Solutions_Q1-Q3.ipynb

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    "# NLP Assignment 2 (40% of grade): Vector Space Semantics for Similarity between Eastenders Characters\n",
    "\n",
    "In this assignment, you will be creating a vector representation of a document containing lines spoken by a character in the Eastenders script data (i.e. from the file `training.csv`), then improving that representation such that each character vector is maximially distinguished from the other character documents. This distinction is measured by how well a simple information retrieval classification method can select documents from validation and test data as belonging to the correct class of document (i.e. deciding which character spoke the lines by measuring the similarity of those character document vectors to those built in training).\n",
    "\n",
    "As the lines are not evenly distributed in terms of frequency, this coursework stipulates you can only use a maximum of the first **400 lines** of each character in the training data `training.csv` to create the training documents, and a maximum of the first **40 lines** in the test data (from `test.csv`). This makes it more challenging, as number of lines spoken by a character can't be used directly or otherwise as a feature.\n",
    "\n",
    "A simple vector representation for each character document is done for you to start with in this code, as is the pipeline of similarity-based information retrieval based evaluation. You need to improve the character vector representations by pre-processing, feature extraction and transformation techniques, as per Questions 1-6 below, which you need to complete as instructed.\n",
    "\n",
    "**Refer to the material in units 8-9 for conceptual background.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: nltk in c:\\users\\pranj\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (3.7)\n",
      "Requirement already satisfied: joblib in c:\\users\\pranj\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from nltk) (1.2.0)\n",
      "Requirement already satisfied: click in c:\\users\\pranj\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from nltk) (8.1.3)\n",
      "Requirement already satisfied: tqdm in c:\\users\\pranj\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from nltk) (4.64.1)\n",
      "Requirement already satisfied: regex>=2021.8.3 in c:\\users\\pranj\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from nltk) (2022.9.13)\n",
      "Requirement already satisfied: colorama in c:\\users\\pranj\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from click->nltk) (0.4.5)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "[notice] A new release of pip available: 22.2.2 -> 22.3.1\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    }
   ],
   "source": [
    "!pip install nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: vaderSentiment in c:\\users\\pranj\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (3.3.2)\n",
      "Requirement already satisfied: requests in c:\\users\\pranj\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from vaderSentiment) (2.28.1)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\users\\pranj\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from requests->vaderSentiment) (1.26.12)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\pranj\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from requests->vaderSentiment) (3.4)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\pranj\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from requests->vaderSentiment) (2022.9.24)\n",
      "Requirement already satisfied: charset-normalizer<3,>=2 in c:\\users\\pranj\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from requests->vaderSentiment) (2.1.1)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "[notice] A new release of pip available: 22.2.2 -> 22.3.1\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    }
   ],
   "source": [
    "!pip install vaderSentiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
      "[nltk_data]     C:\\Users\\pranj\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package averaged_perceptron_tagger is already up-to-\n",
      "[nltk_data]       date!\n",
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\pranj\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\pranj\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "[nltk_data] Downloading package wordnet to\n",
      "[nltk_data]     C:\\Users\\pranj\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n",
      "[nltk_data] Downloading package omw-1.4 to\n",
      "[nltk_data]     C:\\Users\\pranj\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package omw-1.4 is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "#Required Imports\n",
    "\n",
    "import nltk\n",
    "import string\n",
    "import re\n",
    "import csv    # csv reader\n",
    "import numpy as np\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from numpy.linalg import norm\n",
    "import pandas as pd\n",
    "from collections import Counter, OrderedDict\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.feature_extraction import DictVectorizer\n",
    "%matplotlib inline\n",
    "pd.options.display.max_colwidth=500\n",
    "from nltk import pos_tag\n",
    "from nltk import RegexpParser\n",
    "nltk.download('averaged_perceptron_tagger')\n",
    "nltk.download('stopwords')\n",
    "nltk.download('punkt')\n",
    "nltk.download('wordnet')\n",
    "nltk.download('omw-1.4')\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import classification_report\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.stem import SnowballStemmer\n",
    "from nltk.util import ngrams\n",
    "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "metadata": {},
   "outputs": [
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       "      <td>1350</td>\n",
       "      <td>1</td>\n",
       "      <td>DESERTED CAR PARK EXT NIGHT</td>\n",
       "      <td>SHIRLEY</td>\n",
       "      <td>Look at ya, not a mark on ya. And you think you're an unlucky man.</td>\n",
       "      <td>FEMALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1350</td>\n",
       "      <td>1</td>\n",
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       "      <td>OTHER</td>\n",
       "      <td>Shirl...</td>\n",
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       "      <td>1350</td>\n",
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       "      <td>R&amp;R INT NIGHT</td>\n",
       "      <td>JACK</td>\n",
       "      <td>Oi. Where have you been? Huh? What were the texts about?</td>\n",
       "      <td>MALE</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1350</td>\n",
       "      <td>2</td>\n",
       "      <td>R&amp;R INT NIGHT</td>\n",
       "      <td>RONNIE</td>\n",
       "      <td>Nothing. Nothing. I'll be with you in two minutes yeah?</td>\n",
       "      <td>FEMALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1350</td>\n",
       "      <td>2</td>\n",
       "      <td>R&amp;R INT NIGHT</td>\n",
       "      <td>JACK</td>\n",
       "      <td>Well I've got mates here I wanted to have a chat with them, instead I've been serving behind the bar.</td>\n",
       "      <td>MALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15314</th>\n",
       "      <td>1399</td>\n",
       "      <td>55</td>\n",
       "      <td>SQUARE EXT DAY LIGHT</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>Dad? Okay ... alright, just one drink alright. But that's all. It doesn't mean anything. It's just a drink.</td>\n",
       "      <td>MALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15315</th>\n",
       "      <td>1399</td>\n",
       "      <td>55</td>\n",
       "      <td>SQUARE EXT DAY LIGHT</td>\n",
       "      <td>MAX</td>\n",
       "      <td>Thanks Bradley. Thanks mate... It means the world to me...</td>\n",
       "      <td>MALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15316</th>\n",
       "      <td>1399</td>\n",
       "      <td>55</td>\n",
       "      <td>SQUARE EXT DAY LIGHT</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>You alright...</td>\n",
       "      <td>MALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15317</th>\n",
       "      <td>1399</td>\n",
       "      <td>55</td>\n",
       "      <td>SQUARE EXT DAY LIGHT</td>\n",
       "      <td>MAX</td>\n",
       "      <td>Yeah, yeah, yeah. I'm fine.</td>\n",
       "      <td>MALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15318</th>\n",
       "      <td>1399</td>\n",
       "      <td>55</td>\n",
       "      <td>SQUARE EXT DAY LIGHT</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>Well, let me know.</td>\n",
       "      <td>MALE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15319 rows × 6 columns</p>\n",
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      ],
      "text/plain": [
       "       Episode  Scene                   Scene_info Character_name  \\\n",
       "0         1350      1  DESERTED CAR PARK EXT NIGHT        SHIRLEY   \n",
       "1         1350      1  DESERTED CAR PARK EXT NIGHT          OTHER   \n",
       "2         1350      2                R&R INT NIGHT           JACK   \n",
       "3         1350      2                R&R INT NIGHT         RONNIE   \n",
       "4         1350      2                R&R INT NIGHT           JACK   \n",
       "...        ...    ...                          ...            ...   \n",
       "15314     1399     55         SQUARE EXT DAY LIGHT          OTHER   \n",
       "15315     1399     55         SQUARE EXT DAY LIGHT            MAX   \n",
       "15316     1399     55         SQUARE EXT DAY LIGHT          OTHER   \n",
       "15317     1399     55         SQUARE EXT DAY LIGHT            MAX   \n",
       "15318     1399     55         SQUARE EXT DAY LIGHT          OTHER   \n",
       "\n",
       "                                                                                                              Line  \\\n",
       "0                                               Look at ya, not a mark on ya. And you think you're an unlucky man.   \n",
       "1                                                                                                         Shirl...   \n",
       "2                                                         Oi. Where have you been? Huh? What were the texts about?   \n",
