polarity_predictor.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sentiment Polarity Prediction with Naive Bayes\n",
    "\n",
    "This notebook contains a basic implementation of document-level sentiment analysis\n",
    "for movie reviews with multinomial Naive Bayes and bag-of-words features\n",
    "and of cross-validation.\n",
    "* No special treatment of rare or unknown words. Unknown words in the test data are skipped.\n",
    "\n",
    "We use the movie review polarity data set of Pang and Lee 2004 [A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts](https://www.aclweb.org/anthology/P04-1035/) in Version 2.0 available from http://www.cs.cornell.edu/People/pabo/movie-review-data (section \"Sentiment polarity datasets\"). This dataset contains 1000 positive and 1000 negative reviews, each tokenised, sentence-split (one sentence per line) and lowercased. Each review has been assigned to 1 of 10 cross-validation folds by the authors and this setup should be followed to compare with published results.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
      "[nltk_data]     /Users/ivan/nltk_data...\n",
      "[nltk_data]   Package averaged_perceptron_tagger is already up-to-\n",
      "[nltk_data]       date!\n",
      "[nltk_data] Downloading package stopwords to /Users/ivan/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package wordnet to /Users/ivan/nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import tarfile\n",
    "import time\n",
    "import urllib.request\n",
    "import numpy\n",
    "import pandas as pd\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn import metrics\n",
    "import nltk\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import clear_output\n",
    "\n",
    "nltk.download('averaged_perceptron_tagger')\n",
    "nltk.download('stopwords')\n",
    "nltk.download('wordnet')\n",
    "\n",
    "data_source = 'local-folder'\n",
    "data_folder = os.path.join('data', 'txt_sentoken')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class PL04DataLoader_Part_1:\n",
    "    \n",
    "    def __init__(self):\n",
    "        pass\n",
    "    \n",
    "    def get_labelled_dataset(self, fold = 0):\n",
    "        ''' Compile a fold of the data set\n",
    "        '''\n",
    "        dataset = []\n",
    "        for label in ('pos', 'neg'):\n",
    "            for document in self.get_documents(\n",
    "                fold = fold,\n",
    "                label = label,\n",
    "            ):\n",
    "                dataset.append((document, label))\n",
    "        return dataset\n",
    "    \n",
    "    def get_documents(self, fold = 0, label = 'pos'):\n",
    "        ''' Enumerate the raw contents of all data set files.\n",
    "            Args:\n",
    "                data_dir: relative or absolute path to the data set folder\n",
    "                fold: which fold to load (0 to n_folds-1)\n",
    "                label: 'pos' or 'neg' to\n",
    "                    select data with positive or negative sentiment\n",
    "                    polarity\n",
    "            Return:\n",
    "                List of tokenised documents, each a list of sentences\n",
    "                that in turn are lists of tokens\n",
    "        '''\n",
    "        raise NotImplementedError"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class PL04DataLoader(PL04DataLoader_Part_1):\n",
    "    \n",
    "    def get_xval_splits(self):\n",
    "        ''' Split data with labels for cross-validation\n",
    "            returns a list of k pairs (training_data, test_data)\n",
    "            for k cross-validation\n",
    "        '''\n",
    "        # load the folds\n",
    "        folds = []\n",
    "        for i in range(10):\n",
    "            folds.append(self.get_labelled_dataset(\n",
    "                fold = i\n",
    "            ))\n",
    "        # create training-test splits\n",
    "        retval = []\n",
    "        for i in range(10):\n",
    "            test_data = folds[i]\n",
    "            training_data = []\n",
    "            for j in range(9):\n",
    "                ij1 = (i+j+1) % 10\n",
    "                assert ij1 != i\n",
    "                training_data = training_data + folds[ij1]\n",
    "            retval.append((training_data, test_data))\n",
    "        return retval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class PL04DataLoaderFromStream(PL04DataLoader):\n",
    "        \n",
    "    def __init__(self, tgz_stream, **kwargs):\n",
    "        super().__init__(**kwargs)\n",
    "        self.data = {}\n",
    "        counter = 0\n",
    "        with tarfile.open(\n",
    "            mode = 'r|gz',\n",
    "            fileobj = tgz_stream\n",
    "        ) as tar_archive:\n",
    "            for tar_member in tar_archive:\n",
    "                if counter == 2000:\n",
    "                    break\n",
    "                path_components = tar_member.name.split('/')\n",
    "                filename = path_components[-1]\n",
    "                if filename.startswith('cv') \\\n",
    "                and filename.endswith('.txt') \\\n",
    "                and '_' in filename:\n",
    "                    label = path_components[-2]\n",
    "                    fold = int(filename[2])\n",
    "                    key = (fold, label)\n",
    "                    if key not in self.data:\n",
    "                        self.data[key] = []\n",
    "                    f = tar_archive.extractfile(tar_member)\n",
    "                    document = [\n",
    "                        line.decode('utf-8').split()\n",
    "                        for line in f.readlines()\n",
    "                    ]\n",
    "                    self.data[key].append(document)\n",
    "                    counter += 1\n",
    "            \n",
    "    def get_documents(self, fold = 0, label = 'pos'):\n",
    "        return self.data[(fold, label)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read Data from the Web\n",
    "This should run efficiently both on google colab and locally but has the disadvantage that the same data is downloaded each time the notebook is run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class PL04DataLoaderFromURL(PL04DataLoaderFromStream):\n",
    "    \n",
    "    def __init__(self, data_url, **kwargs):\n",
    "        with urllib.request.urlopen(data_url) as tgz_stream:\n",
    "            super().__init__(tgz_stream, **kwargs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read Data from a Local .tgz File\n",
    "\n",
    "You manually download the .tgz once to a filesystem that can be accessed from the notebook, e.g. google drive on colab, and this notebook reads this file in one chunk. \n",
    "\n",
    "Note that if you are accessing files from google drive on colab, you will need to mount your drive and enter an authentication token:\n",
    "\n",
    "```\n",
    "from google.colab import drive\n",
    "drive.mount('/content/drive')\n",
    "```\n",
    "\n",
    "You will also have to change your *data_tgz* or *data_folder* paths above so that they start with *'/content/drive/My Drive/'*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class PL04DataLoaderFromTGZ(PL04DataLoaderFromStream):\n",
    "    \n",
    "    def __init__(self, data_path, **kwargs):\n",
    "        with open(data_path, 'rb') as tgz_stream:\n",
    "            super().__init__(tgz_stream, **kwargs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read Data from a Local Folder\n",
    "\n",
    "Extract the .tgz to a local folder and only load the required files. This is usually the fastest option when storage is on a local SSD. On remote filesystems, however, this can be very slow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "class PL04DataLoaderFromFolder(PL04DataLoader):\n",
    "    \n",
    "    def __init__(self, data_dir, **kwargs):\n",
    "        self.data_dir = data_dir\n",
    "        super().__init__(**kwargs)\n",
    "        \n",
    "    def get_documents(self, fold = 0, label = 'pos'):\n",
    "        # read folder contents\n",
    "        path = os.path.join(self.data_dir, label)\n",
    "        dir_entries = os.listdir(path)\n",
    "        # must process entries in numeric order to\n",
    "        # replicate order of original experiments\n",
    "        dir_entries.sort()\n",
    "        # check each entry and add to data if matching\n",
    "        # selection criteria\n",
    "        for filename in dir_entries:\n",
    "            if filename.startswith('cv') \\\n",
    "            and filename.endswith('.txt'):\n",
    "                if fold == int(filename[2]):\n",
    "                    # correct fold\n",
    "                    f = open(os.path.join(path, filename), 'rt')\n",
    "                    # \"yield\" tells Python to return an iterator\n",
    "                    # object that produces the yields of this\n",
    "                    # function as elements without creating a\n",
    "                    # full list of all elements\n",
    "                    yield [line.split() for line in f.readlines()]\n",
    "                    f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "if data_source == 'local-folder':\n",
    "    data_loader = PL04DataLoaderFromFolder(data_folder)\n",
    "elif data_source == 'local-tgz':\n",
    "    data_loader = PL04DataLoaderFromTGZ(data_tgz)\n",
    "elif data_source == 'web':\n",
    "    data_loader = PL04DataLoaderFromURL(data_url)\n",
    "else:\n",
    "    raise ValueError('Unsupported data source %r' %data_source)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "== pos ==\n",
      "doc sentences start of first sentence\n",
      "  0      25   films|adapted|from|comic|books|have|had|plenty|of|success|,|whether\n",
      "  1      39   every|now|and|then|a|movie|comes|along|from|a|suspect|studio|,|with\n",
      "  2      19   you've|got|mail|works|alot|better|than|it|deserves|to|.|in|order|to|make\n",
      "  3      42   \"|jaws|\"|is|a|rare|film|that|grabs|your|attention|before|it|shows|you|a\n",
      "  4      25   moviemaking|is|a|lot|like|being|the|general|manager|of|an|nfl|team|in\n",
      "== neg ==\n",
      "doc sentences start of first sentence\n",
      "  0      35   plot|:|two|teen|couples|go|to|a|church|party|,|drink|and|then|drive|.\n",
      "  1      13   the|happy|bastard's|quick|movie|review|damn|that|y2k|bug|.|it's|got|a\n",
      "  2      23   it|is|movies|like|these|that|make|a|jaded|movie|viewer|thankful|for|the\n",
