=0.774, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=2.56 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=2.56, score=0.796, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=2.56 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=2.56, score=0.851, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=2.56 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=2.56, score=0.824, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=2.56 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=2.56, score=0.777, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=3.84 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=3.84, score=0.774, total=   0.5s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=3.84 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=3.84, score=0.792, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=3.84 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=3.84, score=0.828, total=   0.5s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=3.84 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=3.84, score=0.805, total=   0.5s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=3.84 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=3.84, score=0.786, total=   0.5s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=5.12 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=5.12, score=0.778, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=5.12 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=5.12, score=0.796, total=   0.7s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=5.12 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=5.12, score=0.833, total=   0.6s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=5.12 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=5.12, score=0.814, total=   0.5s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=5.12 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=5.12, score=0.786, total=   0.5s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=5.76 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=5.76, score=0.778, total=   0.5s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=5.76 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=5.76, score=0.801, total=   0.5s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=5.76 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=5.76, score=0.837, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=5.76 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=5.76, score=0.814, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=5.76 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=5.76, score=0.786, total=   0.5s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=6.36 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=6.36, score=0.774, total=   0.5s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=6.36 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=6.36, score=0.801, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=6.36 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=6.36, score=0.828, total=   0.5s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=6.36 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=6.36, score=0.819, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=6.36 .........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=6.36, score=0.786, total=   0.5s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=7 ............................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=7, score=0.769, total=   0.5s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=7 ............................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=7, score=0.801, total=   0.6s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=7 ............................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=7, score=0.828, total=   0.5s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=7 ............................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=7, score=0.819, total=   0.5s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=7 ............................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=7, score=0.782, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=7.5 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=7.5, score=0.769, total=   0.5s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=7.5 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=7.5, score=0.796, total=   0.5s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=7.5 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=7.5, score=0.828, total=   0.5s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=7.5 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=7.5, score=0.824, total=   0.5s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=7.5 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=7.5, score=0.782, total=   0.5s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=7.8 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=7.8, score=0.769, total=   0.7s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=7.8 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=7.8, score=0.796, total=   0.8s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=7.8 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=7.8, score=0.828, total=   0.5s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=7.8 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=7.8, score=0.819, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=7.8 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=7.8, score=0.782, total=   0.7s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=8 ............................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=8, score=0.769, total=   0.7s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=8 ............................