Solutions_Q4-Q6.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": 1,
   "metadata": {
    "scrolled": true
   },
   "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)"
     ]
    },
    {
     "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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\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: click in c:\\users\\pranj\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from nltk) (8.1.3)\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: joblib in c:\\users\\pranj\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from nltk) (1.2.0)\n",
      "Requirement already satisfied: colorama in c:\\users\\pranj\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from click->nltk) (0.4.5)\n"
     ]
    }
   ],
   "source": [
    "!pip install nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "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"
     ]
    },
    {
     "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: 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: charset-normalizer<3,>=2 in c:\\users\\pranj\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from requests->vaderSentiment) (2.1.1)\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: certifi>=2017.4.17 in c:\\users\\pranj\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from requests->vaderSentiment) (2022.9.24)\n"
     ]
    }
   ],
   "source": [
    "!pip install vaderSentiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "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": 180,
   "metadata": {},
   "outputs": [
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       "      <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",
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       "      <td>Shirl...</td>\n",
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       "      <td>RONNIE</td>\n",
       "      <td>Nothing. Nothing. I'll be with you in two minutes yeah?</td>\n",
       "      <td>FEMALE</td>\n",
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       "      <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",
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       "      <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",
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       "      <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",
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       "<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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       "      <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",
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       "      <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",
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       "      <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",
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       "    <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",
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      "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]"
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    }
   ],
   "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": 181,
   "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": 182,
   "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": 137,
   "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": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n# Create one document per character\\ndef 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    \\n    df[\\'prev\\'] = df.Scene.eq(df.Scene.shift(1))\\n    df[\\'next\\'] = df.Scene.eq(df.Scene.shift(-1))\\n    df[\\'prev_line\\']=(df[\\'Line\\'].shift(1))\\n    df[\\'next_line\\'] = df[\\'Line\\'].shift(-1)\\n    \\n    display(df)\\n\\n    display(df)\\n    for line, name, gender, prev, nxt, prev_line, next_line in zip(df.Line, df.Character_name, df.Gender, df.prev, df.next, df.prev_line, df.next_line):\\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        if nxt == True:\\n            next_line=(str(next_line))\\n            character_docs[name] += str(next_line)   + \" _NEX_ \"\\n        if prev == True:\\n            prev_line=(str(prev_line))\\n            character_docs[name] += str(prev_line)   + \" _PRV_ \"\\n    print(\"lines per character\", character_line_count)\\n    return character_docs\\n'"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "This code did improved the accuracy, but we are getting better accuracy by not using this and only using TF-IDF transformers.\n",
    "Hence this code is commented. But the Mean Rank values by running this code is printed at the end of the code. PFB.\n",
    "\"\"\"\n",
    "\n",
    "'''\n",
    "# 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",
    "    We are then adding the previous and next lines by comaring the episode scene number of all character lines.\n",
    "    \n",
    "    ::max_line_count:: the maximum number of lines to be added per character\n",
    "    \"\"\"\n",
    "    character_docs = {}\n",
    "    character_line_count = {}\n",
    "    \n",
    "    df['prev'] = df.Scene.eq(df.Scene.shift(1))\n",
    "    df['next'] = df.Scene.eq(df.Scene.shift(-1))\n",
    "    df['prev_line']=(df['Line'].shift(1))\n",
    "    df['next_line'] = df['Line'].shift(-1)\n",
    "    \n",
    "    display(df)\n",
    "\n",
    "    display(df)\n",
    "    for line, name, gender, prev, nxt, prev_line, next_line in zip(df.Line, df.Character_name, df.Gender, df.prev, df.next, df.prev_line, df.next_line):\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",
    "        if nxt == True:\n",
    "            next_line=(str(next_line))\n",
    "            character_docs[name] += str(next_line)   + \" _NEX_ \"\n",
    "        if prev == True:\n",
    "            prev_line=(str(prev_line))\n",
    "            character_docs[name] += str(prev_line)   + \" _PRV_ \"\n",
    "    print(\"lines per character\", character_line_count)\n",
    "    return character_docs\n",
    "'''\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "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": 184,