      "  3      19   \"|quest|for|camelot|\"|is|warner|bros|.|'|first|feature-length|,\n",
      "  4      37   synopsis|:|a|mentally|unstable|man|undergoing|psychotherapy|saves|a|boy\n"
     ]
    }
   ],
   "source": [
    "def get_document_preview(document, max_length = 72):\n",
    "    s = []\n",
    "    count = 0\n",
    "    reached_limit = False\n",
    "    for sentence in document:\n",
    "        for token in sentence:\n",
    "            if count + len(token) + len(s) > max_length:\n",
    "                reached_limit = True\n",
    "                break\n",
    "            s.append(token)\n",
    "            count += len(token)\n",
    "        if reached_limit:\n",
    "            break\n",
    "    return '|'.join(s)\n",
    "    \n",
    "for label in 'pos neg'.split():\n",
    "    print(f'== {label} ==')\n",
    "    print('doc sentences start of first sentence')\n",
    "    for index, document in enumerate(data_loader.get_documents(\n",
    "        label = label\n",
    "    )):\n",
    "        print('%3d %7d   %s' %(\n",
    "            index, len(document), get_document_preview(document)\n",
    "        ))\n",
    "        if index == 4:\n",
    "            break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Training-Test Splits for Cross-Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tr-size te-size (number of documents)\n",
      "   1800     200\n",
      "   1800     200\n",
      "   1800     200\n",
      "   1800     200\n",
      "   1800     200\n",
      "   1800     200\n",
      "   1800     200\n",
      "   1800     200\n",
      "   1800     200\n",
      "   1800     200\n"
     ]
    }
   ],
   "source": [
    "splits = data_loader.get_xval_splits()\n",
    "\n",
    "print('tr-size te-size (number of documents)')\n",
    "for xval_tr_data, xval_te_data in splits:\n",
    "    print('%7d %7d' %(len(xval_tr_data), len(xval_te_data)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Interface for Sentiment Polarity Predictor\n",
    "Let's define a base class to clarify how we plan to use polarity predictors. Its functions will have to be implemented in sub-classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "class PolarityPredictorInterface:\n",
    "\n",
    "    def train(self, data_with_labels):\n",
    "        raise NotImplementedError\n",
    "        \n",
    "    def predict(self, data):\n",
    "        raise NotImplementedError"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "class PolarityPredictorInit(PolarityPredictorInterface):\n",
    "    \n",
    "    def train(self, data_with_labels, feature):\n",
    "        \"\"\"\n",
    "        Function which trains model.\n",
    "        \n",
    "        Lemmatisation, negation and removal of stopwords are performed on the data first.\n",
    "        \n",
    "        Then the feature matrix is created using extract_features function.\n",
    "        \n",
    "        This will be based on bag of unigrams, bigrams or trigrams depending on input.\n",
    "        \n",
    "        The targets are then fetched and passed to the learning model for training.\n",
    "        \"\"\"\n",
    "\n",
    "        # Initialise ngram set objects\n",
    "        self.reset_feature_sets()\n",
    "\n",
    "        if self.lemmatise:\n",
    "            \"\"\"\n",
    "            Get Penn POS tags\n",
    "            Convert to WordNet POS tags and retrieve lemma\n",
    "            Reset data\n",
    "            \"\"\"\n",
    "            tagged_data = self.get_pos_tags(data_with_labels)\n",
    "            lemmatised_data = self.lemmatise_data(tagged_data)\n",
    "            data_with_labels = lemmatised_data\n",
    "        \n",
    "        if self.negation:\n",
    "            \"\"\"\n",
    "            'NOT_' is added to tokens which are negated.\n",
    "            \"\"\"\n",
    "            self.add_negation_to_data(data_with_labels)\n",
    "            \n",
    "        if self.remove_stopwords:\n",
    "            \"\"\"\n",
    "            Stopwords are retrieved from NLTK library and are removed if found in the data.\n",
    "            \"\"\"\n",
    "            self.get_stopwords()\n",
    "            self.remove_stopwords_from_data(data_with_labels)\n",
    "            \n",
    "            \n",
    "        self.add_to_feature_sets_from_data(data_with_labels)\n",
    "        self.finalise_feature_sets()\n",
    "        \n",
    "        tr_features = self.extract_features(data_with_labels, feature)\n",
    "        \n",
    "        if self.additional_features:\n",
    "            \"\"\"\n",
    "            Additional features: (for each document)\n",
    "            Positive sentiment lexicon counts \n",
    "            Negative sentiment lexicon counts\n",
    "            Negated word counts\n",
    "            Added as columns to existing feature matrix\n",
    "            \"\"\"\n",
    "            self.get_sentiment_lexicons()\n",
    "            tr_features = self.add_additional_features(data_with_labels, tr_features, self.negation)\n",
    "            \n",
    "        tr_targets = self.get_targets(data_with_labels)\n",
    "        self.train_model_on_features(tr_features, tr_targets)\n",
    "\n",
    "    def reset_feature_sets(self):\n",
    "        self.unigrams = set()\n",
    "        self.bigrams = set()\n",
    "        self.trigrams = set()\n",
    "        self.doc2negate = None\n",
    "        self.stopwords = None\n",
    "        self.unigram2index = None\n",
    "        self.bigram2index = None\n",
    "        self.trigram2index = None\n",
    "        self.positive_lexicons = None\n",
    "        self.negative_lexicons = None\n",
    "        self.doc2poslex = None\n",
    "        self.doc2neglex = None\n",
    "    \n",
    "    def get_pos_tags(self, data):\n",
    "        tagged_data = []\n",
    "        for document, label in data:\n",
    "            new_document = []\n",
    "            for sentence in document:\n",
    "                tagged_sentence = nltk.pos_tag(sentence)\n",
    "                new_document.append(tagged_sentence)\n",
    "            new_entry = (new_document, label)\n",
    "            tagged_data.append(new_entry)\n",
    "        return tagged_data\n",
    "\n",
    "    def lemmatise_data(self, data):\n",
    "        \"\"\"\n",
    "        POS has been retieved as in Penn form\n",
    "        We convert to WordNet form by taking first letter of Penn POS\n",
    "        \"\"\"\n",
    "        lemmatizer = nltk.stem.WordNetLemmatizer()\n",
    "        for document, label in data:\n",
    "            for sentence in document:\n",
    "                for index, token in enumerate(sentence):\n",
    "                    word = token[0]\n",
    "                    penn_pos = token[1]\n",
    "                    if penn_pos[0] == 'N':\n",
    "                        wn_pos = nltk.corpus.wordnet.NOUN\n",
    "                    elif penn_pos[0] == 'J':\n",
    "                        wn_pos = nltk.corpus.wordnet.ADJ\n",
    "                    elif penn_pos[0] == 'V':\n",
    "                        wn_pos = nltk.corpus.wordnet.VERB\n",
    "                    elif penn_pos[0] == 'R':\n",
    "                        wn_pos = nltk.corpus.wordnet.ADV\n",
    "                    else:\n",
    "                        sentence[index] = lemmatizer.lemmatize(word)\n",
    "                        continue\n",
    "                    sentence[index] = lemmatizer.lemmatize(word, wn_pos)\n",
    "        return data\n",
    "    \n",
    "    def add_negation_to_data(self, data):\n",
    "        \"\"\"\n",
    "        if 'no' or 'not' or \"...n't\" are found, \n",
    "        words are negated until end of sentence.\n",
    "        \"\"\"\n",
    "        self.doc2negate = []\n",
    "        for document, label in data:\n",
    "            negate_total = 0\n",
    "            for sentence in document:\n",
    "                negate = False\n",
    "                for index, token in enumerate(sentence):\n",
    "                    if token in ('not', 'no') or (token[-3:] == \"n't\"):\n",
    "                        negate = True\n",
    "                        continue\n",
    "                    if token == '.':\n",
    "                        negate = False\n",
    "                    if negate:\n",
    "                        sentence[index] = 'NOT_' + token\n",
    "                        negate_total += 1\n",
    "            self.doc2negate.append(negate_total)\n",
    "    \n",
    "    def remove_stopwords_from_data(self, data):\n",
    "        for document, label in data:\n",
    "            for sentence in document:\n",
    "                stopword_indices = []\n",
    "                for index, token in enumerate(sentence):\n",
    "                    if token in self.stopwords:\n",
    "                        stopword_indices.append(index)\n",
    "                stopword_indices.reverse()\n",
    "                # we delete stopwords in place, by deleting the latest index first.\n",
    "                for index in stopword_indices:\n",
    "                    del sentence[index]\n",
    "    \n",
    "    def get_stopwords(self):\n",
    "        self.stopwords = set(nltk.corpus.stopwords.words('english'))\n",
    "    \n",
    "    def get_sentiment_lexicons(self):\n",
    "        \"\"\"\n",
    "        We form lists of positive and negative sentiment lexicons.\n",
    "        We used data from MPQA: http://mpqa.cs.pitt.edu/lexicons/subj_lexicon/\n",
    "        \"\"\"\n",
    "        sentiment_lexicon_data = \"data/sentiment_lexicons.tff\"\n",
    "    \n",
    "        self.positive_lexicons = []\n",
    "        self.negative_lexicons = []\n",
    "        with open(sentiment_lexicon_data, 'r') as f:\n",
    "            while len(f.readline()) != 0:\n",
    "                toks = f.readline().split()\n",
    "                word = toks[2].split(\"=\")[1]\n",
    "                polarity = toks[-1].split(\"=\")[1]\n",
    "                if polarity in (\"positive\", \"negative\"):\n",
    "                    if polarity == 'positive':\n",
    "                        self.positive_lexicons.append(word)\n",
    "                    if polarity == 'negative':\n",
    "                        self.negative_lexicons.append(word)\n",
    "        \n",
    "    def add_to_feature_sets_from_data(self, data):\n",
    "        \"\"\"\n",
    "        Parses tokens in data and adds them to each feature set.\n",