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=8, score=0.796, total=   0.8s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=8 ............................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=8, score=0.828, total=   0.7s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=8 ............................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=8, score=0.824, total=   0.8s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=8 ............................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  clsf=LogisticRegression(), clsf__C=8, score=0.782, total=   0.9s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=8.2 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=8.2, score=0.769, total=   1.3s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=8.2 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=8.2, score=0.796, total=   0.9s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=8.2 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=8.2, score=0.833, total=   0.8s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=8.2 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=8.2, score=0.824, total=   0.5s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=8.2 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=8.2, score=0.782, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=8.5 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=8.5, score=0.769, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=8.5 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=8.5, score=0.796, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=8.5 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=8.5, score=0.833, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=8.5 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=8.5, score=0.824, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=8.5 ..........................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=8.5, score=0.777, total=   0.5s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=9 ............................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=9, score=0.769, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=9 ............................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=9, score=0.792, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=9 ............................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=9, score=0.833, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=9 ............................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=9, score=0.814, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(), clsf__C=9 ............................\n",
      "[CV]  clsf=LogisticRegression(), clsf__C=9, score=0.777, total=   0.4s\n",
      "[CV] clsf=SVC(), clsf__C=0.64 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=0.64, score=0.769, total=   1.6s\n",
      "[CV] clsf=SVC(), clsf__C=0.64 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=0.64, score=0.805, total=   1.5s\n",
      "[CV] clsf=SVC(), clsf__C=0.64 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=0.64, score=0.810, total=   1.5s\n",
      "[CV] clsf=SVC(), clsf__C=0.64 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=0.64, score=0.796, total=   1.6s\n",
      "[CV] clsf=SVC(), clsf__C=0.64 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=0.64, score=0.745, total=   1.6s\n",
      "[CV] clsf=SVC(), clsf__C=1.28 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=1.28, score=0.760, total=   1.7s\n",
      "[CV] clsf=SVC(), clsf__C=1.28 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=1.28, score=0.801, total=   1.5s\n",
      "[CV] clsf=SVC(), clsf__C=1.28 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=1.28, score=0.855, total=   2.5s\n",
      "[CV] clsf=SVC(), clsf__C=1.28 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=1.28, score=0.837, total=   1.6s\n",
      "[CV] clsf=SVC(), clsf__C=1.28 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=1.28, score=0.777, total=   1.6s\n",
      "[CV] clsf=SVC(), clsf__C=2.56 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=2.56, score=0.760, total=   1.5s\n",
      "[CV] clsf=SVC(), clsf__C=2.56 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=2.56, score=0.778, total=   1.4s\n",
      "[CV] clsf=SVC(), clsf__C=2.56 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=2.56, score=0.860, total=   1.6s\n",
      "[CV] clsf=SVC(), clsf__C=2.56 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=2.56, score=0.828, total=   1.8s\n",
      "[CV] clsf=SVC(), clsf__C=2.56 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=2.56, score=0.782, total=   1.5s\n",
      "[CV] clsf=SVC(), clsf__C=3.84 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=3.84, score=0.760, total=   2.1s\n",
      "[CV] clsf=SVC(), clsf__C=3.84 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=3.84, score=0.778, total=   1.8s\n",
      "[CV] clsf=SVC(), clsf__C=3.84 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=3.84, score=0.860, total=   1.6s\n",
      "[CV] clsf=SVC(), clsf__C=3.84 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=3.84, score=0.828, total=   1.5s\n",
      "[CV] clsf=SVC(), clsf__C=3.84 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=3.84, score=0.782, total=   1.5s\n",
      "[CV] clsf=SVC(), clsf__C=5.12 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=5.12, score=0.760, total=   1.4s\n",
      "[CV] clsf=SVC(), clsf__C=5.12 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=5.12, score=0.778, total=   1.5s\n",
      "[CV] clsf=SVC(), clsf__C=5.12 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=5.12, score=0.860, total=   1.4s\n",
      "[CV] clsf=SVC(), clsf__C=5.12 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=5.12, score=0.828, total=   1.6s\n",