       "3                                                          Nothing. Nothing. I'll be with you in two minutes yeah?   \n",
       "4            Well I've got mates here I wanted to have a chat with them, instead I've been serving behind the bar.   \n",
       "...                                                                                                            ...   \n",
       "15314  Dad? Okay ... alright, just one drink alright. But that's all. It doesn't mean anything. It's just a drink.   \n",
       "15315                                                   Thanks Bradley. Thanks mate... It means the world to me...   \n",
       "15316                                                                                               You alright...   \n",
       "15317                                                                                  Yeah, yeah, yeah. I'm fine.   \n",
       "15318                                                                                           Well, let me know.   \n",
       "\n",
       "       Gender  \n",
       "0      FEMALE  \n",
       "1        MALE  \n",
       "2        MALE  \n",
       "3      FEMALE  \n",
       "4        MALE  \n",
       "...       ...  \n",
       "15314    MALE  \n",
       "15315    MALE  \n",
       "15316    MALE  \n",
       "15317    MALE  \n",
       "15318    MALE  \n",
       "\n",
       "[15319 rows x 6 columns]"
      ]
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       "      <th>Gender</th>\n",
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       "      <th>0</th>\n",
       "      <td>1350</td>\n",
       "      <td>7</td>\n",
       "      <td>DESERTED CAR PARK EXT NIGHT</td>\n",
       "      <td>SHIRLEY</td>\n",
       "      <td>You'll be alright Kevin. They're gonna cut you out. It's going to be alright. Come on, just talk to me. Just, just talk to me. Call me names- let me know you're still here with me.</td>\n",
       "      <td>FEMALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1350</td>\n",
       "      <td>7</td>\n",
       "      <td>DESERTED CAR PARK EXT NIGHT</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>...I've got pins and needles in my fingers.</td>\n",
       "      <td>MALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1350</td>\n",
       "      <td>7</td>\n",
       "      <td>DESERTED CAR PARK EXT NIGHT</td>\n",
       "      <td>SHIRLEY</td>\n",
       "      <td>That's the cold.</td>\n",
       "      <td>FEMALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1350</td>\n",
       "      <td>7</td>\n",
       "      <td>DESERTED CAR PARK EXT NIGHT</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>Yeah. It's all shutting down.</td>\n",
       "      <td>MALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1350</td>\n",
       "      <td>7</td>\n",
       "      <td>DESERTED CAR PARK EXT NIGHT</td>\n",
       "      <td>SHIRLEY</td>\n",
       "      <td>It's just the cold.</td>\n",
       "      <td>FEMALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1762</th>\n",
       "      <td>1398</td>\n",
       "      <td>32</td>\n",
       "      <td>GEORGE STREET EXT NIGHT</td>\n",
       "      <td>JANE</td>\n",
       "      <td>I know. Now Ian come on. Everything's going to be alright. It is. It's going to be alright.</td>\n",
       "      <td>FEMALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1763</th>\n",
       "      <td>1399</td>\n",
       "      <td>27</td>\n",
       "      <td>[NAME] &amp; [NAME]'S BEDROOM INT NIGHT</td>\n",
       "      <td>TANYA</td>\n",
       "      <td>Night night baby. I love you.</td>\n",
       "      <td>FEMALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1764</th>\n",
       "      <td>1399</td>\n",
       "      <td>27</td>\n",
       "      <td>[NAME] &amp; [NAME]'S BEDROOM INT NIGHT</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>Love you too.</td>\n",
       "      <td>FEMALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1765</th>\n",
       "      <td>1399</td>\n",
       "      <td>27</td>\n",
       "      <td>[NAME] &amp; [NAME]'S BEDROOM INT NIGHT</td>\n",
       "      <td>TANYA</td>\n",
       "      <td>Let's get that under your ankle shall we. Careful, alright? Night night darling. Love you.</td>\n",
       "      <td>FEMALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1766</th>\n",
       "      <td>1399</td>\n",
       "      <td>27</td>\n",
       "      <td>[NAME] &amp; [NAME]'S BEDROOM INT NIGHT</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>Love you Mum.</td>\n",
       "      <td>FEMALE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1767 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Episode  Scene                           Scene_info Character_name  \\\n",
       "0        1350      7          DESERTED CAR PARK EXT NIGHT        SHIRLEY   \n",
       "1        1350      7          DESERTED CAR PARK EXT NIGHT          OTHER   \n",
       "2        1350      7          DESERTED CAR PARK EXT NIGHT        SHIRLEY   \n",
       "3        1350      7          DESERTED CAR PARK EXT NIGHT          OTHER   \n",
       "4        1350      7          DESERTED CAR PARK EXT NIGHT        SHIRLEY   \n",
       "...       ...    ...                                  ...            ...   \n",
       "1762     1398     32              GEORGE STREET EXT NIGHT           JANE   \n",
       "1763     1399     27  [NAME] & [NAME]'S BEDROOM INT NIGHT          TANYA   \n",
       "1764     1399     27  [NAME] & [NAME]'S BEDROOM INT NIGHT          OTHER   \n",
       "1765     1399     27  [NAME] & [NAME]'S BEDROOM INT NIGHT          TANYA   \n",
       "1766     1399     27  [NAME] & [NAME]'S BEDROOM INT NIGHT          OTHER   \n",
       "\n",
       "                                                                                                                                                                                      Line  \\\n",
       "0     You'll be alright Kevin. They're gonna cut you out. It's going to be alright. Come on, just talk to me. Just, just talk to me. Call me names- let me know you're still here with me.   \n",
       "1                                                                                                                                              ...I've got pins and needles in my fingers.   \n",
       "2                                                                                                                                                                         That's the cold.   \n",
       "3                                                                                                                                                            Yeah. It's all shutting down.   \n",
       "4                                                                                                                                                                      It's just the cold.   \n",
       "...                                                                                                                                                                                    ...   \n",
       "1762                                                                                           I know. Now Ian come on. Everything's going to be alright. It is. It's going to be alright.   \n",
       "1763                                                                                                                                                         Night night baby. I love you.   \n",
       "1764                                                                                                                                                                         Love you too.   \n",
       "1765                                                                                            Let's get that under your ankle shall we. Careful, alright? Night night darling. Love you.   \n",
       "1766                                                                                                                                                                         Love you Mum.   \n",
       "\n",
       "      Gender  \n",
       "0     FEMALE  \n",
       "1       MALE  \n",
       "2     FEMALE  \n",
       "3       MALE  \n",
       "4     FEMALE  \n",
       "...      ...  \n",
       "1762  FEMALE  \n",
       "1763  FEMALE  \n",
       "1764  FEMALE  \n",
       "1765  FEMALE  \n",
       "1766  FEMALE  \n",
       "\n",
       "[1767 rows x 6 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load in training data and display in pandas dataframe\n",
    "train_path='training.csv'\n",
    "all_train_data = pd.read_csv(train_path,  delimiter=\"\\t\", skip_blank_lines = True)\n",
    "test_path ='test.csv'\n",
    "test_data = pd.read_csv(test_path,  delimiter=\"\\t\", skip_blank_lines = True)\n",
    "\n",
    "# Inspect\n",
    "display(all_train_data)\n",
    "display(test_data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1394\n",
      "1254 140\n"
     ]
    }
   ],
   "source": [
    "# Split into training and test data for heldout validation with random samples of 9:1 train/heldout split\n",
    "from random import shuffle, seed\n",
    "\n",
    "seed(0) # set a seed for reproducibility so same split is used each time\n",
    "\n",
    "epsiode_scene_column = all_train_data.Episode.astype(str) + \"-\" + all_train_data.Scene.astype(str)\n",
    "#print(epsiode_scene_column)\n",
    "\n",
    "all_train_data['episode_scene'] = epsiode_scene_column\n",
    "episode_scenes = sorted(list(set([x for x in epsiode_scene_column.values]))) # need to sort to ensure same initial order\n",
    "\n",
    "shuffle(episode_scenes)\n",
    "\n",
    "print(len(episode_scenes))\n",
    "episode_split = int(0.9*len(episode_scenes))\n",
    "training_ep_scenes = episode_scenes[:episode_split]\n",
    "test_ep_scenes = episode_scenes[episode_split:]\n",
    "print(len(training_ep_scenes), len(test_ep_scenes))\n",
    "\n",
    "def train_or_heldout_eps(val):\n",
    "    if val in training_ep_scenes:\n",
    "        return \"training\"\n",
    "    return \"heldout\"\n",
    "\n",
    "all_train_data['train_heldout'] = all_train_data['episode_scene'].apply(train_or_heldout_eps)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Raw Data:  (15319, 8)\n",