    "        \"\"\"\n",
    "        for document, label in data:\n",
    "            for sentence in document:\n",
    "                sentence.insert(0, '<s>')\n",
    "                sentence.append('</s>')\n",
    "                prev_token = None\n",
    "                for index, token in enumerate(sentence):\n",
    "                    self.unigrams.add(token)\n",
    "                    if index > 0:\n",
    "                        bigram = (prev_token, token)\n",
    "                        self.bigrams.add(bigram)\n",
    "                    if index > 1:\n",
    "                        trigram = (prev_prev_token, prev_token, token)\n",
    "                        self.trigrams.add(trigram)\n",
    "                    prev_prev_token = prev_token\n",
    "                    prev_token = token\n",
    "                        \n",
    "    def finalise_feature_sets(self):\n",
    "        \"\"\"\n",
    "        Creates a dict for the feature sets for faster operations.\n",
    "        \"\"\"\n",
    "        self.unigrams = list(self.unigrams)\n",
    "        # create reverse map for fast token lookup\n",
    "        self.unigram2index = {}\n",
    "        for index, token in enumerate(self.unigrams):\n",
    "            self.unigram2index[token] = index\n",
    "            \n",
    "        self.bigrams = list(self.bigrams)\n",
    "        # create reverse map for fast token lookup\n",
    "        self.bigram2index = {}\n",
    "        for index, token in enumerate(self.bigrams):\n",
    "            self.bigram2index[token] = index\n",
    "            \n",
    "        self.trigrams = list(self.trigrams)\n",
    "        # create reverse map for fast token lookup\n",
    "        self.trigram2index = {}\n",
    "        for index, token in enumerate(self.trigrams):\n",
    "            self.trigram2index[token] = index\n",
    "        \n",
    "        \n",
    "    def extract_features(self, data, feature):\n",
    "        raise NotImplementedError\n",
    "    \n",
    "    def get_targets(self, data, label2index = None):\n",
    "        raise NotImplementedError\n",
    "        \n",
    "    def train_model_on_features(self, tr_features, tr_targets):\n",
    "        raise NotImplementedError"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "class PolarityPredictorExtractFeatures(PolarityPredictorInit):\n",
    "    \n",
    "    def __init__(self, clip_counts = True, negation=False, remove_stopwords=False, lemmatise=False, additional_features=False, learning_model=MultinomialNB()):\n",
    "        self.clip_counts = clip_counts\n",
    "        self.negation = negation\n",
    "        self.remove_stopwords = remove_stopwords\n",
    "        self.model = learning_model\n",
    "        self.lemmatise = lemmatise\n",
    "        self.additional_features = additional_features\n",
    "        \n",
    "    def extract_features(self, data, ngram):\n",
    "        \"\"\"\n",
    "        Creates features from the data.\n",
    "        Depending on the input, a feature matrix for bag of words, bag of bigrams or bag of trigrams is created.\n",
    "        \"\"\"\n",
    "        rows = len(data)\n",
    "        feature_matrices = {}\n",
    "        # Initialise a feature matrix with zeros\n",
    "        feature_matrices['unigram'] = numpy.zeros((rows, len(self.unigrams)), dtype=numpy.int32)\n",
    "        feature_matrices['bigram'] = numpy.zeros((rows, len(self.bigrams)), dtype=numpy.int32)\n",
    "        feature_matrices['trigram'] = numpy.zeros((rows, len(self.trigrams)), dtype=numpy.int32)\n",
    "        # populate feature matrix\n",
    "        for row, item in enumerate(data):\n",
    "            document, _ = item\n",
    "            for sentence in document:\n",
    "                if sentence[0] != '<s>':\n",
    "                    sentence.insert(0, '<s>')\n",
    "                if sentence[-1] != '</s>':\n",
    "                    sentence.append('</s>')\n",
    "                prev_token = None\n",
    "                for idx, token in enumerate(sentence):\n",
    "                    if ngram == 'unigram':\n",
    "                        try:\n",
    "                            unigram_index = self.unigram2index[token]\n",
    "                        except KeyError:\n",
    "                            continue\n",
    "                        if self.clip_counts:\n",
    "                            feature_matrices['unigram'][row, unigram_index] = 1\n",
    "                        else:\n",
    "                            feature_matrices['unigram'][row, unigram_index] += 1\n",
    "                    if ngram == 'bigram':\n",
    "                        if idx > 0:\n",
    "                            bigram = (prev_token, token)\n",
    "                            try:\n",
    "                                bigram_index = self.bigram2index[bigram]\n",
    "                            except KeyError:\n",
    "                                continue\n",
    "                            if self.clip_counts:\n",
    "                                feature_matrices['bigram'][row, bigram_index] = 1\n",
    "                            else:\n",
    "                                feature_matrices['bigram'][row, bigram_index] += 1\n",
    "                    if ngram == 'trigram':\n",
    "                        if idx > 1:\n",
    "                            trigram = (prev_prev_token, prev_token, token)\n",
    "                            try:\n",
    "                                trigram_index = self.trigram2index[trigram]\n",
    "                            except KeyError:\n",
    "                                continue\n",
    "                            if self.clip_counts:\n",
    "                                feature_matrices['trigram'][row, trigram_index] = 1\n",
    "                            else:\n",
    "                                feature_matrices['trigram'][row, trigram_index] += 1\n",
    "\n",
    "                    prev_prev_token = prev_token\n",
    "                    prev_token = token\n",
    "        return feature_matrices[ngram]\n",
    "        \n",
    "    def add_additional_features(self, data, feature_matrix, negation):\n",
    "        \"\"\"\n",
    "        Additional features representing number of positive lexicons, number of negative lexicons\n",
    "        and number of negated words are added to the existing feature matrix built above.\n",
    "        \"\"\"\n",
    "        self.doc2poslex = []\n",
    "        self.doc2neglex = []\n",
    "        for row, item in enumerate(data):\n",
    "            document, _ = item\n",
    "            doc_pos_tot = 0\n",
    "            doc_neg_tot = 0\n",
    "            for sentence in document:\n",
    "                for token in sentence:\n",
    "                    if token in self.positive_lexicons:\n",
    "                        doc_pos_tot += 1\n",
    "                    if token in self.negative_lexicons:\n",
    "                        doc_neg_tot += 1\n",
    "            self.doc2poslex.append(doc_pos_tot)\n",
    "            self.doc2neglex.append(doc_neg_tot)\n",
    "        if negation:\n",
    "            additional_features = numpy.array((self.doc2negate, self.doc2poslex, self.doc2neglex)).T\n",
    "        else:\n",
    "            additional_features = numpy.array((self.doc2poslex, self.doc2neglex)).T\n",
    "        new_feature_matrix = numpy.append(feature_matrix, additional_features, 1)\n",
    "        return new_feature_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "class PolarityPredictorAssignTargets(PolarityPredictorExtractFeatures):\n",
    " \n",
    "    def get_targets(self, data):\n",
    "        ''' \n",
    "        Column vector with target labels is created.\n",
    "        '''\n",
    "        targets = numpy.zeros(len(data), dtype=numpy.int8)\n",
    "        index = 0\n",
    "        for _, label in data:\n",
    "            if label == 'pos':\n",
    "                targets[index] = 1\n",
    "            index += 1\n",
    "        return targets\n",
    "\n",
    "    def train_model_on_features(self, tr_features, tr_targets):\n",
    "        raise NotImplementedError"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "class PolarityPredictor(PolarityPredictorAssignTargets):\n",
    "\n",
    "    def train_model_on_features(self, tr_features, tr_targets):\n",
    "        self.model.fit(tr_features, tr_targets)\n",
    "        \n",
    "    def predict(self, data, feature, get_accuracy = False, get_confusion_matrix = False):\n",
    "        \n",
    "        if self.lemmatise:\n",
    "            tagged_data = self.get_pos_tags(data)\n",
    "            lemmatised_data = self.lemmatise_data(tagged_data)\n",
    "            data = lemmatised_data\n",
    "        \n",
    "        if self.negation:\n",
    "            self.add_negation_to_data(data)\n",
    "        \n",
    "        if self.remove_stopwords:\n",
    "            self.remove_stopwords_from_data(data)\n",
    "        \n",
    "        features = self.extract_features(data, feature)\n",
    "        \n",
    "        if self.additional_features:\n",
    "            self.get_sentiment_lexicons()\n",
    "            features = self.add_additional_features(data, features, self.negation)\n",
    "            \n",
    "        y_pred = self.model.predict(features)\n",
    "        \n",
    "        labels = []\n",
    "        for is_positive in y_pred:\n",
    "            if is_positive:\n",
    "                labels.append('pos')\n",
    "            else:\n",
    "                labels.append('neg')\n",
    "                \n",
    "        if get_accuracy or get_confusion_matrix:\n",
    "            retval = []\n",
    "            retval.append(labels)\n",
    "            y_true = self.get_targets(data)\n",
    "            if get_accuracy:\n",
    "                retval.append(metrics.accuracy_score(y_true, y_pred))\n",
    "            if get_confusion_matrix:\n",
    "                retval.append(metrics.confusion_matrix(y_true, y_pred))\n",
    "            return retval\n",
    "        else:\n",
    "            return labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_model(model, splits, feature, verbose = False):\n",
    "    accuracies = []\n",
    "    fold = 0\n",
    "    for tr_data, te_data in splits:\n",
    "        if verbose:\n",
    "            print('Evaluating fold %d of %d' %(fold+1, len(splits)))\n",
    "            fold += 1\n",