      "[CV] clsf=SVC(), clsf__C=5.12 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=5.12, score=0.782, total=   1.5s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 205 out of 205 | elapsed:  1.8min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[('tfidfVectorize', TfidfVectorizer()),\n",
       "                                       ('clsf', MultinomialNB())]),\n",
       "             param_grid=[{'clsf': [MultinomialNB()],\n",
       "                          'clsf__alpha': array([ 0.01      ,  0.0143845 ,  0.02069138,  0.02976351,  0.04281332,\n",
       "        0.06158482,  0.08858668,  0.1274275 ,  0.18329807,  0.26366509,\n",
       "        0.37926902,  0.54555948,  0.78475997,  1.12883789,  1.62377674,\n",
       "        2.33572147,  3.35981829,  4.83293024,  6.95192796, 10.        ])},\n",
       "                         {'clsf': [LogisticRegression()],\n",
       "                          'clsf__C': [0.16, 0.32, 0.64, 1.28, 2.56, 3.84, 5.12,\n",
       "                                      5.76, 6.36, 7, 7.5, 7.8, 8, 8.2, 8.5,\n",
       "                                      9]},\n",
       "                         {'clsf': [SVC(C=1.28)],\n",
       "                          'clsf__C': [0.64, 1.28, 2.56, 3.84, 5.12]}],\n",
       "             scoring='accuracy', verbose=3)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsCV_exp3.fit(X_train_processed,Y_train_int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Model = Pipeline(steps=[('tfidfVectorize', TfidfVectorizer()), ('clsf', SVC(C=1.28))])\n",
      "Alpha ={'clsf': SVC(C=1.28), 'clsf__C': 1.28}\n",
      "Accuracy score = 0.8061332784862196\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# GET THE BEST MODEL\n",
    "best_param_exp3 = gsCV_exp3.best_params_\n",
    "best_score_exp3 = gsCV_exp3.best_score_\n",
    "best_model_exp3 = gsCV_exp3.best_estimator_\n",
    "print(f'Best Model = {best_model_exp3}\\nAlpha ={best_param_exp3}\\nAccuracy score = {best_score_exp3}\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The accuracy score is higher than both the Baseline case and Experiment1 where the same TfIdfVectorizer and Classifier are used but no Negation is applied.\n",
    "Interestingly, the average accuracy score obtained with a fixed window negation happened to be higher. it might  be due to an unfortunate splitting and shuffling during the k-fold validation or by the possibility that the reviews are not accurately parsed by the stanza’s  nlp module\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Experiment 4 -  Dense feature vector representation using word embeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All experiments so far have used a bag of words (BOW) approach, where each review was represented as a vector composed by the term frequencies tf (or tf-idf). As a result BOW approaches have the tendency to form a very sparse input matrix to the classifier.\n",
    "The next approach I am going to experiment with is a dense feature vector representation by using word embedding.\n",
    "The underlining concept is that words can be represented as a vector in the embedding space in n-dimensions where similar words (having similar meaning) are represented by similar vector. It's not the scope of this assignment giving a detail explanation on how the word embedding are derived so I will limit myself to explain the \n",
    "In the cell below I train the Doc2Vect model by gensim which is built upon Word2Vect with the difference that Doc2Vect can handle full string of text instead of single words. In fact once trained, it automatically infer the feature vector in n-dimension which represent the whole review. \n",
    "I have opted to train the Doc2Vect model on the training data rather than using a retrained model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Uncomment line below to install gensim module\n",
    "#!python -m pip install -U gensim\n",
    "import gensim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Helper function to tokenize the corpus and reutrn the output compatible with doc2vect\n",
    "def tokenize_corpus_w2v(corpus):\n",
    "    corpus_tokenized = [gensim.models.doc2vec.TaggedDocument([word for word in word_tokenize(sentence)],[i]) for i, sentence in enumerate(corpus)]\n",
    "    return corpus_tokenized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tokenize_corpus(corpus):\n",
    "    return [[word for word in word_tokenize(sentence)] for sentence in corpus]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pre-process the training data\n",
    "Y_train_int = sentiment_to_int(Y_train)\n",
    "X_train_processed = processing_pipe.transform(X_train)\n",
    "train_corpus = tokenize_corpus_w2v(X_train_processed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build  doc2vect model\n",
    "model_Doc2Vec = gensim.models.doc2vec.Doc2Vec(vector_size=300, min_count=3, epochs=60)\n",
    "model_Doc2Vec.build_vocab(train_corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train the model\n",
    "model_Doc2Vec.train(train_corpus, total_examples=model_Doc2Vec.corpus_count, epochs=model_Doc2Vec.epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Helper function which returns a the n-dimensional vector for each string in the corpus\n",
    "def infer_corpus(corpus):\n",
    "    return[model_Doc2Vec.infer_vector(doc) for doc in corpus]                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# In this instance of GridSearchCV I have not include Multinomial Naive Bayes as the feature vector can contain negative\n",
    "# values\n",
    "pipe_exp4 = Pipeline([('clsf',LogisticRegression())])\n",
    "param_grid_exp4 = [{'clsf': [LogisticRegression(max_iter=500,tol=1e-6)],\n",