      "Train set:  (13638, 8)\n",
      "Validation set:  (1681, 8)\n"
     ]
    }
   ],
   "source": [
    "print('Raw Data: ',np.shape(all_train_data))\n",
    "train_data = all_train_data[all_train_data['train_heldout']=='training']\n",
    "val_data = all_train_data[all_train_data['train_heldout']=='heldout']\n",
    "print('Train set: ',np.shape(train_data))\n",
    "print('Validation set: ',np.shape(val_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {},
   "outputs": [],
   "source": [
    "if False:\n",
    "    # A simpler alternative to the above, but where a high split means some characters\n",
    "    # may be left out\n",
    "    print('Raw Data: ',np.shape(all_train_data))\n",
    "    idx = int(0.9 * np.shape(all_train_data)[0])\n",
    "    train_data = all_train_data[:idx]\n",
    "    val_data = all_train_data[idx:]\n",
    "    print('Train set: ',np.shape(train_data))\n",
    "    print('Validation set: ',np.shape(val_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create one document per character\n",
    "def create_character_document_from_dataframe(df, max_line_count):\n",
    "    \"\"\"Returns a dict with the name of the character as key,\n",
    "    their lines joined together as a single string, with end of line _EOL_\n",
    "    markers between them.\n",
    "    \n",
    "    ::max_line_count:: the maximum number of lines to be added per character\n",
    "    \"\"\"\n",
    "    character_docs = {}\n",
    "    character_line_count = {}\n",
    "    for line, name, gender in zip(df.Line, df.Character_name, df.Gender):\n",
    "        if not name in character_docs.keys():\n",
    "            character_docs[name] = \"\"\n",
    "            character_line_count[name] = 0\n",
    "        if character_line_count[name]==max_line_count:\n",
    "            continue\n",
    "        character_docs[name] += str(line)   + \" _EOL_ \"  # adding an end-of-line token\n",
    "        character_line_count[name]+=1\n",
    "    print(\"lines per character\", character_line_count)\n",
    "    return character_docs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lines per character {'SHIRLEY': 360, 'OTHER': 360, 'JACK': 360, 'RONNIE': 360, 'TANYA': 360, 'SEAN': 360, 'ROXY': 360, 'HEATHER': 360, 'MAX': 360, 'IAN': 360, 'JANE': 360, 'STACEY': 360, 'PHIL': 360, 'MINTY': 360, 'CHRISTIAN': 342, 'CLARE': 352}\n",
      "Num. Characters:  16 \n",
      "\n",
      "SHIRLEY Number of Words:  3848\n",
      "OTHER Number of Words:  3244\n",
      "JACK Number of Words:  4435\n",
      "RONNIE Number of Words:  3442\n",
      "TANYA Number of Words:  3786\n",
      "SEAN Number of Words:  3637\n",
      "ROXY Number of Words:  3838\n",
      "HEATHER Number of Words:  4098\n",
      "MAX Number of Words:  4363\n",
      "IAN Number of Words:  4332\n",
      "JANE Number of Words:  3648\n",
      "STACEY Number of Words:  3913\n",
      "PHIL Number of Words:  3635\n",
      "MINTY Number of Words:  4005\n",
      "CHRISTIAN Number of Words:  3738\n",
      "CLARE Number of Words:  4344\n",
      "total words 62306\n"
     ]
    }
   ],
   "source": [
    "# print out the number of words each character has in the training set\n",
    "# only use the first 360 lines of each character\n",
    "\n",
    "train_character_docs = create_character_document_from_dataframe(train_data, max_line_count=360)\n",
    "print('Num. Characters: ',len(train_character_docs.keys()),\"\\n\")\n",
    "total_words = 0\n",
    "for name in train_character_docs.keys():\n",
    "    print(name, 'Number of Words: ',len(train_character_docs[name].split()))\n",
    "    total_words += len(train_character_docs[name].split())\n",
    "print(\"total words\", total_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalizing_lamatizing(tokens):\n",
    "    \"\"\"\n",
    "    This method performs multiple data cleaning steps, such as removing stopwords, \n",
    "    removing punctuation, and performs stemming(Stemming means reducing inflection in words to their root forms).\n",
    "    : return: a list of tokens after cleaning.\n",
    "    \"\"\"\n",
    "    # removing stopwords\n",
    "    tokens_without_sw = [w for w in tokens if not w.lower() in stopwords.words('english')]\n",
    "    tokes_without_pucn = [w.lower() for w in tokens_without_sw if w.isalpha()]\n",
    "    sb = SnowballStemmer('english')\n",
    "    tokens_after_stem = [sb.stem(w) for w in tokes_without_pucn]\n",
    "    return tokens_after_stem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_ngrams(tokens, n):\n",
    "    \"\"\"\n",
    "    This method can convert tokens to given add ngrams.\n",
    "    : param tokens: Input list of words/tokens\n",
    "    : param n: number of ngram need to use.\n",
    "    : return: return a list of tokens based on ngram size.\n",
    "    \"\"\"\n",
    "    return list(ngrams(tokens, n))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "metadata": {},
   "outputs": [],
   "source": [
    "def using_regex(text):\n",
    "    \"\"\"\n",
    "    This method uses a Regex to separates punctuation at beginning and ends of strings. \n",
    "    :param text: a string of one statement\n",
    "    : return: tokens: list of words in text\n",
    "    \"\"\"\n",
    "    text = re.sub(r\"(\\w)([.,;:!?'\\\"”\\)])\", r\"\\1 \\2\", text) # separates punctuation at ends of strings\n",
    "    text = re.sub(r\"([.,;:!?'\\\"“\\(\\)])(\\w)\", r\"\\1 \\2\", text) # separates punctuation at beginning of strings\n",
    "    text = re.sub('\\d+', 'NUMBER',text)\n",
    "    tokens = text.split()\n",
    "    return tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_characters_after_tokenization(tokens):\n",
    "    \"\"\"\n",
    "    Removes characters from tokens. Preserving critical social media/twitter characters @ and #\n",
    "    : param tokens: Input list of words/tokens\n",
    "    : return: tokens: list of words in text\n",
    "    \"\"\"\n",
    "    # note preserving critical social media/twitter characters @ and #\n",
    "    p = '[{}]'.format(re.escape(string.punctuation)+'\\…').replace(\"@\", \"\").replace(\"\\#\", \"\")\n",
    "    pattern = re.compile(p)\n",
    "    filtered_tokens = [f for f in filter(None, [pattern.sub('', token) for token in tokens])]\n",
    "    return filtered_tokens\n",
    "\n",
    "def convert_to_lowercase(tokens):\n",
    "    \"\"\"\n",
    "    Converts the tokens to lowercase\n",
    "    : param tokens: Input list of words/tokens\n",
    "    : return: tokens: list of words in text\n",
    "    \"\"\"\n",
    "    return [token.lower() for token in tokens if token.isalpha()]\n",
    "\n",
    "def remove_stopwords(tokens):\n",
    "    \"\"\"\n",
    "    Removing the stopwords provided by NLTK.\n",
    "    : param tokens: Input list of words/tokens\n",
    "    : return: tokens: list of words in text\n",
    "    \"\"\"\n",
    "    stopword_list = nltk.corpus.stopwords.words('english')\n",
    "    filtered_tokens = [token for token in tokens if token not in stopword_list]\n",
    "    return filtered_tokens\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {},
   "outputs": [],
   "source": [
    "def apply_lemmatization(tokens, wnl=WordNetLemmatizer()):  \n",
    "    \"\"\"\n",
    "    Applying the lamitization using WordNetLemmatizer\n",
    "    : param tokens: Input list of words/tokens\n",
    "    : return: tokens: list of words in text\n",
    "    \"\"\"\n",
    "    return [wnl.lemmatize(token) for token in tokens]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pre_process(character_text):\n",
    "    \"\"\"\n",
    "    Pre-process all the concatenated lines of a character, \n",
    "    using tokenization, spelling normalization, removing punctuation, removing stop words, lemitization\n",
    "    and other techniques.    \n",
    "    :character_text:: a string with all of one character's lines\n",
    "    :return: tokens: list of words in text\n",
    "    \"\"\"\n",
    "    tokens_reg = using_regex(character_text)\n",
    "    tokens_rm_chr = remove_characters_after_tokenization(tokens_reg)\n",
    "    tokens_lc = convert_to_lowercase(tokens_rm_chr)\n",
    "    tokens_rm_stopwords = remove_stopwords(tokens_lc)\n",
    "    tokens = apply_lemmatization(tokens_rm_stopwords)\n",
    "    return tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create list of pairs of (character name, pre-processed character) \n",
    "training_corpus = [(name, pre_process(doc)) for name, doc in sorted(train_character_docs.items())]\n",
    "train_labels = [name for name, doc in training_corpus]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {},
   "outputs": [],
   "source": [
    "def to_feature_vector_dictionary(character_doc, extra_features):\n",
    "    \"\"\"\n",
    "    Converts a list of pre-processed tokens and extra features to a Dictionary as a function of the tokens.\n",
    "    1. Using multiple techniques such as, sentiment analysis (to get the positive, negative or neutral sentiments from sentenses)\n",
    "    2. Using POS taaging for every word in character document.\n",
    "    3. Trying multiple ngrams such as Unigram, Bigram and Trigram.\n",
    "    ::character_doc:: a list of pre-processed tokens\n",
    "    ::extra_features:: any extra features for the character to be added to feature vector dict\n",
    "    \"\"\"\n",
    "    #Vader Semintent Analysis\n",
    "    sentiment = SentimentIntensityAnalyzer()\n",
    "    s1 = []\n",
    "    s2 = []\n",
    "    new_char_doc_with_sentiment = []\n",
    "    for data in character_doc:\n",
    "        if data != 'eol':\n",
    "            s1.append(data)\n",
    "        else:\n",
    "            s1.append('eol')\n",
    "            s2.append(s1)\n",
    "            s1 = []\n",
    "        \n",
    "    for sentence in s2:\n",
    "        new_sen = \" \".join(sentence)\n",
    "        score = sentiment.polarity_scores(new_sen)\n",
    "        if score['pos'] > score['neg']:\n",
    "            new_sen += ' positive'\n",
    "        elif score['neg'] > score['pos']:\n",
    "            new_sen += ' negative'\n",
    "        else:\n",
    "            new_sen += ' neutral'\n",
    "        new_char_doc_with_sentiment.append(new_sen)\n",
    "        \n",
    "    #POS tag\n",