    "        model.train(tr_data, feature)\n",
    "        _, accuracy = model.predict(te_data, feature, get_accuracy = True)\n",
    "        accuracies.append(accuracy)\n",
    "        if verbose:\n",
    "            print('-->', accuracy)\n",
    "    n = float(len(accuracies))\n",
    "    avg = sum(accuracies) / n\n",
    "    mse = sum([(x-avg)**2 for x in accuracies]) / n\n",
    "    return (avg, mse**0.5, min(accuracies),\n",
    "            max(accuracies))\n",
    "\n",
    "def print_first_predictions(model, te_data, feature, n = 12):\n",
    "    predictions = model.predict(te_data, feature)\n",
    "    for i in range(n):\n",
    "        document, label = te_data[i]\n",
    "        prediction = predictions[i]\n",
    "        print('%4d %s %s %s' %(i, label, prediction, get_document_preview(document),))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Baseline Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We plan to run many different experiments using different feature representations and different learning models.   Therefore, having an evaluation table which contains the details of each experiment and the corresponding evalation results will be useful. \n",
    "The following values will be recorded in the dataframe for each model:\n",
    "* Average 10 Fold Cross-Validation Accuracy\n",
    "* Root Mean Square Error (RMSE)\n",
    "* Minimum Accuracy\n",
    "* Maximum Accuracy\n",
    "\n",
    "Below, we define table to store these."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to beat the baseline model which uses <b>clip counts</b> and <b>MultinomialNB</b>. We will test that model below and store the data as *baseline-NB* in the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "learning_model = MultinomialNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "evaluation_dataframes = {}\n",
    "evaluation_dataframes['MultinomialNB'] = pd.DataFrame(columns=['name', 'learning_model', 'feature', 'clip_counts', 'negation', 'remove_stopwords', 'lemmatise', 'additional_features', 'avg_cv_acc', 'rmse', 'min_acc', 'max_acc'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We run the baseline approach as a functionality test. The settings used are listed in the cell below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "clip_counts = True\n",
    "negation = False\n",
    "remove_stopwords = False\n",
    "lemmatise = False\n",
    "additional_features = False\n",
    "feature = 'unigram'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "model = PolarityPredictor(clip_counts, negation, remove_stopwords, lemmatise, additional_features, learning_model)\n",
    "baseline_avg, baseline_rmse, baseline_min_acc, baseline_max_acc = evaluate_model(model, splits, feature, verbose = False)\n",
    "evaluation_dataframes['MultinomialNB'].loc[0] = ['baseline-NB', learning_model, feature, clip_counts, negation, remove_stopwords, lemmatise, additional_features, baseline_avg, baseline_rmse, baseline_min_acc, baseline_max_acc]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, the baseline approach achieves an average accuracy score of 82.9% and has been to our evaluation table. The format of this dataframe can be seen below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>learning_model</th>\n",
       "      <th>feature</th>\n",
       "      <th>clip_counts</th>\n",
       "      <th>negation</th>\n",
       "      <th>remove_stopwords</th>\n",
       "      <th>lemmatise</th>\n",
       "      <th>additional_features</th>\n",
       "      <th>avg_cv_acc</th>\n",
       "      <th>rmse</th>\n",
       "      <th>min_acc</th>\n",
       "      <th>max_acc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>baseline-NB</td>\n",
       "      <td>MultinomialNB()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.829</td>\n",
       "      <td>0.021071</td>\n",
       "      <td>0.785</td>\n",
       "      <td>0.855</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          name   learning_model  feature clip_counts negation  \\\n",
       "0  baseline-NB  MultinomialNB()  unigram        True    False   \n",
       "\n",
       "  remove_stopwords lemmatise additional_features  avg_cv_acc      rmse  \\\n",
       "0            False     False               False       0.829  0.021071   \n",
       "\n",
       "   min_acc  max_acc  \n",
       "0    0.785    0.855  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluation_dataframes['MultinomialNB']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Experiments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our model builds three different feature representations from the input data:\n",
    "* Bag-of-Words (Unigrams)\n",
    "* Bag-of-Bigrams\n",
    "* Bag-of-Trigrams\n",
    "\n",
    "The *PolarityPredictor* model takes in a *feature* parameter which specifies which feature of the above to use. There is also a *learning_model* parameter which specifies which particular learning model to use.\n",
    "\n",
    "The model can also be tweaked to perform the following:\n",
    "* Clip Counts in feature matrices\n",
    "* Negation\n",
    "* Removal of StopWords\n",
    "* Lemmatisation\n",
    "* Include additional features\n",
    "\n",
    "In the following section of the notebook we will experiment to see how the added features affect accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Experiment 1 & 2: Bag of Bigrams and Bag of Trigrams"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first change which we implemented in the code was to add bigram and trigram feature sets. We initialised two extra sets in order to store the bigrams and trigrams in the *reset_feature_sets* function. The bigrams and trigrams were then added to these new sets in the *add_to_feature_sets_from_data* function.\n",
    "\n",
    "This was done by checking if the index of the token was greater than zero before adding bigrams and checking if it was greater than one before adding trigrams. We also escaped the start and end of sentences using the respective '\\<s>' and '\\</s>' tags. In the *finalise_feature_sets* function, a reverse map dictionary is created for both bigrams and trigrams, similar to that which was done for unigrams originally. This enables faster lookup when populating the feature matrix.\n",
    "\n",
    "In order to store a feature matrix for each implementation, we originally created a *feature_matrices* dict in the *extract_features* function. However, when building all three feature matrices, computation time proved to be too long. Therefore, in order to save time in computations, only the feature matrix which is being used (either unigram, bigram or trigram), is populated in the function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating fold 1 of 10\n",
      "--> 0.805\n",
      "Evaluating fold 2 of 10\n",
      "--> 0.86\n",
      "Evaluating fold 3 of 10\n",
      "--> 0.82\n",
      "Evaluating fold 4 of 10\n",
      "--> 0.86\n",
      "Evaluating fold 5 of 10\n",
      "--> 0.85\n",
      "Evaluating fold 6 of 10\n",
      "--> 0.82\n",
      "Evaluating fold 7 of 10\n",
      "--> 0.865\n",
      "Evaluating fold 8 of 10\n",
      "--> 0.825\n",
      "Evaluating fold 9 of 10\n",
      "--> 0.835\n",
      "Evaluating fold 10 of 10\n",
      "--> 0.855\n",
      "Evaluating fold 1 of 10\n",
      "--> 0.745\n",
      "Evaluating fold 2 of 10\n",
      "--> 0.745\n",
      "Evaluating fold 3 of 10\n",
      "--> 0.755\n",
      "Evaluating fold 4 of 10\n",
      "--> 0.805\n",
      "Evaluating fold 5 of 10\n",
      "--> 0.79\n",
      "Evaluating fold 6 of 10\n",
      "--> 0.79\n",
      "Evaluating fold 7 of 10\n",
      "--> 0.785\n",
      "Evaluating fold 8 of 10\n",
      "--> 0.77\n",
      "Evaluating fold 9 of 10\n",
      "--> 0.745\n",
      "Evaluating fold 10 of 10\n",
      "--> 0.755\n"
     ]
    }
   ],
   "source": [
    "index = 1\n",
    "for feature in ['bigram', 'trigram']:\n",
    "    model = PolarityPredictor(clip_counts, negation, remove_stopwords, lemmatise, additional_features, learning_model)\n",
    "    avg, rmse, min_acc, max_acc = evaluate_model(model, splits, feature, verbose = True)\n",
    "    evaluation_dataframes['MultinomialNB'].loc[evaluation_dataframes['MultinomialNB'].index.max()+1] = [f'param-exp-NB-{index}', learning_model, feature, clip_counts, \n",
    "                                                  negation, remove_stopwords, lemmatise, additional_features, avg, rmse, min_acc, max_acc]\n",
    "    index += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Comparison of different ngram feature representation accuracy using baseline settings (RMSE)'}>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAm4AAAFOCAYAAAA/7JG4AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAA82ElEQVR4nO3deZwU9Z3/8ddnOI3HgoIol4MGDMcQNIxgBIWoHAZFXV2BYEBd8YBoEjdGTbKikQQ1MQkGJbqyYFS8BaJkRROPZX8iR5xwOgFkAiMIyKUIKIOf3x/1nbGm6e5phpGh4P18POYxXd+rvlX97epPf6uq29wdERERETnw5dV2B0REREQkNwrcRERERBJCgZuIiIhIQihwExEREUkIBW4iIiIiCaHATURERCQhFLjtB2b2HTObWdv9KGdmh5nZn8xsq5k9k0P5XmZWGltebGa9wmMzs/82s81mNiekXWdm68xsm5kd82Vtx6HAzE42s3fM7GMzu6G2+yP7zsxah9dGndruy6Emfuyq4XaHm9msmm63inWONrPHwuNEjikzm2BmP6uF9V5jZr/d3+vNxszuM7NrcymbqMDNzIaY2bwwQNea2Z/NrEdt96sq7v64u/ep7X7EXAI0A45x90v3trK7d3T318NiD+BcoKW7n2Zm9YD7gD7ufoS7b6ypTufCzErM7Jz9uc4v2c3A6+5+pLuP25eGzOx1M/v3GurXIcnM8s3MzazuXtSpNCbdfVV4bez+cnopmaQcuw4aB9qYSvc6SRfcuvu17v7z/dy3+sBPgXtT+rot/JWY2S0pdUrM7DMza5KSXhTq5ofllmb2nJl9GCZGFprZ8AzrKf+7LDR3L/CT0L+sEhO4mdkPgd8CvyAKOloDDwADa7FbVdqbA/x+dALwD3cvq6G2Stz9k7DcDGgILK5OY0n6xLifntsTqOa+rGn7ur01vb+SNFYOBQfosU4k1UDgXXd/PyW9kbsfQTSx8TMzOzclfyUwuHzBzAqAw1LK/BFYTXTcPgb4LrAu3Xpif08BuPta4F3ggiq3wN0P+D/gX4BtwKVZyjQgCuzWhL/fAg1CXi+glGj2Yj2wFrgQOA/4B7AJuC3W1mjgWeAp4GPgb8DXY/m3ACtC3hLgoljecOD/gN+Edu8KabNCvoW89cBWYAHQKbadjwIbgH8SfSrIi7U7C/gVsJloEPXPsj/aA68DW4je+C8I6XcAnwG7wj69Kk3dw4BJYT1LgB8BpbH8EuAc4CpgJ7A7tDUF+ATwsPzXUP5rwCthfxQD/xZraxLwIDAj1D0HaA48F/bDSuCGlOfm6bCfPg7b1jXk/RH4HNgR1n9zmm0rHws3xcbCFbH8Y4A/AR8Bc8PzNyuW78BIYBmwMqT9jujF+hEwH+iZ0t9ngMdCfxcC7YBbw/pXE81OpnsO/xr27c6wPe2IxvmvgFVEB4QJwGGhfGPgxbDfNofHLUPemJS2fg/kh+2pG1vn68C/ZxnLGdefpv97VT/23NwGfEg0zr6zD2PlNGBeeF7WAffF8roD/4/o9fF3oFfKPvh56PvHwEygSchbxRfjextwOnBSeK42hn4/TnRwhjRjMnW/h22YHvbRcuDqXMZ7hn2ebSzWCfu2/Ng1H2gV8jryxWt0HeF4GPb5Xamvn5RjwY+JjmOfAnXJcnwMda4GlsbyTyU6xjyXUu5+4LcZttOBr6aMjbvC4yZEY39L2J7/5YvjaAlwTi77NvTrnZD3DNH7wV0Z+jM8jJf7iY7r7wJnx/KviG3ze8A1sbxs/a3qWPhYeJxP5TH1OhnGcFXjP822/Rh4P7RTXL5dRBM/5c/1xrAvj87yOom/V2xJ87z1oprHZrK8r6bZnonAT2PLlfZdSJsD/ChlnP8UmBtL+xXwk1A3P6RtA7pkWO8e60lT5ifAf2fKryhXVYED4Q/oB5RVscF3ArOBY4GmYVD+PDYgyoD/BOoRHTg2AE8ARxIdtHYCJ8ZeELuIIu96wH8QvWjqhfxLiV5QecBlRG8ix8dewGXA94gOYodROXDrS3TAbBQGW/tY3UeBaaFP+URB5VWxdneFvtcBriMKUC3NvqhH9AZwG1Af+BbRi+7k1Bd8hn05lujgcTTQClhEmsAt1q94YFNpcAKHE72RXBH2x6lEb24dYy/crcAZYX9+Jeyf/wx9P5HoQNc31vedREF3HeCXwOx0fcuwbb3C83Nn2E/nAduBxiH/yfD3FaBD6Htq4PZK2DflAcdQooNKXaKDzgdAw5T+9g35jxKNpZ/wxVhcmaW/rxMCqbD8W6I3+aPDOPkT8MuQdwzwr6HvRxK92UzN0lal5yq1DOnHcsb1p+n7XtWPPTf3EQV4ZxG9tsrH7ST2bqy8BVweHh8BdA+PWxC90ZwX2jk3LDeN7YMVRIHyYWF5bJZ99tXQRgOiY8+bxAIOUsZkahvAG0RnDxoCXYiOTeVvjqPJMt7T7PNsY/FHRB8cTiY69nw9lD2S6E3yptCHI4FusX1eVeBWRHScKH89ZDs+XkoUBBSGPnyVaHbi+FCuUShXl+hN+BsZtjNb4PZLog8E9cJfT8Jxkj0Dt7T7lmg8/RO4MbRxMdEH3myBWxnwg1D+MqKxWh7IfJsowDeicb0dODVbf8P+q+pYmC1wyzSGs47/lO06megY2Dy2npPC4+8Tvee2JBr7fwCmZHmdDCd2LE3zvPWimsdmsryvptmmucQmgdLsu+5hvfEJmRKiD4rFoe06fDGzFg/cXiUKlgcBrVPWu8c+SdO3i4G/ZcqvKFdVgQPhD/gO8EEVZVYA58WW+xKdwisfEDuAOmH5yLADu8XKzwcujL0g4sFAHtGBrWeGdRcBA2ODc1WaF3X5APsWUUDWnfCpKqTXIfrE2iGWdg3R9U3lbSyP5X0lbMNxafrTk+iAHW9/CjA69QWfYXveA/rFlkdQ/cDtMuB/U9r/A3B7eDwJeDSW1y3N/ruV8Ckk9P3VWF4HYEe6vmXYtvKxED+grA/PRx2i4PjkWF66GbdvVTEWNxNmaEN/X4nlnU/0qSx1LDbK0NbrfBFIGdGb20mx/NPJEPgRBQGb07WV7rlKs77h8eeiGuvfq/p8ceA+PJb/NPCzao6VN4lmmJuklPkx8MeUtJeBYbF9EP9Efj3wP5n2WZrtvhB4J9OYjLdBFPDsBo6M5f8SmJTLeK/qj8pjsZhwnEopMzje35S8SVQduF1ZRR+K+OL4+DJwY4ZyfybMNgIDgCVZ2nQyB253En0A/mqaehXPRbZ9C5xJFGBaLH8W2QO3NSnl5xA+OKQpP7V8P2TqL7kdC7MFbpnGcNbxn5L+VaLj4zmEiYtY3lIqzyoeT3T8rJvan9g+qipwq9axmQzvqxn2/TIqv7+V93VLWL8TzabFn8uSsA9+SvT67Ef0Ab4ulQO3xkQTH4uJXtdFQGGa9cT/2sfWcy7wXlWv66Rc47YRaFLFNRTNiT4hlftnSKtow7+4cHNH+B8/97yD6FN5udXlD9z9c6Ip3OYAZvbdcFHiFjPbAnQimu7eo24qd/8r0Wmq8cA6M3vIzI4K9cs/5cW3oUVs+YNYO9vDw3ifyzUHVod+Z2orm+Yp2/DPTAVzcALQrXxfhf31HeC4WJnVKeWbp5S/jejauXIfxB5vBxru5fU1G73y9X3bifZjU6IXYrw/6Z7LSmlmdpOZLQ0Xo24hOuUdHw+p4+zDNGMx3fOYqilhlim2b/4npGNmXzGzP5jZP83sI6LApdE+XgsW39as66+h+pv9i+slYc/X8d6MlauIZhzeNbO5ZjYgVu/SlHo9iN54yqWOsYzPj5kda2ZPmtn7Yb8/RuXnP5vmwCZ3/ziWlvF1TxXjvYqx2IroA26qTOm5Sn09ZDs+ZlvXZKIZQ8L/P1azP/cSnXGYaWbvpV5oniLTvm0OvO/h3TTIeFwPUstXjF0z629ms81sU9gn5/HFPsnU31yOhdlkGsO5jH8A3H050czaaGB9GOfNY+28EGtjKVGwkmv/0qnWsTnL+2o6m4k+MKdqEtb1H0RBZL00Zf4IDCEKQh9NzXT3ze5+i7t3JNoPRcBUM7P4ety9UexvaSzvSKJgLqukBG5vEU1pX5ilzBqigVSudUirrlblD8wsj2g6eI2ZnQA8DIwiuiuzEdGpxPgTE3/x7sHdx7n7N4hO0bYjOoXxIdEnitRtSL2AMhdrgFah39Vpay2x7Q91q2s18EbKQD3C3a+LlUk9OK5MKX+ku5+X4/qy7vsqbCCa8WkZS2uVplzFOsysJ9En2H8jmtJvRHSKxNLU21cfEgV6HWP75l88uqAWolNdJxPNJB9FNGtArC+p+6Y8QPpKLO24lDLxOlWtP529rd/YzA6PLae+jnMeK+6+zN0HE10+cTfwbGh7NdGMQ7ze4e4+Nst2pFt/uV+G9M5hvw8l9+PBGuBoM4u/kVTrdZ/DWFxNdLouVaZ0iMZItvEBlV8PVR0fs61rKtDZzDoRzbg9nqEcRG/oafvl7h+7+03ufiLRDPcPzezsLG2lsxZokfKGm+5YEJdavjXRe0YDouvUfgU0C/tkBmGfZOnvvh4LM9mr8e/uT7h7D744LXh3rJ3+Ke009Oii/3Rj/ks9Nmd4X01nQcjfs4Puu93910TxxvVp8v9JdKnLecDz2Trs7h8SPefNiS4NyUV7omsOs0pE4ObuW4nO8483swvDzEK98CnmnlBsCvBTM2tq0S27/0n0ybe6vmFmF4dPX98nOo05m+iaLScaSJjZFUSfKHNiZoVm1s2ir834hHDBZpiBeRoYY2ZHhgPgD6u5DW+Htm8O+6kX0QHhyRzrPw3camaNzawl0TVK1fUi0M7MLg99qRf2QfsM5ecAH5nZjy36vrk6ZtbJzApzXN86omtB9lp4Dp4HRocx9jWiu4KyOZLogLIBqGtm/wlk+qS3T8IM6sPAb8zsWAAza2FmfWN92QFsMbOjgdtTmqi0b9x9A1GAMDTs5yvJ/Kaay/r3tf/l7jCz+iEQGUB0rV46WceKmQ01s6ZhvVtCnd1Er6nzzaxvqNPQou8qbJl+NZVsILrZID7GjiRccG1mLdjzDSPjmHT31UTX4/4y9KMz0UxhtqAlk6rG4n8BPzezthbpbNH3LL4IHGdm3zezBuH40y3UKQLOM7Ojzew4omNhNlUdH/8L+A8z+0bow1fDsQ5330l0U9gTwBx3X5VlPUXAkPD89SO6boywzgGhXSO6kH13+Nsbb4U6o8ysrpkNJLrZJZtjgRvCMe5SojfhGURnUhoQgg8z6w9UfD1Ulv7u67Ewk5zHv0XfI/mtEHzuJDq+lO/LCUTvVyeEsk3DfoL0r5N1QEvL4esuUlV1bM70vpqhuRnExksGY4nePxumybuK6HKZT1IzzOzu8BzVtejD2HVElzjl+rVYZxFdMpBVIgI3AHe/jyiQ+SnRoFhN9KluaihyF9EdZAuILsD9W0irrmlE12dtBi4HLnb3Xe6+BPg10Qt7HVBAdDFiro4ievPaTDSVvpEoKocoQPqE6BqzWUQHsIl723F3/4zoluL+RLMcDwDfdfd3c2zijtC3lUR3I1X3lAXhFFAfoos11xBN399NdCBLV343UZDZJaz/Q6KD/b/kuMpfEgXwW8zsP6rR5VFhXR8QbfcUoqA9k5eJXmj/INpnO6n6lMq++DHRaZXZFp2We5Volg2iC/8PI9pns4lOQ8b9DrjEoi9LLv9OuKuJAo2NRJ9U/98+rH9f+w/Rft9MNFYeB67NNG5zGCv9gMVmto1o2we5+84QLA0kOu1Ufiz5ETkcD8MlCmOA/wtjrDvR6+VUotmtl9jzk3hVY3Iw0fUva4AXiK7/fKWqvqRR1Vi8j+hD2UyiAOERohsKPia6tuZ8ov2/DOgd6vyRaAagJNR7KlsHqjo+uvszRPvvCaIbpqZSeTZicqhT1THnxtDfLUSXXkyN5bUlGlfbQj8e8L387rZwDL2Y6E16C9Es6otkPxa8Hdb9IdE2XuLuG8P+vYFo328mOtU2var+1sCxMNO27c34b0AUxHxINDaODfUgek1NJzrF+zHRMadbWEe618lfia79+sDMPqxG17Mdm7O9r6b6E/A1++KUbzovhbauTs1w9xXuPi9Dva8QvYa3EL2Pn8CeX++xxSp/j9sPAczseKLrLKdm6RfwxZ02EmNmo4kuFB1aVVk5uJnZ3UQ3gAyr7b4c7CyaGX7M3XOZ+ZKDkJm1JvoqjePc/aPa7k+cmb0NTHD3/67tvsi+HZvNbATRjYDfr/GOVZOZ/RpY4e4PVFVWX5goEhOm4OsTzdoWEn3i1q8NiHzJLLom94fAkwdC0GZmZxHdifsh0axeZ/acxZb9pCaPze7+UA12rUa4+025llXgJlLZkURT8M2JbkX/NdFpcxH5klh008g6otNc/Wq5O+VOJjq9eQTRnbCXePTt9lI7dGwOdKpUREREJCESc3OCiIiIyKFOgZuIiIhIQhwU17g1adLE8/Pza7sbIiIiIlWaP3/+h+6e7VdnMjooArf8/Hzmzcv0tSoiIiIiBw4zq/ZPSepUqYiIiEhCKHATERERSQgFbiIiIiIJcVBc4yYiIpJ0u3btorS0lJ07d9Z2V6SGNGzYkJYtW1KvXr0aa1OBm4iIyAGgtLSUI488kvz8fMystrsj+8jd2bhxI6WlpbRp06bG2tWpUhERkQPAzp07OeaYYxS0HSTMjGOOOabGZ1AVuImIiBwgFLQdXL6M51OBm4iIiEhCKHATERERSQgFbiIiIlJtJSUltG/fnquvvpqOHTvSp08fduzYwcMPP0xhYSFf//rX+dd//Ve2b98OwPDhw7nuuuvo3bs3J554Im+88QZXXnkl7du3Z/jw4RXtzpw5k9NPP51TTz2VSy+9lG3bttXSFh5YFLiJiIjIPlm2bBkjR45k8eLFNGrUiOeee46LL76YuXPn8ve//5327dvzyCOPVJTfvHkzf/3rX/nNb37D+eefzw9+8AMWL17MwoULKSoq4sMPP+Suu+7i1Vdf5W9/+xtdu3blvvvuq8UtPHDo60BERA5CBZMLarsLh5yFwxbWdhdqTZs2bejSpQsA3/jGNygpKWHRokX89Kc/ZcuWLWzbto2+fftWlD///PMxMwoKCmjWrBkFBdF47dixIyUlJZSWlrJkyRLOOOMMAD777DNOP/30/b5dByIFbiIiIrJPGjRoUPG4Tp067Nixg+HDhzN16lS+/vWvM2nSJF5//fU9yufl5VWqm5eXR1lZGXXq1OHcc89lypQp+20bkkKnSkVERKTGffzxxxx//PHs2rWLxx9/fK/qdu/enf/7v/9j+fLlAGzfvp1//OMfX0Y3E0czbnLIyb/lpdruwiGnZOy3a7sLIrKf/fznP6dbt26ccMIJFBQU8PHHH+dct2nTpkyaNInBgwfz6aefAnDXXXfRrl27L6u7iWHuXtt92Gddu3b1efPm1XY3JCEUuO1/Ctz2P13jtv/t6zVuS5cupX379jXUGzlQpHtezWy+u3etTns6VSoiIiKSEArcRERERBJCgZuIiIhIQihwExEREUkIBW4iIiIiCaHATURERCQhFLiJiIgIEP1gfKdOnb6Utl9//XUGDBgAwPTp0xk7dmyNtDtp0iTy8vJYsGBBRVqnTp0oKSkBID8/n4KCArp06UJBQQHTpk2rkfXWlpy+gNfM+gG/A+oA/+XuY1PyLeSfB2wHhrv738ysFfAocBzwOfCQu/8u1DkaeArIB0qAf3P3zSHvVuAqYDdwg7u/vG+bKSIikiw1/Z2TB9L3KV5wwQVccMEFNdZey5YtGTNmDE899VTa/Ndee40mTZpQXFxMnz59GDhwYI2te3+rcsbNzOoA44H+QAdgsJl1SCnWH2gb/kYAD4b0MuAmd28PdAdGxureAvzF3dsCfwnLhPxBQEegH/BA6IOIiIh8ycrKyhg2bBidO3fmkksuYfv27dx5550UFhbSqVMnRowYQfmX948bN44OHTrQuXNnBg0aBMAnn3zClVdeSWFhIaecckraGa5JkyYxatQoAIYPH84NN9zAN7/5TU488USeffbZinL33nsvhYWFdO7cmdtvvz1jnwcMGMDixYspLi7Oum0fffQRjRs33ut9ciDJ5VTpacByd3/P3T8DngRSQ9WBwKMemQ00MrPj3X2tu/8NwN0/BpYCLWJ1JofHk4ELY+lPuvun7r4SWB76ICIiIl+y4uJiRowYwYIFCzjqqKN44IEHGDVqFHPnzmXRokXs2LGDF198EYCxY8fyzjvvsGDBAiZMmADAmDFj+Na3vsXcuXN57bXX+NGPfsQnn3ySdZ1r165l1qxZvPjii9xyyy0AzJw5k2XLljFnzhyKioqYP38+b775Ztr6eXl53HzzzfziF79Im9+7d286derEWWedxV133VXdXXNAyOVUaQtgdWy5FOiWQ5kWwNryBDPLB04B3g5Jzdx9LYC7rzWzY2NtzU7Tlogk1eh/qe0eHHratK7tHkhCtWrVijPOOAOAoUOHMm7cONq0acM999zD9u3b2bRpEx07duT888+nc+fOfOc73+HCCy/kwgsvBKKAa/r06fzqV78CYOfOnaxatSrrOi+88ELy8vLo0KED69atq2hn5syZnHLKKQBs27aNZcuWceaZZ6ZtY8iQIYwZM4aVK1fukVd+qnTFihWcffbZ9OrViyOOOKJa+6e25RK4WZq01B84zVrGzI4AngO+7+4f1cD6MLMRRKdlad1aBygREZGaEF22Xnn5+uuvZ968ebRq1YrRo0ezc+dOAF566SXefPNNpk+fzs9//nMWL16Mu/Pcc89x8sknV2qnPCBLp0GDBhWPy0/Duju33nor11xzTaWy48eP5+GHHwZgxowZFel169blpptu4u677864npNOOolmzZqxZMkSTjstmSfzcjlVWgq0ii23BNbkWsbM6hEFbY+7+/OxMuvM7PhQ5nhg/V6sD3d/yN27unvXpk2b5rAZIiIiUpVVq1bx1ltvATBlyhR69OgBQJMmTdi2bVvFNWiff/45q1evpnfv3txzzz1s2bKFbdu20bdvX+6///6KAOydd96pVj/69u3LxIkT2bZtGwDvv/8+69evZ+TIkRQVFVFUVETz5s0r1Rk+fDivvvoqGzZsSNvm+vXrWblyJSeccEK1+nQgyGXGbS7Q1szaAO8T3TgwJKXMdGCUmT1JdBp1azj9acAjwFJ3vy9NnWHA2PB/Wiz9CTO7D2hOdMPDnL3eMhEREdlr7du3Z/LkyVxzzTW0bduW6667js2bN1NQUEB+fj6FhYUA7N69m6FDh7J161bcnR/84Ac0atSIn/3sZ3z/+9+nc+fOuDv5+fkV18TtjT59+rB06VJOP/10AI444ggee+wxjj322Ix16tevzw033MCNN95YKb13797UqVOHXbt2MXbsWJo1a7bX/TlQWHlEnLWQ2XnAb4m+DmSiu48xs2sB3H1CCNB+T3QX6HbgCnefZ2Y9gP8FFhJ9HQjAbe4+w8yOAZ4GWgOrgEvdfVNY30+AK4nuSv2+u/85W/+6du3q8+bN27stl0NWTd9iL1UraZj6WU++bAW6xm2/Wzhs4T7VX7p0Ke3bt6+h3siBIt3zambz3b1rddrL6Xvc3H0GMCMlbULssQMj09SbRfpr1nD3jcDZGfLGAGNy6ZuIiIjIoUK/nCAiIiKSEArcRERERBJCgZuIiIhIQihwExEREUkIBW4iIiIiCaHATURERA4ZkyZNIi8vjwULFlSkderUiZKSEgDy8/MpKCigS5cuFBQUMG3atLTtvPvuu5x++uk0aNCg4ue99oecvg5ERERE9rOa/o3f0Vtrtr0UZWVl1K2bjLCiZcuWjBkzhqeeeiptfvlvmxYXF9OnTx8GDhy4R5mjjz6acePGMXXq1C+5t5Vpxk1EREQAKCkp4Wtf+xrDhg2jc+fOXHLJJWzfvp0777yTwsJCOnXqxIgRIyp+zqpXr17cdtttnHXWWfzud7/jT3/6E926deOUU07hnHPOqfh90tGjRzNs2DD69OlDfn4+zz//PDfffDMFBQX069ePXbt2pe3PvffeS2FhIZ07d+b2228H4IUXXuCcc87B3Vm7di3t2rXjgw8+YNKkSQwcOJB+/fpx8sknc8cdd2TczgEDBrB48WKKi4uz7o+PPvqIxo0bp8079thjKSwspF69elXu15qkwE1EREQqFBcXM2LECBYsWMBRRx3FAw88wKhRo5g7dy6LFi1ix44dlX7CasuWLbzxxhvcdNNN9OjRg9mzZ/POO+8waNAg7rnnnopyK1as4KWXXmLatGkMHTqU3r17s3DhQg477DBeemnPX7SZOXMmy5YtY86cORQVFTF//nzefPNNLrroIo477jjGjx/P1VdfzR133MFxxx0HwJw5c3j88ccpKirimWeeIdOvKuXl5XHzzTfzi1/8Im1+79696dSpE2eddRZ33XXXvuzOGqfATURERCq0atWKM844A4ChQ4cya9YsXnvtNbp160ZBQQF//etfWbx4cUX5yy67rOJxaWkpffv2paCggHvvvbdSuf79+1OvXj0KCgrYvXs3/fr1A6CgoKDi+rK4mTNnMnPmTE455RROPfVU3n33XZYtWwbA/fffzy9/+UsaNGjA4MGDK+qce+65HHPMMRx22GFcfPHFzJo1K+N2DhkyhNmzZ7Ny5co98l577TUWLVrEwoULGTVqVMUP3R8IFLiJiIhIhejnxysvX3/99Tz77LMsXLiQq6++mp07d1bkH3744RWPv/e97zFq1CgWLlzIH/7wh0rlGjRoAESzXfXq1atYT15eHmVlZbz99tt06dKFLl26MH36dNydW2+9laKiIoqKili+fDlXXXUVAO+//z55eXmsW7eOzz//vGId6fo+fvz4inbXrFlTkVe3bl1uuukm7r777oz74qSTTqJZs2YsWbIkYzv7mwI3ERERqbBq1SreeustAKZMmUKPHj0AaNKkCdu2bePZZ5/NWHfr1q20aNECgMmTJ+/Vert161YRpF1wwQX07duXiRMnVsx2vf/++6xfv56ysjKuuOIKnnjiCdq3b899991X0cYrr7zCpk2b2LFjB1OnTuWMM85g5MiRFe02b9680jqHDx/Oq6++yoYNG9L2af369axcuZITTjghazv7UzJu/xAREZH9on379kyePJlrrrmGtm3bct1117F582YKCgrIz8+nsLAwY93Ro0dz6aWX0qJFC7p37572NGSu+vTpw9KlSzn99NMBOOKII3jssceYMGECPXv2pGfPnnTp0oXCwkK+/e1vA9CjRw8uv/xyli9fzpAhQ+jatWvWddSvX58bbriBG2+8sVJ67969qVOnDrt27WLs2LE0a9Zsj7offPABXbt25aOPPiIvL4/f/va3LFmyhKOOOqra25wLK78zJMm6du3qmS5AFEmVf8ueF8HKl6uk4ZDa7sIhp6BN69ruwiFn4bCF+1R/6dKltG/fvoZ6Uz0lJSUMGDCARYsW1Wo/qmPSpEnMmzeP3//+97XdlUrSPa9mNt/ds0eVGehUqYiIiEhC6FSpiIiIANGvBiRxtg2i69WGDx9e29340mnGTURERCQhFLiJiIiIJIQCNxEREZGEUOAmIiIikhAK3EREROSQMWnSJPLy8liwYEFFWqdOnSp+dis/P5+CggK6dOlCQUEB06ZNS9vO448/TufOnencuTPf/OY3+fvf/74/uq+7SkVERA5EBZMLarS9ff2euaqUlZVRt24ywoqWLVsyZswYnnrqqbT5r732Gk2aNKG4uJg+ffowcODAPcq0adOGN954g8aNG/PnP/+ZESNG8Pbbb3/ZXc9txs3M+plZsZktN7Nb0uSbmY0L+QvM7NRY3kQzW29mi1LqPGVmReGvxMyKQnq+me2I5U3Yx20UERGRHJSUlPC1r32NYcOG0blzZy655BK2b9/OnXfeSWFhIZ06dWLEiBGUf3l/r169uO222zjrrLP43e9+x5/+9Ce6devGKaecwjnnnMO6deuA6BcVhg0bRp8+fcjPz+f555/n5ptvpqCggH79+rFr1660/bn33nspLCykc+fO3H777QC88MILnHPOObg7a9eupV27dnzwwQdMmjSJgQMH0q9fP04++WTuuOOOjNs5YMAAFi9eTHFxcdb98dFHH9G4ceO0ed/85jcr8rp3705paWn2nVtDqgzczKwOMB7oD3QABptZh5Ri/YG24W8E8GAsbxLQL7Vdd7/M3bu4exfgOeD5WPaK8jx3vzb3zREREZF9UVxczIgRI1iwYAFHHXUUDzzwAKNGjWLu3LksWrSIHTt28OKLL1aU37JlC2+88QY33XQTPXr0YPbs2bzzzjsMGjSIe+65p6LcihUreOmll5g2bRpDhw6ld+/eLFy4kMMOO4yXXtrzF21mzpzJsmXLmDNnDkVFRcyfP58333yTiy66iOOOO47x48dz9dVXc8cdd3DccccBMGfOHB5//HGKiop45plnyPSrSnl5edx888384he/SJvfu3dvOnXqxFlnncVdd91V5T575JFH6N+/f5XlakIuc5qnAcvd/T0AM3sSGAgsiZUZCDzqUQg+28