    "              'clsf__C': [0.16, 0.32, 0.64, 1.28, 2.56, 3.84, 5.12,5.76,6.36, 7,7.5,7.8,8,8.2,8.5,9]},\n",
    "              {'clsf': [SVC()],\n",
    "               'clsf__C': [0.64, 1.28, 2.56, 3.84, 5.12]}]\n",
    "gsCV_exp4 = GridSearchCV(pipe_exp4, param_grid_exp4, scoring='accuracy', cv=5, verbose=3, n_jobs=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 21 candidates, totalling 105 fits\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=0.16 ..\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=0.16, score=0.747, total=   0.0s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=0.16 ..\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=0.16, score=0.719, total=   0.0s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=0.16 ..\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=0.16, score=0.715, total=   0.1s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=0.16 ..\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=0.16, score=0.719, total=   0.1s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=0.16 ..\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=0.16, score=0.750, total=   0.1s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=0.32 ..\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=0.32, score=0.738, total=   0.1s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=0.32 ..\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=0.32, score=0.706, total=   0.1s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=0.32 ..\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=0.32, score=0.706, total=   0.1s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=0.32 ..\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=0.32, score=0.733, total=   0.1s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=0.32 ..\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=0.32, score=0.736, total=   0.1s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=0.64 ..\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=0.64, score=0.733, total=   0.1s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=0.64 ..\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=0.64, score=0.701, total=   0.1s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=0.64 ..\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=0.64, score=0.697, total=   0.1s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=0.64 ..\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=0.64, score=0.729, total=   0.1s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=0.64 ..\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=0.64, score=0.736, total=   0.1s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=1.28 ..\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=1.28, score=0.742, total=   0.1s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=1.28 ..\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=1.28, score=0.692, total=   0.1s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=1.28 ..\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=1.28, score=0.692, total=   0.1s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=1.28 ..\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=1.28, score=0.719, total=   0.1s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=1.28 ..\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=1.28, score=0.741, total=   0.1s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=2.56 ..\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=2.56, score=0.729, total=   0.1s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=2.56 ..\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=2.56, score=0.697, total=   0.1s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=2.56 ..\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=2.56, score=0.688, total=   0.1s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=2.56 ..\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=2.56, score=0.706, total=   0.1s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=2.56 ..\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=2.56, score=0.732, total=   0.1s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=3.84 ..\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=3.84, score=0.729, total=   0.1s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=3.84 ..\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=3.84, score=0.697, total=   0.2s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=3.84 ..\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=3.84, score=0.679, total=   0.1s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=3.84 ..\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=3.84, score=0.710, total=   0.1s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=3.84 ..\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=3.84, score=0.723, total=   0.1s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=5.12 ..\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=5.12, score=0.729, total=   0.1s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=5.12 ..\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=5.12, score=0.706, total=   0.2s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=5.12 ..\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=5.12, score=0.665, total=   0.1s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=5.12 ..\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=5.12, score=0.706, total=   0.2s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=5.12 ..\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=5.12, score=0.723, total=   0.1s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=5.76 ..\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=5.76, score=0.729, total=   0.1s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=5.76 ..\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=5.76, score=0.697, total=   0.2s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=5.76 ..