    "    sen_with_pos_tag = []\n",
    "    for val in new_char_doc_with_sentiment:\n",
    "        tokens_tag = pos_tag(val.split())\n",
    "        pos_tagged_docs = []\n",
    "        for val in tokens_tag:\n",
    "            pos_tagged_docs.append(val[0] + '/' + val[1])\n",
    "        sen_with_pos_tag.append(pos_tagged_docs)\n",
    "    v = sum(sen_with_pos_tag, [])\n",
    "\n",
    "    \n",
    "    #Unigram\n",
    "    ngram_data = list(ngrams(v, 1))\n",
    "    counts = Counter(ngram_data) \n",
    "    counts = dict(counts)\n",
    "    \n",
    "#     #Bigram\n",
    "#     ngram_data = list(ngrams(v, 2))\n",
    "#     counts = Counter(ngram_data) \n",
    "#     counts = dict(counts)\n",
    "    \n",
    "#     #Trigram\n",
    "#     ngram_data = list(ngrams(v, 3))\n",
    "#     counts = Counter(ngram_data)\n",
    "#     counts = dict(counts)\n",
    "    \n",
    "    \n",
    "#     counts = Counter(character_doc)  # for now a simple count\n",
    "#     counts = dict(counts)\n",
    "\n",
    "    # add the extra features, for now just adding one count for each extra feature\n",
    "    for feature in extra_features:\n",
    "        counts[feature] += 1\n",
    "    return counts  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<16x5305 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 13354 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 378,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "corpusVectorizer = DictVectorizer() # corpusVectorizor which will just produce sparse vectors from feature dicts\n",
    "# Any matrix transformers (e.g. tf-idf transformers) should be initialized here\n",
    "\n",
    "\n",
    "def create_document_matrix_from_corpus(corpus, fitting=False):\n",
    "    \"\"\"Method which fits different vectorizers\n",
    "    on data and returns a matrix.\n",
    "    \n",
    "    Currently just does simple conversion to matrix by vectorizing the dictionary. Improve this for Q3.\n",
    "    \n",
    "    ::corpus:: a list of (class_label, document) pairs.\n",
    "    ::fitting:: a boolean indicating whether to fit/train the vectorizers (should be true on training data)\n",
    "    \"\"\"\n",
    "    \n",
    "    # uses the global variable of the corpus Vectorizer to improve things\n",
    "    if fitting:\n",
    "        corpusVectorizer.fit([to_feature_vector_dictionary(doc, []) for name, doc in corpus])\n",
    "    doc_feature_matrix = corpusVectorizer.transform([to_feature_vector_dictionary(doc, []) for name, doc in corpus])\n",
    "    \n",
    "    #training_feature_matrix[0].toarray()\n",
    "    return doc_feature_matrix\n",
    "\n",
    "training_feature_matrix = create_document_matrix_from_corpus(training_corpus, fitting=True)\n",
    "training_feature_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<16x5305 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 13354 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 379,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_feature_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lines per character {'TANYA': 40, 'MAX': 40, 'SEAN': 35, 'SHIRLEY': 40, 'OTHER': 40, 'STACEY': 40, 'RONNIE': 40, 'JACK': 40, 'PHIL': 40, 'IAN': 40, 'JANE': 40, 'ROXY': 40, 'HEATHER': 40, 'MINTY': 40, 'CHRISTIAN': 40, 'CLARE': 40}\n",
      "Num. Characters:  16 \n",
      "\n",
      "TANYA Num of Words:  438\n",
      "MAX Num of Words:  737\n",
      "SEAN Num of Words:  366\n",
      "SHIRLEY Num of Words:  329\n",
      "OTHER Num of Words:  357\n",
      "STACEY Num of Words:  412\n",
      "RONNIE Num of Words:  464\n",
      "JACK Num of Words:  351\n",
      "PHIL Num of Words:  475\n",
      "IAN Num of Words:  508\n",
      "JANE Num of Words:  458\n",
      "ROXY Num of Words:  392\n",
      "HEATHER Num of Words:  411\n",
      "MINTY Num of Words:  470\n",
      "CHRISTIAN Num of Words:  489\n",
      "CLARE Num of Words:  405\n",
      "total words 7062\n"
     ]
    }
   ],
   "source": [
    "# get the validation data- only 40 lines used for each character\n",
    "val_character_docs = create_character_document_from_dataframe(val_data, max_line_count=40)\n",
    "print('Num. Characters: ',len(val_character_docs.keys()),\"\\n\")\n",
    "total_words = 0\n",
    "for name in val_character_docs.keys():\n",
    "    print(name, 'Num of Words: ',len(val_character_docs[name].split()))\n",
    "    total_words += len(val_character_docs[name].split())\n",
    "print(\"total words\", total_words)\n",
    "\n",
    "# create list of pairs of (character name, pre-processed character) \n",
    "val_corpus = [(name, pre_process(doc)) for name, doc in sorted(val_character_docs.items())]\n",
    "val_labels = [name for name, doc in val_corpus]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Just transform the val_feature_matrix, don't fit\n",
    "val_feature_matrix = create_document_matrix_from_corpus(val_corpus, fitting=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<16x5305 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 2122 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 382,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_feature_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_cosine_similarity(v1, v2):\n",
    "    \"\"\"Takes a pair of vectors v1 and v2 (1-d arrays e.g. [0, 0.5, 0.5])\n",
    "    returns the cosine similarity between the vectors\n",
    "    \"\"\"\n",
    "    \n",
    "    # compute cosine similarity manually\n",
    "    manual_cosine_similarity = np.dot(v1, v2)  /(norm(v1) * norm(v2))\n",
    "    \n",
    "    return manual_cosine_similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_IR_evaluation_scores(train_feature_matrix, test_feature_matrix, train_labels, test_labels):\n",
    "    \"\"\"\n",
    "    Computes an information retrieval based on training data feature matrix and test data feature matrix\n",
    "    returns 4-tuple:\n",
    "    ::mean_rank:: mean of the ranking of the target document in terms of similarity to the query/test document\n",
    "    1 is the best possible score.\n",
    "    ::mean_cosine_similarity:: mean cosine similarity score for the target document vs. the test document of the same class\n",
    "    ::accuracy:: proportion of test documents correctly classified\n",
    "    ::df:: a data frame with all the similarity measures of the test documents vs. train documents\n",
    "    \n",
    "    params:\n",
    "    ::train_feature_matrix:: a numpy matrix N x M shape where N = number of characters M = number of features\n",
    "    ::test_feature_matrix::  a numpy matrix N x M shape where N = number of characters M = number of features\n",
    "    ::train_labels:: a list of character names for the training data in order consistent with train_feature_matrix\n",
    "    ::test_labels:: a list of character names for the test data in order consistent with test_feature_matrix\n",
    "    \"\"\"\n",
    "    rankings = []\n",
    "    all_cosine_similarities = []\n",
    "    pairwise_cosine_similarity = []\n",
    "    pairs = []\n",
    "    correct = 0\n",
    "    for i, target in enumerate(test_labels):\n",
    "        # compare the left out character against the mean\n",
    "        idx = i \n",
    "        fm_1 = test_feature_matrix.toarray()[idx]\n",
    "        all_sims = {}\n",
    "        # print(\"target:\", target)\n",
    "        for j, other in enumerate(train_labels):\n",
    "            fm_2 = train_feature_matrix.toarray()[j]\n",
    "            manual_cosine_similarity = compute_cosine_similarity(fm_1, fm_2)\n",
    "            pairs.append((target, other))\n",
    "            pairwise_cosine_similarity.append(manual_cosine_similarity)\n",
    "            if other == target:\n",
    "                all_cosine_similarities.append(manual_cosine_similarity)\n",
    "            all_sims[other] = manual_cosine_similarity\n",
    "\n",
    "            # print(target, other, manual_cosine_similarity)\n",
    "        sorted_similarities = sorted(all_sims.items(),key=lambda x:x[1],reverse=True)\n",
    "        # print(sorted_similarities)\n",
    "        ranking = {key[0]: rank for rank, key in enumerate(sorted_similarities, 1)}\n",
    "        # print(\"Ranking for target\", ranking[target])\n",
    "        if ranking[target] == 1:\n",
    "            correct += 1\n",
    "        rankings.append(ranking[target])\n",
    "        # print(\"*****\")\n",
    "    mean_rank = np.mean(rankings)\n",
    "    mean_cosine_similarity = np.mean(all_cosine_similarities)\n",
    "    accuracy = correct/len(test_labels)\n",
    "    print(\"mean rank\", np.mean(rankings))\n",
    "    print(\"mean cosine similarity\", mean_cosine_similarity)\n",
    "    print(correct, \"correct out of\", len(test_labels), \"/ accuracy:\", accuracy )\n",
    "    \n",
    "    # get a dafaframe showing all the similarity scores of training vs test docs\n",
    "    df = pd.DataFrame({'doc1': [x[0] for x in pairs], 'doc2': [x[1] for x in pairs],\n",
    "                       'similarity': pairwise_cosine_similarity})\n",
    "\n",
    "    # display characters which are most similar and least similar\n",
    "    df.loc[[df.similarity.values.argmax(), df.similarity.values.argmin()]]\n",
    "    return (mean_rank, mean_cosine_similarity, accuracy, df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_heat_map_similarity(df):\n",
    "    \"\"\"Takes a dataframe with header 'doc1, doc2, similarity'\n",
    "    Plots a heatmap based on the similarity scores.\n",
    "    \"\"\"\n",
    "    test_labels =  sorted(list(set(df.sort_values(['doc1'])['doc1'])))\n",
    "    # add padding 1.0 values to either side\n",
    "    cm = [[1.0,] * (len(test_labels)+2)]\n",
    "    for target in test_labels:\n",
    "        new_row = [1.0]\n",
    "        for x in df.sort_values(['doc1', 'doc2'])[df['doc1']==target]['similarity']:\n",
    "            new_row.append(x)\n",
    "        new_row.append(1.0)\n",
    "        cm.append(new_row)\n",
    "    cm.append([1.0,] * (len(test_labels)+2))\n",
    "    #print(cm)\n",
    "    labels = [\"\"] + test_labels + [\"\"]\n",
    "    fig = plt.figure(figsize=(20,20))\n",
    "    ax = fig.add_subplot(111)\n",
    "    cax = ax.matshow(cm)\n",
    "    plt.title('Similarity matrix between documents as vectors')\n",
    "    fig.colorbar(cax)\n",