wamdnx7r7W3d80s/xMjZuZAf8GfKu6GyEiIiI1o1WrVpxxxhkADB06lHHjxtGmTRvuuecetm/fzqZNm+jYsSPnn38+AJdddllF3dLSUi677DLWrl3LZ599Rps2bSry+vfvT7169SgoKGD37t306xfN6RQUFFRcXxY3c+ZMZs6cySmnnALAtm3bWLZsGWeeeSb3338/nTp1onv37gwePLiizrnnnssxxxwDwMUXX8ysWbMy/l7pkCFDGDNmTNrfUy0/VbpixQrOPvtsevXqxRFHHJG2nddee41HHnmEWbNmZdynNSmXU6UtgNWx5dKQtrdlMukJrHP3ZbG0Nmb2jpm9YWY901UysxFmNs/M5m3YsCHHVYmIiEg20XxK5eXrr7+eZ599loULF3L11Vezc+fOivzDDz+84vH3vvc9Ro0axcKFC/nDH/5QqVyDBg2AaLarXr16FevJy8ujrKyMt99+my5dutClSxemT5+Ou3PrrbdSVFREUVERy5cv56qrrgLg/fffJy8vj3Xr1vH5559n7fv48eMr2l2zZk1FXt26dbnpppu4++67M+6Lk046iWbNmrFkyZK07SxYsIB///d/Z9q0aRUB45ctl8DN0qSl/jJ9LmUyGQxMiS2vBVq7+ynAD4EnzOyoPRp3f8jdu7p716ZNm+a4KhEREclm1apVvPXWWwBMmTKFHj16ANCkSRO2bdvGs88+m7Hu1q1badEimreZPHnyXq23W7duFUHaBRdcQN++fZk4cSLbtm0DomBt/fr1lJWVccUVV/DEE0/Qvn177rvvvoo2XnnlFTZt2sSOHTuYOnUqZ5xxBiNHjqxot3nz5pXWOXz4cF599VUyTQCtX7+elStXcsIJJ+zRzqpVq7j44ov54x//SLt27fZqW/dFLqdKS4FWseWWwJpqlNmDmdUFLga+UZ7m7p8Cn4bH881sBdAOSH+iWkRERGpM+/btmTx5Mtdccw1t27bluuuuY/PmzRQUFJCfn09hYWHGuqNHj+bSSy+lRYsWdO/ePe1pyFz16dOHpUuXcvrppwNwxBFH8NhjjzFhwgR69uxJz5496dKlC4WFhXz7298GoEePHlx++eUsX76cIUOGZDxNWq5+/frccMMN3HjjjZXSe/fuTZ06ddi1axdjx46lWbNme9S988472bhxI9dffz0QzeBluqauJln5nSEZC0TB1T+As4H3gbnAEHdfHCvzbWAUcB7QDRjn7qfF8vOBF929U0rb/YBb3f2sWFpTYJO77zazE4H/BQrcfVOmPnbt2tX3x86Sg0P+LXteBCtfrpKGQ2q7C4ecgjata7sLh5x9/bqNpUuX0r59+xrqTfWUlJQwYMCARP7Q/KRJk5g3bx6///3va7srlaR7Xs1svrtnjyozqHLGzd3LzGwU8DJQB5jo7ovN7NqQPwGYQRS0LQe2A1fEOjcF6AU0MbNS4HZ3fyRkD6LyaVKAM4E7zawM2A1cmy1oExERETlU5PRNee4+gyg4i6dNiD12YGSGuoPTpYe84WnSniP6ehARERHZj/Lz8xM52wbR9WrDhw+v7W586fSTVyIiIiIJocBNRETkAFHVdeeSLF/G86nATURE5ADQsGFDNm7cqODtIOHubNy4kYYNG9Zou8n4NVgREZGDXMuWLSktLc34nWKSPA0bNqRly5Y12qYCNxERkQNAvXr1Kv1ElEg6OlUqIiIikhAK3EREREQSQoGbiIiISEIocBMRERFJCAVuIiIiIgmhwE1EREQkIRS4iYiIiCSEAjcRERGRhFDgJiIiIpIQCtxEREREEkKBm4iIiEhCKHATERERSQgFbiIiIiIJocBNREREJCEUuImIiIgkhAI3ERERkYTIKXAzs35mVmxmy83sljT5ZmbjQv4CMzs1ljfRzNab2aKUOqPN7H0zKwp/58Xybg1tFZtZ333ZQBEREZGDRZWBm5nVAcYD/YEOwGAz65BSrD/QNvyNAB6M5U0C+mVo/jfu3iX8zQjr6wAMAjqGeg+EPoiIiIgc0nKZcTsNWO7u77n7Z8CTwMCUMgOBRz0yG2hkZscDuPubwKa96NNA4El3/9TdVwLLQx9EREREDmm5BG4tgNWx5dKQtrdl0hkVTq1ONLPG+9iWiIiIyEEtl8DN0qR5NcqkehA4CegCrAV+vTdtmdkIM5tnZvM2bNhQxapEREREki+XwK0UaBVbbgmsqUaZStx9nbvvdvfPgYf54nRoTm25+0Pu3tXduzZt2jSHzRARERFJtlwCt7lAWzNrY2b1iW4cmJ5SZjrw3XB3aXdgq7uvzdZo+TVwwUVA+V2n04FBZtbAzNoQ3fAwJ4d+ioiIiBzU6lZVwN3LzGwU8DJQB5jo7ovN7NqQPwGYAZxHdCPBduCK8vpmNgXoBTQxs1Lgdnd/BLjHzLoQnQYtAa4J7S02s6eBJUAZMNLdd9fI1oqIiIgkWJWBG0D4qo4ZKWkTYo8dGJmh7uAM6ZdnWd8YYEwufRMRERE5VOiXE0REREQSQoGbiIiISEIocBMRERFJCAVuIiIiIgmhwE1EREQkIRS4iYiIiCSEAjcRERGRhFDgJiIiIpIQCtxEREREEkKBm4iIiEhCKHATERERSQgFbiIiIiIJocBNREREJCEUuImIiIgkhAI3ERERkYRQ4CYiIiKSEArcRERERBJCgZuIiIhIQihwExEREUkIBW4iIiIiCaHATURERCQhcgrczKyfmRWb2XIzuyVNvpnZuJC/wMxOjeVNNLP1ZrYopc69ZvZuKP+CmTUK6flmtsPMisLfhH3cRhEREZGDQpWBm5nVAcYD/YEOwGAz65BSrD/QNvyNAB6M5U0C+qVp+hWgk7t3Bv4B3BrLW+HuXcLftTlui4iIiMhBLZcZt9OA5e7+nrt/BjwJDEwpMxB41COzgUZmdjyAu78JbEpt1N1nuntZWJwNtKzuRoiIiIgcCnIJ3FoAq2PLpSFtb8tkcyXw59hyGzN7x8zeMLOee9GOiIiIyEGrbg5lLE2aV6NM+sbNfgKUAY+HpLVAa3ffaGbfAKaaWUd3/yil3gii07K0bt06l1WJiIiIJFouM26lQKvYcktgTTXK7MHMhgEDgO+4uwO4+6fuvjE8ng+sANql1nX3h9y9q7t3bdq0aQ6bISIiIpJsuQRuc4G2ZtbGzOoDg4DpKWWmA98Nd5d2B7a6+9psjZpZP+DHwAXuvj2W3jTcEIGZnUh0w8N7OW+RiIiIyEGqylOl7l5mZqOAl4E6wER3X2xm14b8CcAM4DxgObAduKK8vplNAXoBTcysFLjd3R8Bfg80AF4xM4DZ4Q7SM4E7zawM2A1c6+573NwgIiIicqjJ5Ro33H0GUXAWT5sQe+zAyAx1B2dI/2qG9OeA53Lpl4iIiMihRL+cICIiIpIQCtxEREREEkKBm4iIiEhCKHATERERSQgFbiIiIiIJocBNREREJCEUuImIiIgkhAI3ERERkYRQ4CYiIiKSEArcRERERBJCgZuIiIhIQihwExEREUkIBW4iIiIiCaHATURERCQhFLiJiIiIJIQCNxEREZGEUOAmIiIikhAK3EREREQSQoGbiIiISEIocBMRERFJCAVuIiIiIgmhwE1EREQkIXIK3Mysn5kVm9lyM7slTb6Z2biQv8DMTo3lTTSz9Wa2KKXO0Wb2ipktC/8bx/JuDW0Vm1nffdlAERERkYNFlYGbmdUBxgP9gQ7AYDPrkFKsP9A2/I0AHozlTQL6pWn6FuAv7t4W+EtYJrQ9COgY6j0Q+iAiIiJySMtlxu00YLm7v+funwFPAgNTygwEHvXIbKCRmR0P4O5vApvStDsQmBweTwYujKU/6e6fuvtKYHnog4iIiMghLZfArQWwOrZcGtL2tkyqZu6+FiD8P3Zv2jKzEWY2z8zmbdiwocqNEBEREUm6XAI3S5Pm1SiTq5zacveH3L2ru3dt2rRpNVclIiIikhy5BG6lQKvYcktgTTXKpFpXfjo1/F+/D22JiIiIHPRyCdzmAm3NrI2Z1Se6cWB6SpnpwHfD3aXdga3lp0GzmA4MC4+HAdNi6YPMrIGZtSG64WFODv0UEREROajVraqAu5eZ2SjgZaAOMNHdF5vZtSF/AjADOI/oRoLtwBXl9c1sCtALaGJmpcDt7v4IMBZ42syuAlYBl4b2FpvZ08ASoAwY6e67a2h7RURERBKrysANwN1nEAVn8bQJsccOjMxQd3CG9I3A2RnyxgBjcumbiIiIyKFCv5wgIiIikhAK3EREREQSQoGbiIiISEIocBMRERFJCAVuIiIiIgmhwE1EREQkIRS4iYiIiCSEAjcRERGRhFDgJiIiIpIQCtxEREREEkKBm4iIiEhCKHATERERSQgFbiIiIiIJocBNREREJCEUuImIiIgkhAI3ERERkYRQ4CYiIiKSEArcRERERBJCgZuIiIhIQihwExEREUkIBW4iIiIiCZFT4GZm/cys2MyWm9ktafLNzMaF/AVmdmpVdc3sKTMrCn8lZlYU0vPNbEcsb0INbKeIiIhI4tWtqoCZ1QHGA+cCpcBcM5vu7ktixfoDbcNfN+BBoFu2uu5+WWwdvwa2xtpb4e5d9mnLRERERA4yucy4nQYsd/f33P0z4ElgYEqZgcCjHpkNNDKz43Opa2YG/BswZR+3RUREROSglkvg1gJYHVsuDWm5lMmlbk9gnbsvi6W1MbN3zOwNM+uZQx9FREREDnpVnioFLE2a51gml7qDqTzbthZo7e4bzewbwFQz6+juH1VaodkIYARA69ats3RfRERE5OCQy4xbKdAqttwSWJNjmax1zawucDHwVHmau3/q7hvD4/nACqBdaqfc/SF37+ruXZs2bZrDZoiIiIgkWy6B21ygrZm1MbP6wCBgekqZ6cB3w92l3YGt7r42h7rnAO+6e2l5gpk1DTc1YGYnEt3w8F41t09ERETkoFHlqVJ3LzOzUcDLQB1gorsvNrNrQ/4EYAZwHrAc2A5cka1urPlB7HlTwpnAnWZWBuwGrnX3TfuwjSIiIiIHhVyuccPdZxAFZ/G0CbHHDozMtW4sb3iatOeA53Lpl4iIiMihRL+cICIiIpIQCtxEREREEkKBm4iIiEhCKHATERERSQgFbiIiIiIJocBNREREJCEUuImIiIgkhAI3ERERkYRQ4CYiIiKSEArcRERERBJCgZuIiIhIQihwExEREUkIBW4iIiIiCaHATURERCQhFLiJiIiIJIQCNxEREZGEUOAmIiIikhAK3EREREQSQoGbiIiISEIocBMRERFJCAVuIiIiIgmRU+BmZv3MrNjMlpvZLWnyzczGhfwFZnZqVXXNbLSZvW9mReHvvFjeraF8sZn13deNFBERETkY1K2qgJnVAcYD5wKlwFwzm+7uS2LF+gNtw1834EGgWw51f+Puv0pZXwdgENARaA68ambt3H33PmyniIiISOLlMuN2GrDc3d9z98+AJ4GBKWUGAo96ZDbQyMyOz7FuqoHAk+7+qbuvBJaHdkREREQOabkEbi2A1bHl0pCWS5mq6o4Kp1YnmlnjvVifiIiIyCEnl8DN0qR5jmWy1X0QOAnoAqwFfr0X68PMRpjZPDObt2HDhjRVRERERA4uuQRupUCr2HJLYE2OZTLWdfd17r7b3T8HHuaL06G5rA93f8jdu7p716ZNm+awGSIiIiLJlkvgNhdoa2ZtzKw+0Y0D01PKTAe+G+4u7Q5sdfe12eqGa+DKXQQsirU1yMwamFkbohse5lRz+0REREQOGlXeVeruZWY2CngZqANMdPfFZnZtyJ8AzADOI7qRYDtwRba6oel7zKwL0WnQEuCaUGexmT0NLAHKgJG6o1REREQkh8ANwN1nEAVn8bQJsccOjMy1bki/PMv6xgBjcumbiIiIyKFCv5wgIiIikhAK3EREREQSQoGbiIiISEIocBMRERFJCAVuIiIiIgmhwE1EREQkIRS4iYiIiCSEAjcRERGRhFDgJiIiIpIQCtxEREREEkKBm4iIiEhCKHATERERSQgFbiIiIiIJocBNREREJCEUuImIiIgkhAI3ERERkYRQ4CYiIiKSEArcRERERBJCgZuIiIhIQihwExEREUkIBW4iIiIiCaHATURERCQhcgrczKyfmRWb2XIzuyVNvpnZuJC/wMxOraqumd1rZu+G8i+YWaOQnm9mO8ysKPxNqIHtFBEREUm8KgM3M6sDjAf6Ax2AwWbWIaVYf6Bt+BsBPJhD3VeATu7eGfgHcGusvRXu3iX8XVvdjRMRERE5mOQy43YasNzd33P3z4AngYEpZQYCj3pkNtDIzI7PVtfdZ7p7Wag/G2hZA9sjIiIictDKJXBrAayOLZeGtFzK5FIX4Ergz7HlNmb2jpm9YWY903XKzEaY2Twzm7dhw4YcNkNEREQk2XIJ3CxNmudYpsq6ZvYToAx4PCStBVq7+ynAD4EnzOyoPRpxf8jdu7p716ZNm1axCSIiIiLJVzeHMqVAq9hyS2BNjmXqZ6trZsOAAcDZ7u4A7v4p8Gl4PN/MVgDtgHk59FVERETkoJXLjNtcoK2ZtTGz+sAgYHpKmenAd8Pdpd2Bre6+NltdM+sH/Bi4wN23lzdkZk3DTQ2Y2YlENzy8t09bKSIiInIQqHLGzd3LzGwU8DJQB5jo7ovN7NqQPwGYAZwHLAe2A1dkqxua/j3QAHjFzABmhztIzwTuNLMyYDdwrbtvqqkNFhEREUmqXE6V4u4ziIKzeNqE2GMHRuZaN6R/NUP554DncumXiIiIyKFEv5wgIiIikhAK3EREREQSQoGbiIiISEIocBMRERFJCAVuIiIiIgmhwE1EREQkIRS4iYiIiCSEAjcRERGRhFDgJiIiIpIQCtxEREREEkKBm4iIiEhCKHATERERSQgFbiIiIiIJocBNREREJCEUuImIiIgkhAI3ERERkYRQ4CYiIiKSEArcRERERBJCgZuIiIhIQihwExEREUkIBW4iIiIiCZFT4GZm/cys2MyWm9ktafLNzMaF/AVmdmpVdc3saDN7xcyWhf+NY3m3hvLFZtZ3XzdSRERE5GBQZeBmZnWA8UB/oAMw2Mw6pBTrD7QNfyOAB3OoewvwF3dvC/wlLBPyBwEdgX7AA6EdERERkUNaLjNupwHL3f09d/8MeBIYmFJmIPCoR2YDjczs+CrqDgQmh8eTgQtj6U+6+6fuvhJYHtoREREROaTlEri1AFbHlktDWi5lstVt5u5rAcL/Y/difSIiIiKHnLo5lLE0aZ5jmVzqVmd9mNkIotOyANvMrLiKdkWklqR7USdEE+DD2u5E9Syq7Q4ccmx4gke67G8nVLdiLoFbKdAqttwSWJNjmfpZ6q4zs+PdfW04rbp+L9aHuz8EPJRD/0VEqsXM5rl719ruh4hIuVxOlc4F2ppZGzOrT3TjwPSUMtOB74a7S7sDW8Ppz2x1pwPDwuNhwLRY+iAza2BmbYhueJhTze0TEREROWhUOePm7mVmNgp4GagDTHT3xWZ2bcifAMwAziO6kWA7cEW2uqHpscDTZnYVsAq4NNRZbGZPA0uAMmCku++uqQ0WERERSSpzr+qSMxGRQ5OZjQiXZYiIHBAUuImIiIgkhH7ySkRERCQhFLiJiIiIJIQCNxEREZGEUOAmIpLCzHqY2RXhcdPw1UQiIrVONyeIiMSY2e1AV+Bkd29nZs2BZ9z9jFrumoiIZtxERFJcBFwAfALg7muAI2u1RyIigQI3EZHKPvPoVIQDmNnhtdwfEZEKCtxERCp72sz+ADQys6uBV4GHa7lPIiKArnETEdmDmZ0L9AEMeNndX6nlLomIAArcREQqCadGd7r7bjM7GTgZ+LO776rlromIKHATEYkzs/lAT6AxMBuYB2x39+/UasdERNA1biIiqczdtwMXA/e7+0VAh1ruk4gIoMBNRCSVmdnpwHeAl0Ja3Vrsj4hIBQVuIiKVfR+4FXjB3Reb2YnAa7XbJRGRiK5xExEREUkITf+LiMSYWVfgNiCf2DHS3TvXVp9ERMppxk1EJMbMioEfAQuBz8vT3f2ftdYpEZFAM24iIpVtcPfptd0JEZF0NOMmIhJjZmcDg4G/AJ+Wp7v787XWKRGRQDNuIiKVXQF8DajHF6dKHVDgJiK1ToGbiEhlX3f3gtruhIhIOvoeNxGRymabmX4pQUQOSLrGTUQkMDMDVgAtgZVE17gZ4Po6EBE5EOhUqYhI4O5uZo2AtrXdFxGRdBS4iYhUNgU41t3n1nZHRERS6VSpiEiMmS0B2gH/BD5Bp0pF5ACiwE1EJMbMTkiXrl9OEJEDgQI3ERERkYTQ14GIiIiIJIQCNxEREZGEUOAmIiIikhAK3EREREQSQoGbiIiISEL8f3RUi2VFl+fdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ngram_comparison = evaluation_dataframes['MultinomialNB'][[\"name\", \"avg_cv_acc\", \"min_acc\", \"max_acc\"]]\n",
    "graph = ngram_comparison.T\n",
    "graph_header = graph.iloc[0]\n",
    "graph = graph[1:]\n",
    "graph.columns = graph_header\n",
    "graph.plot(kind='bar', figsize=(10,5), title=\"Comparison of different ngram feature representation accuracy using baseline settings\")\n",
    "\n",
    "ngram_comparison_rmse = evaluation_dataframes['MultinomialNB'][[\"name\", \"rmse\"]]\n",
    "graph = ngram_comparison_rmse.T\n",
    "graph_header = graph.iloc[0]\n",
    "graph = graph[1:]\n",
    "graph.columns = graph_header\n",
    "graph.plot(kind='bar', figsize=(10,5), title=\"Comparison of different ngram feature representation accuracy using baseline settings (RMSE)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>learning_model</th>\n",
       "      <th>feature</th>\n",
       "      <th>clip_counts</th>\n",
       "      <th>negation</th>\n",
       "      <th>remove_stopwords</th>\n",
       "      <th>lemmatise</th>\n",
       "      <th>additional_features</th>\n",
       "      <th>avg_cv_acc</th>\n",
       "      <th>rmse</th>\n",
       "      <th>min_acc</th>\n",
       "      <th>max_acc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>baseline-NB</td>\n",
       "      <td>MultinomialNB()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.8290</td>\n",
       "      <td>0.021071</td>\n",
       "      <td>0.785</td>\n",
       "      <td>0.855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>param-exp-NB-1</td>\n",
       "      <td>MultinomialNB()</td>\n",
       "      <td>bigram</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.8395</td>\n",
       "      <td>0.020056</td>\n",
       "      <td>0.805</td>\n",
       "      <td>0.865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>param-exp-NB-2</td>\n",
       "      <td>MultinomialNB()</td>\n",
       "      <td>trigram</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.7685</td>\n",
       "      <td>0.021337</td>\n",
       "      <td>0.745</td>\n",
       "      <td>0.805</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             name   learning_model  feature clip_counts negation  \\\n",