\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=5.76, score=0.670, total=   0.1s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=5.76 ..\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=5.76, score=0.706, total=   0.2s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=5.76 ..\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=5.76, score=0.718, total=   0.1s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=6.36 ..\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=6.36, score=0.729, total=   0.1s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=6.36 ..\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=6.36, score=0.697, total=   0.2s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=6.36 ..\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=6.36, score=0.665, total=   0.1s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=6.36 ..\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=6.36, score=0.701, total=   0.2s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=6.36 ..\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=6.36, score=0.714, total=   0.1s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=7 .....\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=7, score=0.729, total=   0.1s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=7 .....\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=7, score=0.697, total=   0.2s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=7 .....\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=7, score=0.661, total=   0.2s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=7 .....\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=7, score=0.701, total=   0.2s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=7 .....\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=7, score=0.714, total=   0.2s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=7.5 ...\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=7.5, score=0.724, total=   0.3s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=7.5 ...\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=7.5, score=0.697, total=   0.3s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=7.5 ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=7.5, score=0.661, total=   0.3s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=7.5 ...\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=7.5, score=0.697, total=   0.2s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=7.5 ...\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=7.5, score=0.718, total=   0.2s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=7.8 ...\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=7.8, score=0.724, total=   0.1s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=7.8 ...\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=7.8, score=0.701, total=   0.2s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=7.8 ...\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=7.8, score=0.661, total=   0.1s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=7.8 ...\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=7.8, score=0.697, total=   0.2s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=7.8 ...\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=7.8, score=0.718, total=   0.1s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=8 .....\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=8, score=0.724, total=   0.2s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=8 .....\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=8, score=0.701, total=   0.2s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=8 .....\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=8, score=0.665, total=   0.1s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=8 .....\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=8, score=0.697, total=   0.2s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=8 .....\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=8, score=0.718, total=   0.1s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=8.2 ...\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=8.2, score=0.724, total=   0.1s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=8.2 ...\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=8.2, score=0.706, total=   0.2s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=8.2 ...\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=8.2, score=0.665, total=   0.1s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=8.2 ...\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=8.2, score=0.697, total=   0.2s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=8.2 ...\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=8.2, score=0.714, total=   0.1s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=8.5 ...\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=8.5, score=0.724, total=   0.2s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=8.5 ...\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=8.5, score=0.706, total=   0.2s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=8.5 ...\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=8.5, score=0.665, total=   0.1s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=8.5 ...\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=8.5, score=0.697, total=   0.2s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=8.5 ...\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=8.5, score=0.714, total=   0.2s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=9 .....\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=9, score=0.724, total=   0.2s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=9 .....\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=9, score=0.706, total=   0.4s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=9 .....\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=9, score=0.670, total=   0.2s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=9 .....\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=9, score=0.701, total=   0.3s\n",