    "    ax.set_xticks(np.arange(len(labels)))\n",
    "    ax.set_yticks(np.arange(len(labels)))\n",
    "    ax.set_xticklabels( labels, rotation=45)\n",
    "    ax.set_yticklabels( labels)\n",
    "\n",
    "    for i in range(len(cm)):\n",
    "        for j in range(len(cm)):\n",
    "\n",
    "            text = ax.text(j, i, round(cm[i][j],3),\n",
    "                           ha=\"center\", va=\"center\", color=\"w\")\n",
    "\n",
    "    plt.xlabel('Training Vector Doc')\n",
    "    plt.ylabel('Test Vector Doc')\n",
    "    #fig.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean rank 7.3125\n",
      "mean cosine similarity 0.9860094066863742\n",
      "1 correct out of 16 / accuracy: 0.0625\n",
      "Mean Rank and mean cosine similarity with Tokenization: (7.3125, 0.9860094066863742)\n"
     ]
    }
   ],
   "source": [
    "# mean_rank, mean_cosine_simliarity, acc, df = compute_IR_evaluation_scores(training_feature_matrix, val_feature_matrix, train_labels, val_labels)\n",
    "# print(f\"Mean Rank and mean cosine similarity with Tokenization: {mean_rank, mean_cosine_simliarity}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "mean rank 7.3125\n",
    "\n",
    "mean cosine similarity 0.9860094066863742\n",
    "\n",
    "1 correct out of 16 / accuracy: 0.0625\n",
    "\n",
    "Mean Rank and mean cosine similarity with Tokenization: (7.3125, 0.9860094066863742)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean rank 5.4375\n",
      "mean cosine similarity 0.9951208062467722\n",
      "4 correct out of 16 / accuracy: 0.25\n",
      "Mean Rank and mean cosine similarity with lower case: (5.4375, 0.9951208062467722)\n"
     ]
    }
   ],
   "source": [
    "# mean_rank, mean_cosine_simliarity, acc, df = compute_IR_evaluation_scores(training_feature_matrix, val_feature_matrix, train_labels, val_labels)\n",
    "# print(f\"Mean Rank and mean cosine similarity with lower case: {mean_rank, mean_cosine_simliarity}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "mean rank 5.4375\n",
    "\n",
    "mean cosine similarity 0.9951208062467722\n",
    "\n",
    "4 correct out of 16 / accuracy: 0.25\n",
    "\n",
    "Mean Rank and mean cosine similarity with lower case: (5.4375, 0.9951208062467722)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean rank 4.5\n",
      "mean cosine similarity 0.9971964461936824\n",
      "3 correct out of 16 / accuracy: 0.1875\n",
      "Mean Rank and mean cosine similarity with Removing stopwords: (4.5, 0.9971964461936824)\n"
     ]
    }
   ],
   "source": [
    "# mean_rank, mean_cosine_simliarity, acc, df = compute_IR_evaluation_scores(training_feature_matrix, val_feature_matrix, train_labels, val_labels)\n",
    "# print(f\"Mean Rank and mean cosine similarity with Removing stopwords: {mean_rank, mean_cosine_simliarity}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "mean rank 4.5\n",
    "\n",
    "mean cosine similarity 0.9971964461936824\n",
    "\n",
    "3 correct out of 16 / accuracy: 0.1875\n",
    "\n",
    "Mean Rank and mean cosine similarity with Removing stopwords: (4.5, 0.9971964461936824)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean rank 4.6875\n",
      "mean cosine similarity 0.996453490972118\n",
      "5 correct out of 16 / accuracy: 0.3125\n",
      "Mean Rank and mean cosine similarity after applying Lammatization: (4.6875, 0.996453490972118)\n"
     ]
    }
   ],
   "source": [
    "# mean_rank, mean_cosine_simliarity, acc, df = compute_IR_evaluation_scores(training_feature_matrix, val_feature_matrix, train_labels, val_labels)\n",
    "# print(f\"Mean Rank and mean cosine similarity after applying Lammatization: {mean_rank, mean_cosine_simliarity}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "mean rank 4.6875\n",
    "\n",
    "mean cosine similarity 0.996453490972118\n",
    "\n",
    "5 correct out of 16 / accuracy: 0.3125\n",
    "\n",
    "Mean Rank and mean cosine similarity after applying Lammatization: (4.6875, 0.996453490972118)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean rank 3.375\n",
      "mean cosine similarity 0.8929213375666305\n",
      "10 correct out of 16 / accuracy: 0.625\n",
      "Mean Rank and mean cosine similarity by applying all selected preprocessing and applying Sentiment Analysis, Unigram and POS tagging: (3.375, 0.8929213375666305)\n"
     ]
    }
   ],
   "source": [
    "mean_rank, mean_cosine_simliarity, acc, df = compute_IR_evaluation_scores(training_feature_matrix, val_feature_matrix, train_labels, val_labels)\n",
    "print(f\"Mean Rank and mean cosine similarity by applying all selected preprocessing and applying Sentiment Analysis, Unigram and POS tagging: {mean_rank, mean_cosine_simliarity}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "mean rank 3.375\n",
    "\n",
    "mean cosine similarity 0.8929213375666305\n",
    "\n",
    "10 correct out of 16 / accuracy: 0.625\n",
    "\n",
    "Mean Rank and mean cosine similarity by applying all selected preprocessing and applying Sentiment Analysis, Unigram and POS tagging: (3.375, 0.8929213375666305)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean rank 6.125\n",
      "mean cosine similarity 0.753238456052451\n",
      "4 correct out of 16 / accuracy: 0.25\n",
      "Mean Rank and mean cosine similarity by applying all selected preprocessing and applying Sentiment Analysis, Bigram and POS tagging: (6.125, 0.753238456052451)\n"
     ]
    }
   ],
   "source": [
    "# mean_rank, mean_cosine_simliarity, acc, df = compute_IR_evaluation_scores(training_feature_matrix, val_feature_matrix, train_labels, val_labels)\n",
    "# print(f\"Mean Rank and mean cosine similarity by applying all selected preprocessing and applying Sentiment Analysis, Bigram and POS tagging: {mean_rank, mean_cosine_simliarity}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "mean rank 6.125\n",
    "\n",
    "mean cosine similarity 0.753238456052451\n",
    "\n",
    "4 correct out of 16 / accuracy: 0.25\n",
    "\n",
    "Mean Rank and mean cosine similarity by applying all selected preprocessing and applying Sentiment Analysis, Bigram and POS tagging: (6.125, 0.753238456052451)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean rank 6.1875\n",
      "mean cosine similarity 0.12991717707323638\n",
      "2 correct out of 16 / accuracy: 0.125\n",
      "Mean Rank and mean cosine similarity by applying all selected preprocessing and applying Sentiment Analysis, Trigram and POS tagging: (6.1875, 0.12991717707323638)\n"
     ]
    }
   ],
   "source": [
    "# mean_rank, mean_cosine_simliarity, acc, df = compute_IR_evaluation_scores(training_feature_matrix, val_feature_matrix, train_labels, val_labels)\n",
    "# print(f\"Mean Rank and mean cosine similarity by applying all selected preprocessing and applying Sentiment Analysis, Trigram and POS tagging: {mean_rank, mean_cosine_simliarity}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "mean rank 6.1875\n",
    "\n",
    "mean cosine similarity 0.12991717707323638\n",
    "\n",
    "2 correct out of 16 / accuracy: 0.125\n",
    "\n",
    "Mean Rank and mean cosine similarity by applying all selected preprocessing and applying Sentiment Analysis, Trigram and POS tagging: (6.1875, 0.12991717707323638)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x2000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_heat_map_similarity(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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\"didn't\", '_EOL_', \"I'm\", 'not', 'a', '_EOL_', \"I'm\", '_EOL_', 'Yeah,', '_EOL_', 'have', 'gone', 'away', 'Am', 'I', 'a', 'or', 'what?', '_EOL_', 'You', 'gonna', 'me', '_EOL_', \"Let's\", 'just', 'say', \"he's\", 'a', 'bit', 'in', 'his', 'pay', 'this', '_EOL_', 'it', \"wasn't\", 'to', 'eh?', 'the', 'was', 'having', 'a', 'The', 'look', 'on', 'your', 'you', 'thought', 'it', 'was', 'going', 'to', 'be', 'me,', \"didn't\", 'you?', '_EOL_', 'You', '_EOL_', 'give', 'you', 'a', 'if', 'you', 'like.', 'Come', 'on,', \"I'll\", 'you', 'wear', 'my', '_EOL_', '_EOL_', \"You're\", 'not', 'getting', 'up', 'to', 'make', 'me', 'are', 'you.', 'It', 'was', 'a', 'right?', 'have', 'done', 'it', 'and', \"let's\", 'forget', 'it', 'ever', '_EOL_', 'What', 'if', 'I', \"don't\", 'want', 'What', 'if', 'I', 'want', 'this', 'to', 'again?', '_EOL_', 'get', 'back', 'to', 'This', 'is', 'to', 'be', 'a', '_EOL_', 'Yes,', 'well,', 'make', 'the', 'of', 'it.', \"It's\", 'your', 'turn', 'next', 'time.', 'If', 'there', 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\"I've\", 'just', 'come', 'here', 'now', 'to', 'I', \"you'll\", 'give', 'me', 'that', 'chance.', '_EOL_', 'I', 'know.', 'Thanks.', 'like', 'you', 'best', 'not', 'to', 'than', 'they', 'want', 'to', 'go.', '_EOL_', \"I'm\", 'going', 'to', 'go', 'back', 'and', 'start', 'the', 'Why', \"don't\", 'you', 'stay', 'here', 'for', 'a', 'me', 'up.', 'I', 'think', 'I', 'need', 'it.', '_EOL_', '_EOL_', 'No', 'problem.', 'Not', 'as', 'long', 'as', \"you've\", 'it.', '_EOL_', 'please.', 'no', 'I', 'thought', 'it', 'was', 'your', 'turn', 'to', 'make', '_EOL_', 'one', 'if', 'I', 'you.', 'How', 'about', 'you', 'it', \"you're\", '_EOL_', 'You', 'either', 'that', 'right', 'now,', 'or', 'get', 'out.', '_EOL_', 'But', 'you', 'like', 'that', 'thought', 'in', 'my', \"Don't\", 'you?', '_EOL_', 'man.', 'his', 'though', 'it', 'him', 'his', 'family.', '_EOL_', 'for', 'This', 'is', \"isn't\", 'it?', 'can', 'talk', 'now.', 'what', 'we', 'want', 'to', 'than', 'what', 'we', 'care', 'about', 'want', 'to', '_EOL_', 'No,', 'that', 'was', 'I', 'do', 'want', 'us', 'to', 'get', 'on.', 'I', 'like', 'Ronnie,', 'so', \"it's\", 'in', 'my', 'But', \"it's\", 'not', 'in', 'your', 'is', 'it', \"You're\", 'thinking', '-', 'why', 'should', 'I', 'make', 'the', 'to', 'get', 'along', 'with', 'this', 'I', \"don't\", 'even', 'like.', '_EOL_', '_EOL_', 'And', 'with', 'all', 'this', 'new', '-', 'they', 'still', '_EOL_', \"You're\", 'a', 'stupid', 'You', 'with', 'you', 'know', 'nothing', 'with', 'no', 'of', 'how', 'they', 'might', 'Well,', 'you', 'know', 'now,', \"don't\", 'you?', '_EOL_', \"It's\", 'With', 'what', 'the', 'police', 'You', 'sold', 'stolen', 'cars', 'to', 'Kevin', 'Wicks', 'who', 'then', 'one', '-', 'Yes,', \"that's\", 'it.', 'thinking', 'with', 'your', 'fists.', 'Make', 'it', 'even', 'for', 'me,', 'why', \"don't\", 'you?', 'No,', \"there's\", 'else', 'here.', 'And', 'I', \"ain't\", 