   "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": 185,
   "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": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "def using_regex(text):\n",
    "    \"\"\"\n",
    "    This method uses a Regex to separates punctuation at beginning and ends of strings. \n",
    "    After that, text is converted to list of words with each word converted to lower case.\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": 187,
   "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",
    "    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": 188,
   "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": 189,
   "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",
    "    \"\"\"\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": 190,
   "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": 191,
   "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",
    "    \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\n",
    "\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",
    "\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": 192,
   "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": 192,
     "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",
    "Tfidf_Transformer=TfidfTransformer(norm = 'l2', smooth_idf = False, sublinear_tf = True, use_idf = True)\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",
    "    if fitting:\n",
    "        Tfidf_Transformer.fit(doc_feature_matrix)\n",
    "    doc_feature_matrix= Tfidf_Transformer.transform(doc_feature_matrix)\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": 193,
   "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": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_feature_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "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": 195,
   "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": 196,
   "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": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_feature_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "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": 198,
   "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": 199,
   "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": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean rank 2.5\n",
      "mean cosine similarity 0.8448197687122945\n",
      "8 correct out of 16 / accuracy: 0.5\n",
      "Mean Rank and Cosine Similarity after adding dialogue context and scene : (2.5, 0.8448197687122945)\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 Cosine Similarity after adding dialogue context and scene : {mean_rank, mean_cosine_simliarity}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean rank 1.4375\n",
      "mean cosine similarity 0.25346060320263836\n",
      "10 correct out of 16 / accuracy: 0.625\n",
      "Mean Rank and Cosine Similarity after adding dialogue context and scene features and TF-IDF is : (1.4375, 0.25346060320263836)\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 Cosine Similarity after adding dialogue context and scene features and TF-IDF is : {mean_rank, mean_cosine_simliarity}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean rank 1.25\n",
      "mean cosine similarity 0.23572305188381898\n",
      "13 correct out of 16 / accuracy: 0.8125\n",
      "Mean Rank and Cosine Similarity after only adding tf-idf is : (1.25, 0.23572305188381898)\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 Cosine Similarity after only adding tf-idf is : {mean_rank, mean_cosine_simliarity}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "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": "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": 202,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lines per character {'SHIRLEY': 400, 'OTHER': 400, 'JACK': 400, 'RONNIE': 400, 'TANYA': 400, 'SEAN': 400, 'ROXY': 400, 'HEATHER': 400, 'MAX': 400, 'IAN': 400, 'JANE': 400, 'STACEY': 400, 'PHIL': 400, 'MINTY': 400, 'CHRISTIAN': 385, 'CLARE': 400}\n",
      "Num. Characters:  16 \n",
      "\n",
      "SHIRLEY Number of Words:  4233\n",
      "OTHER Number of Words:  3606\n",
      "JACK Number of Words:  4786\n",
      "RONNIE Number of Words:  3872\n",
      "TANYA Number of Words:  4214\n",
      "SEAN Number of Words:  4026\n",
      "ROXY Number of Words:  4200\n",
      "HEATHER Number of Words:  4504\n",
      "MAX Number of Words:  5107\n",
      "IAN Number of Words:  4863\n",
      "JANE Number of Words:  4117\n",
      "STACEY Number of Words:  4325\n",
      "PHIL Number of Words:  4103\n",
      "MINTY Number of Words:  4391\n",
      "CHRISTIAN Number of Words:  4250\n",
      "CLARE Number of Words:  4844\n",
      "total words 69441\n",
      "lines per character {'SHIRLEY': 40, 'OTHER': 40, 'HEATHER': 40, 'PHIL': 40, 'SEAN': 40, 'TANYA': 40, 'MAX': 40, 'JACK': 40, 'IAN': 40, 'JANE': 40, 'STACEY': 40, 'ROXY': 40, 'RONNIE': 40, 'CHRISTIAN': 40, 'MINTY': 40, 'CLARE': 40}\n",
      "Num. Characters:  16 \n",
      "\n",
      "SHIRLEY Number of Words:  373\n",
      "OTHER Number of Words:  453\n",
      "HEATHER Number of Words:  451\n",
      "PHIL Number of Words:  406\n",
      "SEAN Number of Words:  466\n",
      "TANYA Number of Words:  465\n",
      "MAX Number of Words:  494\n",
      "JACK Number of Words:  412\n",
      "IAN Number of Words:  509\n",
      "JANE Number of Words:  414\n",
      "STACEY Number of Words:  634\n",
      "ROXY Number of Words:  392\n",
      "RONNIE Number of Words:  390\n",
      "CHRISTIAN Number of Words:  629\n",
      "MINTY Number of Words:  428\n",
      "CLARE Number of Words:  368\n",
      "total words 7284\n",
      "mean rank 1.0625\n",
      "mean cosine similarity 0.24764488114775712\n",
      "15 correct out of 16 / accuracy: 0.9375\n"
     ]
    }
   ],
   "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",
    "Tfidf_Transformer=TfidfTransformer(norm = 'l2', smooth_idf = False, sublinear_tf = True, use_idf = True)\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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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