      "[CV] clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=9 .....\n",
      "[CV]  clsf=LogisticRegression(max_iter=500, tol=1e-06), clsf__C=9, score=0.709, total=   0.3s\n",
      "[CV] clsf=SVC(), clsf__C=0.64 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=0.64, score=0.769, total=   0.4s\n",
      "[CV] clsf=SVC(), clsf__C=0.64 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=0.64, score=0.760, total=   0.4s\n",
      "[CV] clsf=SVC(), clsf__C=0.64 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=0.64, score=0.733, total=   0.4s\n",
      "[CV] clsf=SVC(), clsf__C=0.64 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=0.64, score=0.769, total=   0.4s\n",
      "[CV] clsf=SVC(), clsf__C=0.64 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=0.64, score=0.777, total=   0.4s\n",
      "[CV] clsf=SVC(), clsf__C=1.28 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=1.28, score=0.774, total=   0.4s\n",
      "[CV] clsf=SVC(), clsf__C=1.28 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=1.28, score=0.760, total=   0.4s\n",
      "[CV] clsf=SVC(), clsf__C=1.28 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=1.28, score=0.747, total=   0.4s\n",
      "[CV] clsf=SVC(), clsf__C=1.28 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=1.28, score=0.792, total=   0.4s\n",
      "[CV] clsf=SVC(), clsf__C=1.28 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=1.28, score=0.782, total=   0.4s\n",
      "[CV] clsf=SVC(), clsf__C=2.56 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=2.56, score=0.765, total=   0.4s\n",
      "[CV] clsf=SVC(), clsf__C=2.56 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=2.56, score=0.751, total=   0.4s\n",
      "[CV] clsf=SVC(), clsf__C=2.56 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=2.56, score=0.742, total=   0.4s\n",
      "[CV] clsf=SVC(), clsf__C=2.56 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=2.56, score=0.751, total=   0.4s\n",
      "[CV] clsf=SVC(), clsf__C=2.56 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=2.56, score=0.773, total=   0.4s\n",
      "[CV] clsf=SVC(), clsf__C=3.84 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=3.84, score=0.769, total=   0.4s\n",
      "[CV] clsf=SVC(), clsf__C=3.84 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=3.84, score=0.760, total=   0.4s\n",
      "[CV] clsf=SVC(), clsf__C=3.84 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=3.84, score=0.719, total=   0.4s\n",
      "[CV] clsf=SVC(), clsf__C=3.84 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=3.84, score=0.733, total=   0.4s\n",
      "[CV] clsf=SVC(), clsf__C=3.84 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=3.84, score=0.777, total=   0.4s\n",
      "[CV] clsf=SVC(), clsf__C=5.12 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=5.12, score=0.778, total=   0.4s\n",
      "[CV] clsf=SVC(), clsf__C=5.12 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=5.12, score=0.765, total=   0.5s\n",
      "[CV] clsf=SVC(), clsf__C=5.12 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=5.12, score=0.733, total=   0.4s\n",
      "[CV] clsf=SVC(), clsf__C=5.12 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=5.12, score=0.729, total=   0.4s\n",
      "[CV] clsf=SVC(), clsf__C=5.12 ........................................\n",
      "[CV] ............ clsf=SVC(), clsf__C=5.12, score=0.777, total=   0.4s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 105 out of 105 | elapsed:   21.8s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=Pipeline(steps=[('clsf', LogisticRegression())]),\n",
       "             n_jobs=1,\n",
       "             param_grid=[{'clsf': [LogisticRegression(max_iter=500, tol=1e-06)],\n",
       "                          'clsf__C': [0.16, 0.32, 0.64, 1.28, 2.56, 3.84, 5.12,\n",
       "                                      5.76, 6.36, 7, 7.5, 7.8, 8, 8.2, 8.5,\n",
       "                                      9]},\n",
       "                         {'clsf': [SVC(C=1.28)],\n",
       "                          'clsf__C': [0.64, 1.28, 2.56, 3.84, 5.12]}],\n",
       "             scoring='accuracy', verbose=3)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# tokenize each review\n",
    "X_train_tokenized = tokenize_corpus(X_train_processed)\n",
    "# Infer the dense feature vector for each review\n",
    "X_train_vect = infer_corpus(X_train_tokenized)\n",
    "# Run GridSeachCV\n",
    "gsCV_exp4.fit(X_train_vect,Y_train_int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Model = Pipeline(steps=[('clsf', SVC(C=1.28))])\n",
      "Alpha ={'clsf': SVC(C=1.28), 'clsf__C': 1.28}\n",
      "Accuracy score = 0.7708432743726861\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# GET THE BEST MODEL\n",
    "best_param_exp4 = gsCV_exp4.best_params_\n",
    "best_score_exp4 = gsCV_exp4.best_score_\n",
    "best_model_exp4 = gsCV_exp4.best_estimator_\n",
    "print(f'Best Model = {best_model_exp4}\\nAlpha ={best_param_exp4}\\nAccuracy score = {best_score_exp4}\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using a dense rapresentation for the feature vector of each review along with SVC classifier I have obtained an avarage acuruacy over the validation data of 75.81% which is the lowest so far beteween all experiments.\n",
    "The reason for this relatively low accuracy score may be due to the fact that the Doc2vect model is locally trained on a a small corpus ( training data). Using pre-trained model on larger corpus may increase the accuracy. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusions\n",