'it', 'but', \"you've\", 'had', 'your', 'cheap', 'You', 'want', 'to', 'go', 'to', 'when', 'my', 'back', \"ain't\", \"let's\", 'do', 'it', '-', 'see', 'how', 'long', 'you', '_EOL_', 'For', 'the', \"I'm\", 'going', 'to', 'give', 'you', 'it', 'back.', \"It's\", 'just', 'a', 'me', 'me', 'with', 'respect.', '_EOL_', 'That', 'and', 'the', 'car', '_EOL_', \"You're\", 'going', 'to', 'me', 'the', 'for', 'the', 'car', 'lot.', 'At', 'half', 'the', 'a', 'way', 'of', \"Let's\", 'call', 'it', 'a', 'of', '_EOL_', \"That's\", 'for', \"isn't\", 'it?', 'Cos', 'once', 'I', 'give', 'that', 'tape', 'to', 'the', 'you', \"won't\", 'be', 'seeing', 'him', 'for', 'a', '_EOL_', 'than', 'you', 'ever', 'did.', 'I', 'make', 'it', 'my', 'business', 'to', 'know', 'exactly', 'who', \"I'm\", 'dealing', 'with,', 'what', \"they're\", 'you', 'never', 'did', 'the', 'Just', 'be', 'all', 'I', 'want', 'is', 'the', 'car', 'lot.', \"You've\", 'got', 'until', '_EOL_', 'I', 'know.', \"That's\", 'not', 'my', 'problem.', '_EOL_', 'You', 'tell', 'Ronnie', 'this,', 'the', 'tape', 'to', 'the', 'police.', 'You', 'it', 'to', 'the', 'police.', 'This', 'is', 'just', 'you', 'and', 'me.', 'To', 'the', 'Make', 'things', 'even', 'us.', 'I', 'really', 'did', 'try', 'and', 'you', 'with', 'Louise,', 'and', 'now', 'you', 'me', 'something', 'in', 'So', 'why', \"don't\", 'you', 'phone', 'your', 'and', 'start', 'saving', 'yourself', 'you', 'still', '_EOL_', 'I', 'doing', 'it.', '_EOL_', 'what', 'are', 'you', '_EOL_', 'When', \"you're\", 'One', 'more', 'for', 'the', '_EOL_', 'come', 'and', 'join', 'us.', '_EOL_', 'again', 'all', 'only', \"let's\", 'make', 'it', '-', \"We're\", 'all', '-', 'and', \"you've\", 'got', 'join', 'us', 'this', 'time,', 'No', '_EOL_', 'I', 'know.', 'of', '_EOL_', 'You', 'might', 'have', 'had', 'you', '200', 'notes', 'to', 'keep', 'That', 'makes', 'you', 'an', 'you', 'I', 'give', 'that', 'tape', 'to', 'the', 'police', 'now', 'you', 'be', 'a', '-', 'is', 'that', 'how', 'you', 'want', 'this', 'to', 'Look,', 'you', 'wanted', 'for', 'right?', '_EOL_', 'And', \"that's\", 'exactly', 'what', \"he's\", 'going', 'to', 'do.', \"He's\", 'going', 'to', 'pay', 'by', 'giving', 'you', 'the', 'car', 'lot.', 'The', \"it's\", \"That's\", 'right.', 'it', 'over', 'to', 'me', 'then', \"I'll\", 'give', 'it', 'to', 'you', 'when', \"you're\", '_EOL_', \"Don't\", 'He', \"can't\", 'you.', '_EOL_', 'me.', '_EOL_', '-', '_EOL_', 'Just', 'get', 'a', 'okay?', 'Look', 'at', 'me.', \"You're\", 'going', 'to', 'be', 'fine.', \"won't\", 'you.', 'I', 'it.', 'Now', 'sit', 'And', 'cos', 'what', 'you', 'here', 'is', 'going', 'to', 'how', 'the', 'rest', 'of', 'your', '_EOL_', 'I', 'you', 'for', 'a', 'Darren.', \"You're\", 'and', \"you've\", 'got', 'a', 'of', '-', 'not', 'many', 'to', 'take', 'on.', 'But', 'you', 'need', 'to', 'to', 'me', 'you', 'can', 'live', 'with', 'the', 'of', 'the', 'you', 'Do', 'and', \"you'll\", 'be', 'a', 'lot', 'more', 'than', 'a', 'car', 'lot', 'in', 'a', 'few', 'years', 'time.', 'what', \"I'm\", 'you', 'is', 'an', 'If', 'you', \"can't\", 'it', 'just', 'say', 'so', 'now.', \"I'll\", 'give', 'you', 'some', 'you', 'can', 'start', 'again', 'Or', 'you', 'can', 'accept', 'the', 'stay', 'and', 'learn', 'how', 'to', 'really', 'play', 'the', \"It's\", 'up', 'to', 'you.', 'That', 'or', 'What', 'are', 'you', 'going', 'to', 'do?', '_EOL_', \"You're\", 'late.', '_EOL_', 'What', 'are', 'you', '_EOL_', \"that's\", 'I', 'like', 'that.', 'could', 'really', 'get', 'on,', 'you', 'and', 'me', '-', 'if', 'you', 'just', 'us', 'a', 'chance.', '_EOL_', \"That's\", 'the', '-', 'on', 'the', 'And', \"that's\", 'the', 'I', 'made', 'for', 'the', 'this', '_EOL_', 'Like', 'I', 'this', 'makes', 'us', 'All', 'I', 'want', 'now,', 'is', 'your', 'You', 'need', 'a', 'from', 'me,', \"it's\", 'Just', \"That's\", 'how', 'this', 'should', 'work.', '_EOL_', 'No', 'hard', 'then.', '_EOL_', 'Long', 'term', 'view.', '_EOL_', 'You', 'really', 'hard', 'over', 'the', 'weekend.', \"There's\", 'a', 'little', 'in', 'there', 'for', 'you.', '_EOL_', 'Do', 'you', 'wanna', 'tell', 'about', '_EOL_', 'Yeah.', 'It', \"That's\", 'why', 'we', 'had', 'to', 'some', 'staff.', '_EOL_', \"It's\", 'just', 'a', 'one', '_EOL_', 'Look,', \"it's\", 'gonna', 'be', 'a', 'tonight.', '_EOL_', 'is', 'that', 'for', 'me?', '_EOL_', 'Do', 'what', 'you', 'need', 'to', 'do.', '_EOL_', 'try.', 'But', 'I', 'think', 'you', 'the', '_EOL_', 'the', 'back.', '_EOL_', 'What', 'are', 'you', 'doing', 'back', 'here?', '_EOL_', 'So', 'how', 'did', 'the', 'go', '_EOL_', 'Look', 'this', 'you', 'two', 'and', 'your', '-', \"what's\", 'the', 'big', 'deal?', '_EOL_', 'But', 'you', 'still', 'going', 'to', 'do?', '_EOL_', 'The', 'still', 'you', 'know.', '_EOL_', 'Here,', 'Ronnie', 'me', \"it's\", 'your', '_EOL_', 'well', \"she's\", 'just', 'me', 'to', 'your', 'do.', 'You', \"don't\", 'mind,', 'do', 'you?', '_EOL_', \"I'm\", 'just', 'I', 'might', 'even', 'you', 'a', '_EOL_', 'Well', \"isn't\", 'that', 'the', '_EOL_', 'Where', 'you', 'going?', '_EOL_', 'Just', 'gonna', 'off', 'were', 'ya?', '_EOL_', 'I', \"don't\", 'know', 'if', \"I'd\", 'call', 'it.', 'Not', 'last', '_EOL_', 'Oh', 'come', 'on,', 'more', '_EOL_', \"What's\", 'the', '_EOL_', \"That's\", 'not', 'tonight', 'is', 'it?', '_EOL_', 'Oh', 'yeah', 'yeah.', \"It's\", 'the', 'I', 'thought', 'about', 'when', 'I', 'up.', '_EOL_', 'Is', 'that', 'all', 'I', 'get', 'then?', '_EOL_', 'Yeah.', '_EOL_', 'Er,', 'no.', '_EOL_', 'Oh,', \"it's\", 'my', '_EOL_', 'Yeah.', '_EOL_', 'Look', 'I', 'just', 'wanna', 'get', 'on', 'with', '_EOL_', 'If', \"we're\", 'going', 'over', 'the', 'Vic,', 'why', 'did', 'you', 'come', 'over', 'here', 'for', 'me?', '_EOL_', 'What', 'like', 'a', 'proper', '_EOL_', 'You', 'look', '_EOL_', \"It's\", 'gonna', 'be', 'fine.', '_EOL_', 'Yeah,', 'happy', '_EOL_', 'I', 'got', 'you', \"It's\", 'for', 'a', 'special', '_EOL_', 'Yeah,', 'you', \"wouldn't\", 'know', 'it', \"wasn't\", 'the', 'real', '_EOL_', 'I', 'venison.', '_EOL_', 'Yeah,', \"I'd\", 'be', 'happy', 'to.', '_EOL_', 'I', \"didn't\", 'you', 'were', '_EOL_', 'You', 'not', 'about', 'getting', '_EOL_', 'Oh,', 'I', 'Not', 'being', 'to', 'do', 'what', 'I', 'used', 'to', 'do.', 'You', 'know,', 'getting', 'my', '_EOL_', 'Well', 'you', 'can', 'see', 'that.', \"You're\", 'of', '_EOL_', 'the', '_EOL_', 'Oh,', 'watch', 'it,', \"you're\", 'going', 'to', 'get', 'me', '_EOL_', 'Oh', \"there's\", 'no', 'of', 'I', 'mean', 'you', 'can', 'your', \"can't\", '_EOL_', 'That', 'was', '_EOL_', 'So', 'did', 'you', 'really', 'make', 'this', '_EOL_', 'So', 'I', 'thought', 'we', 'were', 'mates.', '_EOL_', 'I', 'thought', 'we', \"that's\", 'why', 'you', 'sold', 'me', 'the', 'car', 'lot.', '_EOL_', 'What?', 'he', 'told', 'you?', 'Yeah', 'he', 'was', 'so', 'he', 'sold', 'it', 'to', 'me', 'at', 'a', 'down', '_EOL_', 'Yeah,', 'for', '_EOL_', '_EOL_', 'Oh', \"it's\", 'just', '_EOL_', 'I', \"don't\", 'know.', '_EOL_', 'Look', 'he', 'was', 'I', 'was', 'just', 'him', 'out.', 'What', 'are', 'you', 'about?', '_EOL_', \"I'm\", 'gonna', 'open', \"I'll\", 'see', 'you', 'down', 'there.', '_EOL_', 'You', \"can't\", 'me', 'to', 'get', 'on', 'with', \"I'm\", 'an', 'a', '-', '_EOL_', 'as', 'Roxy,', 'same', 'as', 'I', \"don't\", 'have', 'a', 'with', 'them.', '_EOL_', 'what', \"d'you\", 'wanna', 'know?', '_EOL_', \"d'you\", 'really', 'think', 'this', 'is', 'getting', 'us', 'Cos', \"I've\", 'got', 'some', '_EOL_', 'You', 'can', 'ask', 'me', 'anything', 'you', 'like.', \"We'll\", 'play', 'When', 'I', 'get', 'back.', 'Right', 'now,', \"I've\", 'gotta', 'go', 'my', 'tax', '_EOL_', 'So,', 'where', 'were', \"That's\", '_EOL_', '_EOL_', 'What', 'the', 'on', 'that?', '_EOL_', \"I'm\", \"that's\", 'a', 'no', 'can', 'do.', '_EOL_', 'of', '-', 'daughter.', \"It's\", 'her', 'I', 'said', \"I'd\", 'take', 'her', 'to', 'the', \"She's\", \"didn't\", 'I', 'tell', 'ya?', '_EOL_', 'Yeah', 'it', 'as', 'I', 'might', 'they', \"wouldn't\", 'put', 'on', 'another', '_EOL_', \"Don't\", '_EOL_', 'Look', 'when', 'things', 'wrong', 'me', 'and', '-', 'There', 'was', 'a', 'lot', 'of', 'a', 'lot', 'of', 'That', \"wasn't\", 'for', 'any', 'of', 'us.', 'of', 'all', 'I', 'to', 'keep', 'my', '_EOL_', '_EOL_', '_EOL_', 'come', 'with', 'us?', '_EOL_', \"I'd\", 'like', 'that.', 'I', 'know', 'she', '_EOL_', 'Look,', 'I', 'know', \"it's\", 'been', 'hard', 'for', 'you', 'what', 'with', 'your', 'on', 'you.', '_EOL_', \"That's\", 'I', 'wanted', 'to', 'give', 'you', 'I', 'mean', 'if', \"you're\", 'gonna', 'be', 'coming', 'round', 'more', 'now.', '_EOL_', \"I'm\", 'not', 'Ronnie.', 'I', 'want', 'it', 'to', 'work.', 'Do', 'you?', '_EOL_', 'Just', 'wanted', 'to', 'say', 'thanks.', 'like', \"it's\", 'gonna', 'be', 'a', 'busy', '_EOL_', 'Jack', 'in', 'the', '_EOL_', 'not', 'her', 'phone.', 'Thought', 'I', 'better', 'get', '_EOL_', 'Then', 'I', 'thought', \"I'd\", 'go', 'you', 'know,', 'have', 'a', 'up.', 'And', 'you', 'out', 'to', 'So', 'we', 'can', 'I', 'know', \"that's\", 'what', 'you', 'from', 'now', 'on,', \"we'll\", 'more', 'time', '-', '_EOL_', \"It's\", 'not', 'how', 'it', 'you', \"don't\", 'know', 'the', 'half', 'of', 'it.', '_EOL_', 'he', 'was', 'a', \"We're\", 'talking', 'been', 'him', 'for', 'And', 'I', 'was', 'the', 'one', 'that', 'got', 'him.', 'I', 'him', 'down', 'for', 'There', 'was', 'a', 'of', 'of', 'Oh,', 'I', 'made', 'sure', 'He', 'was', 'my', 'of', '-', 'and', 'were', 'at', 'a', 'the', 'them', 'both', '_EOL_', 'was', 'that', 'big', 'time.', '_EOL_', 'Yeah.', 'he', 'make', 'sure', 'I', 'knew', 'it.', 'got', 'she', 'her', 'But', '-', 'She', 'her', 'She', \"won't\", 'again.', 'and', 'we', 'at', 'Just', 'getting', 'it,', 'day', 'by', 'But', \"I'd\", 'her', 'me,', 'you', 'know,', 'looking', 'at', 'me.', 'I', 'knew', 'that', \"weren't\", 'in', 'her', 'eyes.', '_EOL_', 'No.', 'If', \"I'd\", 'But', 'no,', 'Jack', 'he', 'he', 'to', 'his', 'in', 'it,', \"don't\", 'we', \"couldn't\", 'get', 'it.', 'So', 'we', 'I', 'my', '_EOL_', 'I', 'a', 'life,', 'Ronnie.', 'If', \"you're\", 'gonna', 'time', 'someone', '-', '_EOL_', \"I'm\", 'gonna', 'go', 'and', 'get', 'up.', '_EOL_', 'No', \"I'm\", \"I'm\", 'just', 'gonna', 'take', 'it', 'easy', 'tonight.', '_EOL_', 'Well,', 'I', 'thought', \"I'd\", 'you.', '_EOL_', 'Well', 'I', 'your', '_EOL_', 'Well', 'it', \"you're\", 'not', 'allowed', 'to', 'open', 'them', 'until', 'tomorrow.', 'But', 'I', 'was', 'wondering', '-', '_EOL_', 'About', 'maybe', 'taking', 'you', 'out', 'for', 'a', '_EOL_', 'Just', 'a', 