       "0     baseline-NB  MultinomialNB()  unigram        True    False   \n",
       "1  param-exp-NB-1  MultinomialNB()   bigram        True    False   \n",
       "2  param-exp-NB-2  MultinomialNB()  trigram        True    False   \n",
       "\n",
       "  remove_stopwords lemmatise additional_features  avg_cv_acc      rmse  \\\n",
       "0            False     False               False      0.8290  0.021071   \n",
       "1            False     False               False      0.8395  0.020056   \n",
       "2            False     False               False      0.7685  0.021337   \n",
       "\n",
       "   min_acc  max_acc  \n",
       "0    0.785    0.855  \n",
       "1    0.805    0.865  \n",
       "2    0.745    0.805  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluation_dataframes['MultinomialNB']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We can see above that the Bag-of-Bigrams is the best feature representation to use as input for the baseline model.  \n",
    "### However, in order to effectively test the rest of our features, we will continue using the unigram feature representation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Experiment 3: Clip Counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The baseline model has a clip counts feature which limits the numbers in the feature matrix to either 0 or 1. Below we test the effect of disabling this feature and allowing the count of the terms in the matrix be greater than 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating fold 1 of 10\n",
      "--> 0.75\n",
      "Evaluating fold 2 of 10\n",
      "--> 0.84\n",
      "Evaluating fold 3 of 10\n",
      "--> 0.83\n",
      "Evaluating fold 4 of 10\n",
      "--> 0.805\n",
      "Evaluating fold 5 of 10\n",
      "--> 0.82\n",
      "Evaluating fold 6 of 10\n",
      "--> 0.8\n",
      "Evaluating fold 7 of 10\n",
      "--> 0.8\n",
      "Evaluating fold 8 of 10\n",
      "--> 0.82\n",
      "Evaluating fold 9 of 10\n",
      "--> 0.77\n",
      "Evaluating fold 10 of 10\n",
      "--> 0.815\n"
     ]
    }
   ],
   "source": [
    "feature = 'unigram'\n",
    "clip_counts = False\n",
    "model = PolarityPredictor(clip_counts, negation, remove_stopwords, lemmatise, additional_features, learning_model)\n",
    "avg, rmse, min_acc, max_acc = evaluate_model(model, splits, feature, verbose = True)\n",
    "evaluation_dataframes['MultinomialNB'].loc[evaluation_dataframes['MultinomialNB'].index.max()+1] = [f'param-exp-NB-{index}', learning_model, feature, clip_counts, \n",
    "                                              negation, remove_stopwords, lemmatise, additional_features, avg, rmse, min_acc, max_acc]\n",
    "index += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Comparison of baseline model with and without clipped counts (RMSE)'}>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "clip_counts_comparison = evaluation_dataframes['MultinomialNB'].iloc[[0, 3],]\n",
    "clip_counts_comparison = clip_counts_comparison[[\"name\", \"avg_cv_acc\", \"min_acc\", \"max_acc\"]]\n",
    "graph = clip_counts_comparison.T\n",
    "graph_header = graph.iloc[0]\n",
    "graph = graph[1:]\n",
    "graph.columns = graph_header\n",
    "graph.plot(kind='bar', figsize=(10,5), title=\"Comparison of baseline model with and without clipped counts\")\n",
    "clip_counts_comparison_rmse = evaluation_dataframes['MultinomialNB'].iloc[[0, 3],]\n",
    "clip_counts_comparison_rmse = clip_counts_comparison_rmse[[\"name\", \"rmse\"]]\n",
    "graph = clip_counts_comparison_rmse.T\n",
    "graph_header = graph.iloc[0]\n",
    "graph = graph[1:]\n",
    "graph.columns = graph_header\n",
    "graph.plot(kind='bar', figsize=(10,5), title=\"Comparison of baseline model with and without clipped counts (RMSE)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>learning_model</th>\n",
       "      <th>feature</th>\n",
       "      <th>clip_counts</th>\n",
       "      <th>negation</th>\n",
       "      <th>remove_stopwords</th>\n",
       "      <th>lemmatise</th>\n",
       "      <th>additional_features</th>\n",
       "      <th>avg_cv_acc</th>\n",
       "      <th>rmse</th>\n",
       "      <th>min_acc</th>\n",
       "      <th>max_acc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>baseline-NB</td>\n",
       "      <td>MultinomialNB()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.8290</td>\n",
       "      <td>0.021071</td>\n",
       "      <td>0.785</td>\n",
       "      <td>0.855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>param-exp-NB-1</td>\n",
       "      <td>MultinomialNB()</td>\n",
       "      <td>bigram</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.8395</td>\n",
       "      <td>0.020056</td>\n",
       "      <td>0.805</td>\n",
       "      <td>0.865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>param-exp-NB-2</td>\n",
       "      <td>MultinomialNB()</td>\n",
       "      <td>trigram</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.7685</td>\n",
       "      <td>0.021337</td>\n",
       "      <td>0.745</td>\n",
       "      <td>0.805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>param-exp-NB-3</td>\n",
       "      <td>MultinomialNB()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.8050</td>\n",
       "      <td>0.025884</td>\n",
       "      <td>0.750</td>\n",
       "      <td>0.840</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             name   learning_model  feature clip_counts negation  \\\n",
       "0     baseline-NB  MultinomialNB()  unigram        True    False   \n",
       "1  param-exp-NB-1  MultinomialNB()   bigram        True    False   \n",
       "2  param-exp-NB-2  MultinomialNB()  trigram        True    False   \n",
       "3  param-exp-NB-3  MultinomialNB()  unigram       False    False   \n",
       "\n",
       "  remove_stopwords lemmatise additional_features  avg_cv_acc      rmse  \\\n",
       "0            False     False               False      0.8290  0.021071   \n",
       "1            False     False               False      0.8395  0.020056   \n",
       "2            False     False               False      0.7685  0.021337   \n",
       "3            False     False               False      0.8050  0.025884   \n",
       "\n",
       "   min_acc  max_acc  \n",
       "0    0.785    0.855  \n",
       "1    0.805    0.865  \n",
       "2    0.745    0.805  \n",
       "3    0.750    0.840  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluation_dataframes['MultinomialNB']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clipped counts work marginally better with the unigram feature representation. This makes sense as the learning model can identify differences within the data easier given the data is binary."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Experiment 4: Remove Stopwords"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also created a parameter in our model for removing stop words. The list of stop words which we used is from the English stop word list accessed through NLTK. These are shown below.\n",
    "\n",
    "Stop words are removed from the data in the *remove_stopwords_from_data* function. This function works by storing the indices of stop words when iterating through each sentence and then removing these from the list of sentence tokens."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'what', 'by', 'off', \"mustn't\", 'they', 'with', \"couldn't\", 'it', 'so', \"should've\", 'that', \"you'd\", 'below', \"shouldn't\", 'between', 'out', 'mightn', 'do', \"you've\", 've', 'how', 'few', 'did', 'hers', \"haven't\", 'doesn', 'then', 't', \"wouldn't\", \"you'll\", \"won't\", 'yours', 'should', 'he', 'my', 'of', 'you', 'shouldn', \"needn't\", 'isn', \"you're\", 'been', 'doing', 'those', 'has', 'yourselves', 'the', 'against', 'me', 'ours', \"hadn't\", 'about', 'll', 'from', 'an', 're', 'both', 'ain', 'will', 'their', 'weren', 'your', \"doesn't\", 'wouldn', 'd', 'were', 'over', \"that'll\", 'does', 'into', \"didn't\", \"she's\", 'is', 'aren', \"wasn't\", 'under', 'only', 'and', 'in', 'for', 'her', 'each', \"it's\", 'because', 'why', 'had', 'most', 'don', \"hasn't\", \"mightn't\", 'needn', 'herself', 'to', 'have', 'once', 'who', 'here', 'him', 'yourself', 'before', \"don't\", 'a', 'but', \"aren't\", 'itself', 'some', 'same', 'couldn', 'while', 'be', 'own', 'nor', 'than', 'mustn', \"shan't\", 'through', 'all', 'haven', 'up', 'himself', 'down', 'where', 'other', 'this', 'as', 'during', 'hadn', 'y', 'shan', 'which', 'hasn', 'them', 's', 'am', 'again', 'his', 'theirs', 'at', 'being', 'if', 'these', 'such', 'very', 'having', 'just', 'can', 'our', 'when', 'not', 'wasn', 'its', 'further', 'was', 'now', 'too', 'ma', 'whom', 'myself', 'themselves', 'more', 'are', 'she', 'o', 'there', 'm', 'until', \"isn't\", 'after', 'above', 'i', 'on', 'ourselves', 'any', \"weren't\", 'didn', 'or', 'we', 'no', 'won'}\n"
     ]
    }
   ],
   "source": [
    "print(set(nltk.corpus.stopwords.words('english')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will inspect the effect of removing these stopwords from the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating fold 1 of 10\n",
      "--> 0.815\n",
      "Evaluating fold 2 of 10\n",
      "--> 0.84\n",
      "Evaluating fold 3 of 10\n",
      "--> 0.85\n",
      "Evaluating fold 4 of 10\n",
      "--> 0.81\n",
      "Evaluating fold 5 of 10\n",
      "--> 0.84\n",
      "Evaluating fold 6 of 10\n",
      "--> 0.835\n",
      "Evaluating fold 7 of 10\n",
      "--> 0.865\n",
      "Evaluating fold 8 of 10\n",
      "--> 0.85\n",
      "Evaluating fold 9 of 10\n",
      "--> 0.775\n",
      "Evaluating fold 10 of 10\n",
      "--> 0.86\n"
     ]
    }
   ],
   "source": [
    "clip_counts = True\n",
    "remove_stopwords = True\n",
    "model = PolarityPredictor(clip_counts, negation, remove_stopwords, lemmatise, additional_features, learning_model)\n",
    "avg, rmse, min_acc, max_acc = evaluate_model(model, splits, feature, verbose = True)\n",
    "evaluation_dataframes['MultinomialNB'].loc[evaluation_dataframes['MultinomialNB'].index.max()+1] = [f'param-exp-NB-{index}', learning_model, feature, clip_counts, \n",
    "                                              negation, remove_stopwords, lemmatise, additional_features, avg, rmse, min_acc, max_acc]\n",
    "index += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Comparison of baseline model with and without stop words removed (RMSE)'}>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stop_words_comparison = evaluation_dataframes['MultinomialNB'].iloc[[0, 4],]\n",