    "\n",
    "To establish, with a certain degree of confidence, which of the 5 approaches (1 baseline + 4 experiments) is the best performing, I am now going to run a repeated k-fold validation using the best performing model for each experiment. The results are plotted in the cell below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Perform 10 5-fold cross validation on each best performing model from each experiment.\n",
    "from sklearn.model_selection import RepeatedKFold\n",
    "rkf = RepeatedKFold(n_splits=5, n_repeats=10, random_state=1)\n",
    "score_baseline = []\n",
    "scores_exp1 = []\n",
    "scores_exp2 = []\n",
    "scores_exp3 = []\n",
    "scores_exp4 = []\n",
    "Y_train_int = sentiment_to_int(Y_train)\n",
    "Y_train_int_exp3 = sentiment_to_int(Y_train_rn)\n",
    "\n",
    "X_train_processed_exp1 = processing_pipe.transform(X_train)\n",
    "X_train_processed_exp2 = processing_pipe_neg.transform(X_train)\n",
    "X_train_processed_exp3 = processing_pipe.transform(X_train_rn)\n",
    "X_train_processed_exp4 = X_train_vect\n",
    "\n",
    "for train_index, valid_index in rkf.split(X_train_processed_exp2):     \n",
    "    # apply the experiment's specif preocessing pipelines\n",
    "    X_train_rkf_exp1, X_valid_index_rkf_exp1 = np.array(X_train_processed_exp1)[train_index], np.array(X_train_processed_exp1)[valid_index]\n",
    "    X_train_rkf_exp2, X_valid_index_rkf_exp2 = np.array(X_train_processed_exp2)[train_index], np.array(X_train_processed_exp2)[valid_index]\n",
    "    X_train_rkf_exp3, X_valid_index_rkf_exp3 = np.array(X_train_processed_exp3)[train_index], np.array(X_train_processed_exp3)[valid_index]\n",
    "    X_train_rkf_exp4, X_valid_index_rkf_exp4 = np.array(X_train_processed_exp4)[train_index], np.array(X_train_processed_exp4)[valid_index]\n",
    "    \n",
    "    y_train_rkf_exp1, y_valid_index_rkf_exp1 = np.array(Y_train_int)[train_index], np.array(Y_train_int)[valid_index]\n",
    "    y_train_rkf_exp2, y_valid_index_rkf_exp2 = np.array(Y_train_int)[train_index], np.array(Y_train_int)[valid_index]\n",
    "    y_train_rkf_exp3, y_valid_index_rkf_exp3 = np.array(Y_train_int_exp3)[train_index], np.array(Y_train_int_exp3)[valid_index]\n",
    "    y_train_rkf_exp4, y_valid_index_rkf_exp4 = np.array(Y_train_int)[train_index], np.array(Y_train_int)[valid_index]\n",
    "    \n",
    "    # fit each model\n",
    "    best_model_baseline.fit(X_train_rkf_exp1,y_train_rkf_exp1)\n",
    "    best_model_exp1.fit(X_train_rkf_exp1,y_train_rkf_exp1)\n",
    "    best_model_exp2.fit(X_train_rkf_exp2,y_train_rkf_exp2)\n",
    "    best_model_exp3.fit(X_train_rkf_exp3,y_train_rkf_exp3)\n",
    "    best_model_exp4.fit(X_train_rkf_exp4,y_train_rkf_exp4)\n",
    "    \n",
    "    # score the models on the validation data \n",
    "    score_baseline.append(best_model_baseline.score(X_valid_index_rkf_exp1,y_valid_index_rkf_exp1))\n",
    "    scores_exp1.append(best_model_exp1.score(X_valid_index_rkf_exp1,y_valid_index_rkf_exp1))\n",
    "    scores_exp2.append(best_model_exp2.score(X_valid_index_rkf_exp2,y_valid_index_rkf_exp2))\n",
    "    scores_exp3.append(best_model_exp3.score(X_valid_index_rkf_exp3,y_valid_index_rkf_exp3))\n",
    "    scores_exp4.append(best_model_exp4.score(X_valid_index_rkf_exp4,y_valid_index_rkf_exp4))\n",
    "\n",
    "scores_baseline = np.array(score_baseline)\n",
    "scores_exp1 = np.array(scores_exp1)\n",
    "scores_exp2 = np.array(scores_exp2)\n",
    "scores_exp3 = np.array(scores_exp3)\n",
    "scores_exp4 = np.array(scores_exp4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Create a unique dataframe where to store all results from each experiment\n",
    "df_baseline = pd.DataFrame(np.repeat('Baseline',len(score_baseline)), columns=[\"Experiment\"])\n",
    "df_baseline['Accuracy'] = np.array(score_baseline) \n",
    "df_exp1 = pd.DataFrame(np.repeat('Experiment1',len(scores_exp1)), columns=[\"Experiment\"])\n",
    "df_exp1['Accuracy'] = np.array(scores_exp1) \n",
    "df_exp2 = pd.DataFrame(np.repeat('Experiment2',len(scores_exp2)), columns=[\"Experiment\"])\n",
    "df_exp2['Accuracy'] = np.array(scores_exp2) \n",
    "df_exp3 = pd.DataFrame(np.repeat('Experiment3',len(scores_exp3)), columns=[\"Experiment\"])\n",
    "df_exp3['Accuracy'] = np.array(scores_exp3) \n",
    "df_exp4 = pd.DataFrame(np.repeat('Experiment4',len(scores_exp4)), columns=[\"Experiment\"])\n",
    "df_exp4['Accuracy'] = np.array(scores_exp4) \n",
    "df_exp_results = df_baseline\n",
    "df_exp_results = df_baseline.append(df_exp1)\n",
    "df_exp_results = df_exp_results.append(df_exp2)\n",
    "df_exp_results = df_exp_results.append(df_exp3)\n",
    "df_exp_results = df_exp_results.append(df_exp4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the distributions for each experiment in a boxplot form\n",
    "plt.figure(figsize=(16, 10))\n",
    "ax= sns.boxplot(x=\"Experiment\", y=\"Accuracy\", data=df_exp_results)\n",
    "ax = sns.swarmplot(x=\"Experiment\", y=\"Accuracy\", data=df_exp_results, color=\".25\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Baseline:** CountVectorizer (Word count) Multinomial Naive Bayes\n",
    "- **Experiment1:** TfIdfCountVectorizer (tf binary) SVC\n",
    "- **Experiment2:** Fixed window negation handling, TfIdfCountVectorizerf (tf binary) SVC\n",
    "- **Experiment3:** Dependency parser negation handling, TfIdfCountVectorizer (tf binary) SVC\n",
    "- **Experiment4:** Dense feature vector representation using doc2vect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>Experiment</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Std</th>\n",
       "      <th>Median</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Baseline</td>\n",
       "      <td>0.768384</td>\n",