'thought', '-', '_EOL_', 'Look', 'I', \"don't\", 'want', 'to', 'any', '-', '_EOL_', \"It's\", 'the', '_EOL_', 'No', \"it's\", 'that', 'one.', '_EOL_', 'Well', \"I'm\", '_EOL_', \"I'll\", 'you', 'yeah?', '_EOL_', 'No.', '_EOL_', 'Come', 'on.', '_EOL_', 'I', 'best', 'be', 'getting', 'off.', '_EOL_', 'Yeah', 'I', '_EOL_', 'got', 'that', 'coming', 'round', 'this', 'afternoon.', 'You', 'had', 'you?', 'be', 'a', 'for', 'once', 'a', \"it's\", 'exactly', 'what', 'we', 'need', 'now', 'that', 'is', '_EOL_', 'Yeah', 'I', 'had', 'things', 'to', 'do.', '_EOL_', '_EOL_', 'of', 'night', 'I', '_EOL_', 'Look', 'it', \"it's\", '_EOL_', 'if', 'you', \"can't\", 'keep', 'your', 'in', 'check', 'then', 'maybe', 'I', 'should', 'see', 'him', '_EOL_', 'This', 'is', 'Ronnie', 'my', 'business', '_EOL_', 'Yeah,', 'this', 'is', 'it.', '_EOL_', 'do', 'our', '_EOL_', 'So', 'what', 'sort', 'of', 'are', 'you', '_EOL_', 'I', 'just', 'got', 'the', 'phone', '_EOL_', '_EOL_', 'we', 'could', 'join', '_EOL_', \"I'm\", 'sure', 'the', \"wouldn't\", 'if', 'we', 'it', 'to', 'them.', '_EOL_', 'be', 'a', 'bit', 'for', 'on', 'the', 'door', '_EOL_', \"It's\", 'not', 'either.', '_EOL_', 'I', '_EOL_', 'me.', 'Look', 'Ronnie', '-', 'I', 'just', '_EOL_', 'Yeah.', '_EOL_', \"I'll\", 'come', '_EOL_', 'Look', 'I', 'wanted', 'to', 'be', 'there', 'But', 'we', 'both', 'know', 'what', 'would', 'have', 'happened', 'if', \"I'd\", '_EOL_', \"We're\", 'not', 'living', '_EOL_', \"There's\", 'no', 'in', 'it.', '_EOL_', '_EOL_', 'I', '_EOL_', \"You'll\", 'never', 'be', 'in', 'the', 'how', 'can', 'you,', 'make', 'sure', 'of', 'that', 'if', 'nothing', '_EOL_', 'me', 'what?', '_EOL_', 'of', 'the', '_EOL_', 'Where', 'are', 'you', 'going?', '_EOL_', 'So,', \"what's\", 'this,', '_EOL_', 'You', 'out', 'because', 'I', \"didn't\", 'stay', '_EOL_', 'So', 'what', 'is', 'this', 'then?', '_EOL_', 'No,', \"you're\", 'not', 'taking', 'her', 'away', 'from', 'me.', '_EOL_', 'Look', '-', 'we', 'got', 'it', '_EOL_', \"I've\", 'moved', 'out.', '_EOL_', \"It's\", 'only', \"'cause\", 'you', 'keep', 'me.', '_EOL_', 'What?', 'the', 'other', 'one', 'out', 'you', 'mean.', '_EOL_', 'Well', 'it', 'might', 'as', 'well', 'when', \"you're\", 'in', 'this', 'sort', 'of', '_EOL_', '-', '_EOL_', 'What', 'does', 'this', 'mean', '-', 'what', 'does', 'that', '_EOL_', 'I', \"don't\", 'know.', '_EOL_', 'So', 'why', 'this?', 'Why', 'last', 'night?', '_EOL_', 'No.', '_EOL_', 'See', 'you', 'And', 'good', 'to', 'you', 'too', '_EOL_', 'thanks.', '_EOL_', '-', '_EOL_', 'Yeah', 'well', 'I', 'you', 'could', 'tell', 'her', 'if', 'you', 'like', '-', '_EOL_', 'But', 'there', 'much', 'seeing', 'as', 'how', 'it', 'like', \"Ronnie's\", 'had', 'enough', 'of', 'me', 'and', 'cleared', 'off.', '_EOL_', 'And', 'there', 'was', 'me', 'thinking', 'you', 'were', 'looking', 'out', 'for', 'Ronnie.', '_EOL_', 'And', 'even', '_EOL_', \"You're\", 'of', '_EOL_', 'Yeah', 'I', 'I', 'that', '_EOL_', 'Well', 'you', 'have', 'gone', 'my', 'back.', '_EOL_', 'But', 'if', \"that's\", 'the', 'way', 'you', 'free', 'to', 'mess', 'up', 'you', 'like.', '_EOL_', 'So', 'does', 'this', 'mean', \"we're\", 'back', '_EOL_', 'What,', 'you', 'of', 'me?', '_EOL_', 'If', \"that's\", 'what', 'you', '_EOL_', \"Let's\", 'go', 'back', 'to', '_EOL_', \"What's\", 'the', '_EOL_', 'Why', 'you', 'so', 'about', 'what', 'she', '_EOL_', 'If', 'she', 'you', 'up', 'so', 'why', \"don't\", 'you', 'just', 'move', 'out.', '_EOL_', 'Yeah.', 'Why', 'not.', '_EOL_', 'I', 'was', 'being', '_EOL_', 'like', 'to', 'me.', '_EOL_', 'And', 'the', 'point', 'to', 'this', '_EOL_', 'Ronnie', 'came', 'to', 'see', 'me.', '_EOL_', 'Yeah,', 'it', 'was', '_EOL_', 'If', 'you', \"don't\", 'me,', 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'No,', 'Oh', 'shut', 'up.', 'Come', 'on', \"let's\", 'go.', '_EOL_', 'Thank', 'you.', '_EOL_', 'Yeah,', 'I', 'Come', 'on.', '_EOL_', 'Happy', '_EOL_', 'Yeah,', \"that's\", 'why', 'he', 'him', 'this', 'wine', 'as', '_EOL_', 'This', 'amazing', 'Auntie', 'What', 'is', 'it?', '_EOL_', 'Oh', 'right.', '_EOL_', 'did', '_EOL_', '_EOL_', 'You', 'alright?', '_EOL_', '_EOL_', 'What', 'did', 'you', 'do', 'that', '_EOL_', 'I', \"can't\", 'believe', 'you', 'just', 'did', 'that.', 'Here', 'let', 'me', 'you', 'up.', '_EOL_', 'I', 'think', 'we', 'should', '_EOL_', 'I', \"don't\", 'understand.', 'You', 'and', \"can't\", '_EOL_', 'It', 'was', 'a', 'big', 'thing', 'me', 'bringing', 'you', 'and', \"you've\", 'been', 'up', 'and', 'doing', '_EOL_', 'How', 'did', 'he', 'even', 'come', 'to', 'sell', 'it', 'to', 'you?', '_EOL_', 'Why', 'would', 'you', 'even', 'buy', 'it', '_EOL_', 'Oh,', 'I', 'my', 'make', 'up', 'at', '_EOL_', 'Okay.', '_EOL_', 'I', 'just', 'wanted', 'to', '_EOL_', 'What?', '_EOL_', 'Everything', 'there', 'is', 'to', '_EOL_', 'No', 'I', \"didn't\", 'know', '_EOL_', 'I', 'know', 'enough.', '_EOL_', 'Yeah,', 'yeah', \"she's\", \"they're\", 'and', 'got', 'a', 'daughter.', '_EOL_', 'I', 'him', 'one', 'hundred', 'per', \"I'm\", 'not', 'gonna', 'to', 'let', 'some', 'get', 'in', 'the', 'way', 'of', 'what', \"we've\", '_EOL_', 'No,', 'no', 'is', 'still', 'Well', \"don't\", 'worry,', \"I'll\", 'just', 'find', 'my', 'make', 'up', 'and', 'then', \"I'll\", 'be', 'down', 'No', 'I', 'think', 'it', 'in', 'the', 'Well', \"that's\", \"it's\", 'great', 'that', \"it's\", 'it', 'should', 'be', 'a', 'good', 'Oh', 'here', 'it', 'is,', \"I've\", 'found', 'it.', \"I've\", 'got', 'what', 'I', '_EOL_', 'I', 'was', 'just', 'gonna', 'come', 'over', '-', '_EOL_', 'I', 'thought', 'we', 'a', \"that's\", 'all.', '_EOL_', 'Look,', 'why', \"don't\", 'you', 'take', 'back.', 'And', \"we'll\", 'go', 'and', 'talk', 'about', 'it', 'in', 'the', '_EOL_', 'we', 'got', 'back', 'to', 'we', 'had', 'okay.', '_EOL_', 'Look,', 'bringing', 'him', 'over', 'maybe', \"wasn't\", 'one', 'of', 'my', 'best', '-', '_EOL_', 'You', 'just', \"can't\", 'can', 'you?', 'You', \"can't\", 'me', 'having', 'a', 'at', '_EOL_', 'You', \"didn't\", 'even', 'make', 'it', 'that', '_EOL_', 'Same', 'as', 'me.', '_EOL_', \"ain't\", 'exactly', 'part', 'of', 'your', 'club.', 'I', \"don't\", 'know', 'about', 'you.', '_EOL_', 'I', 'dunno.', 'What', 'is', 'your', '_EOL_', 'I', 'was', 'just', 'getting', 'up', '-', '_EOL_', 'Why', \"don't\", 'we', 'go', 'out?', '_EOL_', 'How', 'next', '_EOL_', 'The', 'first.', '_EOL_', '_EOL_', 'I', 'thought', 'it', 'was', 'her', 'birthday', 'this', '_EOL_', 'You', \"don't\", 'talk', 'about', 'her', 'do', 'you.', '_EOL_', 'I', 'understand.', 'be', 'really', 'if', 'your', 'is', '_EOL_', 'on', 'you.', '_EOL_', 'If', 'you', 'and', \"can't\", 'to', 'be', 'in', 'the', 'why', \"don't\", 'you', 'take', \"don't\", 'need', 'anything', 'do', 'We', 'could', 'go', 'get', 'a', '-', '_EOL_', '_EOL_', 'Why', 'not?', '_EOL_', 'I', 'make', 'my', 'own', '_EOL_', 'the', 'to', 'your', '_EOL_', \"It's\", 'just', 'for', 'a', 'couple', 'of', '_EOL_', 'you', 'this?', 'Why', 'are', 'you', 'come', 'us?', '_EOL_', 'Yeah,', \"I'm\", 'to', 'be', 'the', 'one', 'who', 'here,', 'waiting', 'to', 'up', 'the', 'Maybe', 'I', 'a', '_EOL_', \"I'm\", 'not', 'moving', \"It's\", 'just', 'a', 'couple', 'of', 'things.', '_EOL_', 'why', \"don't\", 'you', 'come', 'to', 'the', 'OK?', \"I'm\", 'gonna', 'be', 'working', 'but', 'we', 'can', 'have', 'a', 'few', 'a', 'Me', 'and', 'you.', '_EOL_', 'Yeah?', 'A', '_EOL_', '_EOL_', 'What', 'I', 'want', 'is', 'a', 'No', '_EOL_', 'some', 'over.', '_EOL_', 'Just', 'wanted', 'to', 'say', 'for', 'the', \"Jane's\", 'very', 'having', 'you', 'for', 'a', 'brother.', '_EOL_', 'I', 'was', 'on', 'the', 'phone.', \"I'm\", \"It's\", 'when', 'I', 'heard', 'the', '-', '_EOL_', 'He', 'told', 'me', '_EOL_', 'just', 'to', 'take', 'his', 'out', 'on', 'her', 'birthday', '-', '_EOL_', 'He', 'to', 'be', 'there', 'for', 'time', 'with', 'mean', \"you've\", 'got', 'to', 'give', 'him', 'credit', 'for', 'that', 'at', '-', '_EOL_', 'I', 'thought', 'she', 'to', '_EOL_', 'I', 'had', 'to', 'I', \"wasn't\", 'too', '_EOL_', 'Right.', '_EOL_', 'That', 'what', 'you', 'We', 'I', 'went', 'to', 'see', 'Well,', '_EOL_', 'Your', 'own', 'Just', 'town', 'as', 'it', '_EOL_', \"That's\", 'what', 'me', 'to', 'ask', 'you.', '_EOL_', 'was', 'this?', '_EOL_', 'It', 'was', 'on', '_EOL_', 'She', \"can't\", '_EOL_', \"I'm\", 'so', 'I', 'come', 'to', 'you', 'I', 'asked', 'you', 'straight', \"It's\", 'just', \"I've\", 'got', 'in', 'one', \"I've\", 'got', 'in', 'the', '_EOL_', 'I', 'I', \"won't\", '-', '_EOL_', 'Okay.', \"we'll\", 'go', 'out', 'yeah?', \"D'you\", 'wanna', 'go', 'out', 'and', 'get', 'something', 'to', '_EOL_', '-', '_EOL_', \"Don't\", 'worry,', \"you'll\", 'get', \"'em\", 'back.', '_EOL_', 'What', 'you', 'gonna', 'stop', 'me?', '_EOL_', 'Who', 'said', '_EOL_', 'Just', 'trying', 'to', 'sort', 'out', 'a', 'few', 'staff', 'to', 'find', 'a', 'for', 'OK?', '_EOL_', 'Hi.', '_EOL_', 'I', 'was', 'just', '-', '_EOL_', 'Hi', 'you', 'and', 'me', 'are', 'one', 'but', 'not', 'turning', 'up', 'at', 'the', 'last', \"that's\", 'business.', '_EOL_', 'What', 'are', 'you', 'doing', 'here?', '_EOL_', '_EOL_', 'I', 'just', 'need', 'to', 'talk', 'to', 'about', 'a', 'work', \"that's\", 'all.', '_EOL_', 'What', 'business', 'is', 'it', 'of', '_EOL_', 'Just', 'leave', 'it', '_EOL_', 'I', 'said,', 'leave', 'it', '-', '_EOL_', 'Well,', 'The', 'Mr', '_EOL_', 'We', 'were', 'last', 'night.', '_EOL_', '_EOL_', 'So', 'what', 'is', 'it?', 'The', 'that', 'this', 'to', 'talk', 'to', 'us', '_EOL_', 'What?', '_EOL_', '_EOL_', 'Yeah', 'why', \"don't\", 'you,', \"I've\", 'had', 'to', 'this', 'on', 'my', 'maybe', 'the', 'will', 'do', 'you', 'good', '-', '_EOL_', '_EOL_', 'That', 'if', 'the', 'are', 'right.', '_EOL_', '_EOL_', 'I', \"didn't\", 'read', 'any', '_EOL_', '_EOL_', 'Think', \"we'd\", 'like', 'if', 'we', '_EOL_', \"doesn't\", 'wear', 'that', 'much', '_EOL_', 'No', \"he's\", 'from', 'the', 'they', 'make', 'them', 'hard', 'up', 'there.', '_EOL_', 'No', 'no', 'around.', 'Is', 'this', 'a', '_EOL_', 'You', 'knew', 'about', 'this', '-', '_EOL_', 'You', 'had', 'to', '-', '_EOL_', 'business,', 'is', 'it?', '_EOL_', 'from', 'here.', '_EOL_', 'Do', 'I', 'want', '_EOL_', 'Well', 'I', 'like', 'to', 'keep', 'my', 'on', 'their', '_EOL_', 'You', 'know', 'I', 'would', 'have', 'If', \"you'd\", 'just', 'been', 'straight', 'with', 'me.', '_EOL_', 'No.', 'I', '_EOL_', 'Right,', 'I', 'should', 'be', 'getting', 'back.', 'the', '_EOL_', 'Yeah.', 'just', 'keep', 'it', '_EOL_', 'No', 'I', 'could', 'just', 'do', 'in', 'my', 'right', 'now.', 