       "      <td>0.025329</td>\n",
       "      <td>0.769231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Experiment1</td>\n",
       "      <td>0.806781</td>\n",
       "      <td>0.028382</td>\n",
       "      <td>0.809091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Experiment2</td>\n",
       "      <td>0.815934</td>\n",
       "      <td>0.026385</td>\n",
       "      <td>0.816742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Experiment3</td>\n",
       "      <td>0.802623</td>\n",
       "      <td>0.022409</td>\n",
       "      <td>0.800905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Experiment4</td>\n",
       "      <td>0.768109</td>\n",
       "      <td>0.024790</td>\n",
       "      <td>0.769231</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Experiment  Accuracy       Std    Median\n",
       "0     Baseline  0.768384  0.025329  0.769231\n",
       "1  Experiment1  0.806781  0.028382  0.809091\n",
       "2  Experiment2  0.815934  0.026385  0.816742\n",
       "3  Experiment3  0.802623  0.022409  0.800905\n",
       "4  Experiment4  0.768109  0.024790  0.769231"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Repeated (10 times) 5-fold cross validation average accuracy of each experiments\n",
    "df_exp_stats = df_exp_results.groupby('Experiment', as_index=False)['Accuracy'].mean()\n",
    "df_exp_stats['Std'] = df_exp_results.groupby('Experiment', as_index=False)['Accuracy'].std()['Accuracy']\n",
    "df_exp_stats['Median'] = df_exp_results.groupby('Experiment', as_index=False)['Accuracy'].median()['Accuracy']\n",
    "df_exp_stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The box plot above clearly shows the performances of each approach (experiment).\n",
    "It is clear that as expected negation handling is a good strategy to improve the classifier performances. In fact Experiment2 ( which employes fix window nagation handling) has a slight advantage over Experiment1 which uses the same exact feature extraction strategy and calssifier but does not use nagation handling.  \n",
    "Another interesting result is that classification using a dense feature vector by mean of word embedding although has outperformed the baseline case, it has scored the lowest amongst the 4 attempts to beat the initial classification performances. This might be due to the fact the word2Doc model was trained only on the the given train data. Although not guaranteed, using pre-trained models on bigger corpus might result in better final accuracy.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test Best Model on the test Data\n",
    "\n",
    "Now that we have an overview on how each model performs, I can test the best performing model (**Experiment2 model**) on the test data and compare it to the baseline case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy improved Model = 0.8309352517985612. \n",
      "Accuracy baseline Model = 0.8093525179856115. \n",
      "F1 score  improved Model = 0.8290909090909091\n",
      "F1 score baseline Model = 0.8100358422939068\n",
      "[[117  22]\n",
      " [ 25 114]]\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x432 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test =  True\n",
    "if test:\n",
    "    Y_test_int = sentiment_to_int(Y_test)\n",
    "    X_test_processed_exp2 = processing_pipe_neg.transform(X_test)\n",
    "    X_test_processed_baseline = processing_pipe.transform(X_test)\n",
    "    Y_test_pred_exp2 = best_model_exp2.predict(X_test_processed_exp2)\n",
    "    Y_test_pred_baseline = best_model_baseline.predict(X_test_processed_baseline)\n",
    "    cnf_matrix_exp2 = confusion_matrix(Y_test_int, Y_test_pred_exp2)\n",
    "    cnf_matrix_baseline = confusion_matrix(Y_test_int, Y_test_pred_baseline)\n",
    "    avg_acc_exp2 = accuracy_score(Y_test_int, Y_test_pred_exp2)\n",
    "    avg_acc_baseline = accuracy_score(Y_test_int, Y_test_pred_baseline)\n",
    "    f1_exp2 = f1_score(Y_test_int, Y_test_pred_exp2)\n",
    "    f1_baseline = f1_score(Y_test_int, Y_test_pred_baseline)\n",
    "    print(f\"Accuracy improved Model = {avg_acc_exp2}. \")\n",
    "    print(f\"Accuracy baseline Model = {avg_acc_baseline}. \")\n",
    "    \n",
    "    print(f\"F1 score  improved Model = {f1_exp2}\")\n",
    "    print(f\"F1 score baseline Model = {f1_baseline}\")\n",
    "    print(cnf_matrix_exp2)\n",
    "    \n",
    "    fig, (ax1,ax2) = plt.subplots(1, 2, figsize=(16,6))\n",
    "    sns.heatmap(cnf_matrix_baseline,ax=ax1, annot=True, fmt=\"d\",xticklabels=[\"Pos\",\"Neg\"],yticklabels=[\"Pos\",\"Neg\"],cmap=\"YlGnBu\");\n",
    "    sns.heatmap(cnf_matrix_exp2,ax=ax2, annot=True, fmt=\"d\",xticklabels=[\"Pos\",\"Neg\"],yticklabels=[\"Pos\",\"Neg\"],cmap=\"YlGnBu\")\n",
    "    ax1.set_title('Confusion Matrix Baseline classifier');\n",
    "    ax1.set_xlabel('True sentiment')\n",
    "    ax1.set_ylabel('Predicted sentiment')\n",
    "    ax2.set_title('Confusion Matrix Improved classifier');\n",
    "    ax2.set_xlabel('True sentiment')\n",
    "    ax2.set_ylabel('Predicted sentiment')\n",
    "   \n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The two confusion matrices above compare the final performances of the improved model (Experiment2) with respect the baseline case. We can see that true positive have increased from 112 to 117 with the equivalent reduction fo false positive (27 to 22). A slight improvement was also recorded amongst the classification of negative reviews, where true negative went from 113 to 114 and false negative went from 26 to 25.\n",
    "Both average accuracy and F1 score have increased."
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
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 },
 "nbformat": 4,
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}