'You', \"don't\", 'do', 'you?', '_EOL_', \"I'll\", 'make', 'it', 'up', 'to', 'you', 'I', 'promise.', '_EOL_', 'Did', 'you', 'me?', '_EOL_', 'Thanks', 'very', '_EOL_', 'How', 'is', '_EOL_', \"Don't\", \"I'll\", 'things', 'over.', '_EOL_', 'Auntie', 'is', 'it', 'alright', 'if', 'I', 'have', 'a', 'quick', 'first.', \"I'll\", 'talk', 'to', 'her', 'later,', 'I', 'promise.', '_EOL_', 'Just', 'now.', '_EOL_', 'Just', 'for', 'a', 'few', 'I', 'wanted', 'to', 'my', 'head.', '_EOL_', 'Yeah.', '_EOL_', 'No.', '_EOL_', 'and', '_EOL_', \"It's\", 'I', '-', '_EOL_', '_EOL_', 'Where', 'you', 'going?', '_EOL_', 'Oh,', 'I', '-', '_EOL_', 'time', '_EOL_', 'I', 'I', 'just', 'a', 'in.', '_EOL_', 'I', 'said', 'a', 'in.', 'Now', 'go', 'on,', 'go', 'and', 'the', 'of', '_EOL_', \"It's\", 'very', 'But', \"I've\", 'gotta', 'go.', '_EOL_', 'I', 'just', 'wanna', 'be', 'back', 'before', 'up', 'and', 'about.', 'She', 'I', 'was', 'up', 'with', 'a', 'and', 'a', 'of', 'last', 'night.', '_EOL_', \"I'm\", 'not.', 'I', 'just', 'want', 'a', 'Look', 'I', 'really', 'love', 'my', 'but', 'with', 'her', 'does', 'my', 'head', 'in.', '_EOL_', 'That', 'an', '_EOL_', 'I', 'was', '_EOL_', \"It's\", 'not', 'the', 'right', 'time.', '_EOL_', 'it', 'what', 'you', 'want,', 'but', 'the', 'still', \"I've\", 'gotta', 'go.', \"I'll\", 'see', 'you', 'later.', '_EOL_', \"I'm\", 'not', 'you', 'am', 'I?', 'Still', 'doing', 'the', 'I', 'hope', \"you're\", 'gonna', 'this', 'lot', 'up.', '_EOL_', 'Have', 'you', 'my', 'pink', '_EOL_', 'Why', 'do', 'you', 'have', 'to', '_EOL_', 'Yes,', 'you.', '_EOL_', 'yes', 'but', \"it's\", 'my', 'pink', '_EOL_', 'Look', \"it's\", 'not', 'really', 'about', 'the', '_EOL_']\n"
     ]
    }
   ],
   "source": [
    "# For Analysing the similarity results, Question no. 3\n",
    "\n",
    "jack_lines = (train_character_docs['JACK'])\n",
    "phil_lines = (train_character_docs['PHIL'])\n",
    "confused_words = []\n",
    "\n",
    "for i in jack_lines.split():\n",
    "    if i in phil_lines.split():\n",
    "        confused_words.append(i)\n",
    "print(confused_words)\n",
    "        \n",
    "ronnie_lines = (train_character_docs['RONNIE'])\n",
    "ian_lines = (train_character_docs['IAN'])\n",
    "similar_words = []\n",
    "\n",
    "\n",
    "for i in ronnie_lines.split():\n",
    "    if i in ian_lines.split():\n",
    "        similar_words.append(i)\n",
    "print(similar_words)\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of confused words between Jack and Phil: 3338\n",
      "Length of confused words between Ronnie and Ian: 2578\n"
     ]
    }
   ],
   "source": [
    "print(f\"Length of confused words between Jack and Phil: {len(confused_words)}\")\n",
    "\n",
    "print(f\"Length of confused words between Ronnie and Ian: {len(similar_words)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q1. Improve pre-processing (20 marks)\n",
    "Using the pre-processing techniques you have learned in the module, improve the `pre_process` function above, which currently just tokenizes text based on white space.\n",
    "\n",
    "When developing, use the 90% train and 10% validation data split from the training file, using the first 360 lines from the training split and first 40 lines from the validation split, as per above. To check the improvements by using the different techniques, use the `compute_IR_evaluation_scores` function as above. The **mean rank** is the main metric you need to focus on improving throughout this assignment, where the target/best possible performance is **1** (i.e. all test/validation data character documents are closest to their corresponding training data character documents) and the worst is **16**. Initially the code in this template achieves a mean rank of **5.12**  and accuracy of **0.3125** on the test set and a mean rank of **4.5** and accuracy of **0.25** on the validation set - you should be looking to improve those, particularly getting the mean rank as close to 1 as possible.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q2. Improve linguistic feature extraction (20 marks)\n",
    "Use the feature extraction techniques you have learned to improve the `to_feature_vector_dictionary` function above. Examples of extra features could include extracting n-grams of different lengths and including POS-tags. You could also use sentiment analysis or another text classifier's result when applied to the features for each character document. You could even use a gender classifier trained on the same data using the GENDER column **(but DO NOT USE the GENDER column directly in the features for the final vector)**.\n",
    "\n",
    "You could use feature selection/reduction with techniques like minimum/maximum document frequency and/or feature selection like k-best selection using different statistical tests https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.SelectKBest.html. Again, develop on 90% training and 10% validation split and note the effect/improvement in mean rank with the techniques you use."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q3. Analyse the similarity results (10 marks)\n",
    "From your system so far run on the 90%/10% training/validation split, identify the heldout character vectors ranked closest to each character's training vector which are not the character themselves, and those furthest away, as displayed using the `plot_heat_map_similarity` function. In your report, try to ascribe reasons why this is the case, particularly for those where there isn't a successful highest match between the target character in the training set and that character's vector in the heldout set yet. Observations you could make include how their language use is similar, resulting in similar word or ngram features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q4. Add dialogue context and scene features (20 marks)\n",
    "Adjust `create_character_document_from_dataframe` and the other functions appropriately so the data incorporates the context of the line spoken by the characters in terms of the lines spoken by other characters in the same scene (before and after the target character's lines). HINT: you should use the *Episode* and *Scene* columns to check which characters are in the same scene to decide whether to include their lines or not. You can also use **scene_info** column to extract information about the scene **(but DO NOT USE the GENDER and CHARACTER columns directly)**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q5. Improve the vectorization method (20 marks)\n",
    "Use a matrix transformation technique like TF-IDF (https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfTransformer.html) to improve the `create_document_matrix_from_corpus` function, which currently only uses a dictionary vectorizor (`DictVectorizer`) which straight-forwardly maps from the feature dictionaries produced for each character document to a sparse matrix.\n",
    "\n",
    "As the `create_document_matrix_from_corpus` is designed to be used both in training/fitting (with `fitting` set to `True`) and in transformation alone on test/validation data (with `fitting` set to `False`), make sure you initialize any transformers you want to try in the same place as `corpusVectorizer = DictVectorizer()` before you call \n",
    "`create_document_matrix_from_corpus`. Again, develop on 90% training 10% validation split and note the effect/improvement in mean rank with each technique you try."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q6. Run on final test data  (10 marks)\n",
    "Test your best system using the code below to train on all of the training data (using the first 400 lines per character maximum) and do the final testing on the test file (using the first 40 lines per character maximum).\n",
    "\n",
    "Make any neccessary adjustments such that it runs in the same way as the training/testing regime you developed above- e.g. making sure any transformer objects are initialized before `create_document_matrix_from_corpus` is called. Make sure your best system is left in the notebook and it is clear what the mean rank, accuracy of document selection are on the test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# redo on all training data with the first 400 character lines used\n",
    "train_character_docs = create_character_document_from_dataframe(all_train_data, max_line_count=400)\n",
    "print('Num. Characters: ',len(train_character_docs.keys()),\"\\n\")\n",
    "total_words = 0\n",
    "for name in train_character_docs.keys():\n",
    "    print(name, 'Number of Words: ',len(train_character_docs[name].split()))\n",
    "    total_words += len(train_character_docs[name].split())\n",
    "print(\"total words\", total_words)\n",
    "\n",
    "training_corpus = [(name, pre_process(doc)) for name, doc in train_character_docs.items()]\n",
    "train_labels = [name for name, doc in training_corpus]\n",
    "\n",
    "corpusVectorizer = DictVectorizer()   # initialize a corpusVectorizor which will output sparse vectors from dicts\n",
    "# Any matrix transformers (e.g. tf-idf transformers) should be initialized here\n",
    "\n",
    "\n",
    "training_feature_matrix = create_document_matrix_from_corpus(training_corpus, fitting=True)\n",
    "\n",
    "# get the test data using 40 lines per character\n",
    "test_character_docs = create_character_document_from_dataframe(test_data, max_line_count=40)\n",
    "print('Num. Characters: ',len(test_character_docs.keys()),\"\\n\")\n",
    "total_words = 0\n",
    "for name in test_character_docs.keys():\n",
    "    print(name, 'Number of Words: ',len(test_character_docs[name].split()))\n",
    "    total_words += len(test_character_docs[name].split())\n",
    "print(\"total words\", total_words)\n",
    "\n",
    "# create list of pairs of (character name, pre-processed character) \n",
    "test_corpus = [(name, pre_process(doc)) for name, doc in test_character_docs.items()]\n",
    "test_labels = [name for name, doc in test_corpus]\n",
    "\n",
    "\n",
    "# Just transform the val_feature_matrix, don't fit\n",
    "test_feature_matrix = create_document_matrix_from_corpus(test_corpus, fitting=False)\n",
    "\n",
    "\n",
    "mean_rank, mean_cosine_simliarity, acc, df = compute_IR_evaluation_scores(training_feature_matrix, test_feature_matrix, train_labels, test_labels)"
   ]
  }
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