    "stop_words_comparison = stop_words_comparison[[\"name\", \"avg_cv_acc\", \"min_acc\", \"max_acc\"]]\n",
    "graph = stop_words_comparison.T\n",
    "graph_header = graph.iloc[0]\n",
    "graph = graph[1:]\n",
    "graph.columns = graph_header\n",
    "graph.plot(kind='bar', figsize=(10,5), title=\"Comparison of baseline model with and without stop words removed\")\n",
    "clip_counts_comparison_rmse = evaluation_dataframes['MultinomialNB'].iloc[[0, 4],]\n",
    "clip_counts_comparison_rmse = clip_counts_comparison_rmse[[\"name\", \"rmse\"]]\n",
    "graph = clip_counts_comparison_rmse.T\n",
    "graph_header = graph.iloc[0]\n",
    "graph = graph[1:]\n",
    "graph.columns = graph_header\n",
    "graph.plot(kind='bar', figsize=(10,5), title=\"Comparison of baseline model with and without stop words removed (RMSE)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>learning_model</th>\n",
       "      <th>feature</th>\n",
       "      <th>clip_counts</th>\n",
       "      <th>negation</th>\n",
       "      <th>remove_stopwords</th>\n",
       "      <th>lemmatise</th>\n",
       "      <th>additional_features</th>\n",
       "      <th>avg_cv_acc</th>\n",
       "      <th>rmse</th>\n",
       "      <th>min_acc</th>\n",
       "      <th>max_acc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>baseline-NB</td>\n",
       "      <td>MultinomialNB()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.8290</td>\n",
       "      <td>0.021071</td>\n",
       "      <td>0.785</td>\n",
       "      <td>0.855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>param-exp-NB-1</td>\n",
       "      <td>MultinomialNB()</td>\n",
       "      <td>bigram</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.8395</td>\n",
       "      <td>0.020056</td>\n",
       "      <td>0.805</td>\n",
       "      <td>0.865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>param-exp-NB-2</td>\n",
       "      <td>MultinomialNB()</td>\n",
       "      <td>trigram</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.7685</td>\n",
       "      <td>0.021337</td>\n",
       "      <td>0.745</td>\n",
       "      <td>0.805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>param-exp-NB-3</td>\n",
       "      <td>MultinomialNB()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.8050</td>\n",
       "      <td>0.025884</td>\n",
       "      <td>0.750</td>\n",
       "      <td>0.840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>param-exp-NB-4</td>\n",
       "      <td>MultinomialNB()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.8340</td>\n",
       "      <td>0.025768</td>\n",
       "      <td>0.775</td>\n",
       "      <td>0.865</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             name   learning_model  feature clip_counts negation  \\\n",
       "0     baseline-NB  MultinomialNB()  unigram        True    False   \n",
       "1  param-exp-NB-1  MultinomialNB()   bigram        True    False   \n",
       "2  param-exp-NB-2  MultinomialNB()  trigram        True    False   \n",
       "3  param-exp-NB-3  MultinomialNB()  unigram       False    False   \n",
       "4  param-exp-NB-4  MultinomialNB()  unigram        True    False   \n",
       "\n",
       "  remove_stopwords lemmatise additional_features  avg_cv_acc      rmse  \\\n",
       "0            False     False               False      0.8290  0.021071   \n",
       "1            False     False               False      0.8395  0.020056   \n",
       "2            False     False               False      0.7685  0.021337   \n",
       "3            False     False               False      0.8050  0.025884   \n",
       "4             True     False               False      0.8340  0.025768   \n",
       "\n",
       "   min_acc  max_acc  \n",
       "0    0.785    0.855  \n",
       "1    0.805    0.865  \n",
       "2    0.745    0.805  \n",
       "3    0.750    0.840  \n",
       "4    0.775    0.865  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluation_dataframes['MultinomialNB']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We can see a higher maximum accuracy when stopwords are removed, however we also see a lower minimum accuracy. The increase in range suggests that there may be some words within the stopword corpus which hold meaning. Overall we have achieved the second highest average accuracy so far."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Experiment 5: Addition of negation to data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another option which can be toggled when running the model is whether to negate the data or not. Negation is performed in the *add_negation_to_data* function. This function essentially checks if a token is \"not\", \"no\" or ends in \"n't\". When one of these tokens is found, each token following this has \"NOT_\" prefixed to it until the next full stop in the data. This is a simple but effective way to perform negation.\n",
    "\n",
    "In the aforementioned function, a vector is also created, called *doc2negate*, which stores the number of negated words in each document. This will be used later on to create additional features. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating fold 1 of 10\n",
      "--> 0.815\n",
      "Evaluating fold 2 of 10\n",
      "--> 0.84\n",
      "Evaluating fold 3 of 10\n",
      "--> 0.855\n",
      "Evaluating fold 4 of 10\n",
      "--> 0.805\n",
      "Evaluating fold 5 of 10\n",
      "--> 0.83\n",
      "Evaluating fold 6 of 10\n",
      "--> 0.83\n",
      "Evaluating fold 7 of 10\n",
      "--> 0.85\n",
      "Evaluating fold 8 of 10\n",
      "--> 0.845\n",
      "Evaluating fold 9 of 10\n",
      "--> 0.79\n",
      "Evaluating fold 10 of 10\n",
      "--> 0.855\n"
     ]
    }
   ],
   "source": [
    "remove_stopwords = False\n",
    "negation = True\n",
    "model = PolarityPredictor(clip_counts, negation, remove_stopwords, lemmatise, additional_features, learning_model)\n",
    "avg, rmse, min_acc, max_acc = evaluate_model(model, splits, feature, verbose = True)\n",
    "evaluation_dataframes['MultinomialNB'].loc[evaluation_dataframes['MultinomialNB'].index.max()+1] = [f'param-exp-NB-{index}', learning_model, feature, clip_counts, \n",
    "                                              negation, remove_stopwords, lemmatise, additional_features, avg, rmse, min_acc, max_acc]\n",
    "index += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Comparison of baseline model with and without negation (RMSE)'}>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "negation_comparison = evaluation_dataframes['MultinomialNB'].iloc[[0, 5],]\n",
    "negation_comparison = negation_comparison[[\"name\", \"avg_cv_acc\", \"min_acc\", \"max_acc\"]]\n",
    "graph = negation_comparison.T\n",
    "graph_header = graph.iloc[0]\n",
    "graph = graph[1:]\n",
    "graph.columns = graph_header\n",
    "graph.plot(kind='bar', figsize=(10,5), title=\"Comparison of baseline model with and without negation\")\n",
    "negation_comparison_rmse = evaluation_dataframes['MultinomialNB'].iloc[[0, 5],]\n",
    "negation_comparison_rmse = negation_comparison_rmse[[\"name\", \"rmse\"]]\n",
    "graph = clip_counts_comparison_rmse.T\n",
    "graph_header = graph.iloc[0]\n",
    "graph = graph[1:]\n",
    "graph.columns = graph_header\n",
    "graph.plot(kind='bar', figsize=(10,5), title=\"Comparison of baseline model with and without negation (RMSE)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>learning_model</th>\n",
       "      <th>feature</th>\n",
       "      <th>clip_counts</th>\n",
       "      <th>negation</th>\n",
       "      <th>remove_stopwords</th>\n",
       "      <th>lemmatise</th>\n",
       "      <th>additional_features</th>\n",
       "      <th>avg_cv_acc</th>\n",
       "      <th>rmse</th>\n",
       "      <th>min_acc</th>\n",
       "      <th>max_acc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>baseline-NB</td>\n",
       "      <td>MultinomialNB()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.8290</td>\n",
       "      <td>0.021071</td>\n",
       "      <td>0.785</td>\n",
       "      <td>0.855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>param-exp-NB-1</td>\n",
       "      <td>MultinomialNB()</td>\n",
       "      <td>bigram</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.8395</td>\n",
       "      <td>0.020056</td>\n",
       "      <td>0.805</td>\n",
       "      <td>0.865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>param-exp-NB-2</td>\n",
       "      <td>MultinomialNB()</td>\n",
       "      <td>trigram</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.7685</td>\n",
       "      <td>0.021337</td>\n",
       "      <td>0.745</td>\n",
       "      <td>0.805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>param-exp-NB-3</td>\n",
       "      <td>MultinomialNB()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.8050</td>\n",
       "      <td>0.025884</td>\n",
       "      <td>0.750</td>\n",
       "      <td>0.840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>param-exp-NB-4</td>\n",
       "      <td>MultinomialNB()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.8340</td>\n",
       "      <td>0.025768</td>\n",
       "      <td>0.775</td>\n",
       "      <td>0.865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>param-exp-NB-5</td>\n",
       "      <td>MultinomialNB()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.8325</td>\n",
       "      <td>0.021243</td>\n",
       "      <td>0.790</td>\n",
       "      <td>0.855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>param-exp-NB-6</td>\n",
       "      <td>MultinomialNB()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.8315</td>\n",
       "      <td>0.020982</td>\n",
       "      <td>0.790</td>\n",
       "      <td>0.855</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             name   learning_model  feature clip_counts negation  \\\n",
       "0     baseline-NB  MultinomialNB()  unigram        True    False   \n",
       "1  param-exp-NB-1  MultinomialNB()   bigram        True    False   \n",
       "2  param-exp-NB-2  MultinomialNB()  trigram        True    False   \n",
       "3  param-exp-NB-3  MultinomialNB()  unigram       False    False   \n",
       "4  param-exp-NB-4  MultinomialNB()  unigram        True    False   \n",
       "5  param-exp-NB-5  MultinomialNB()  unigram        True     True   \n",
       "6  param-exp-NB-6  MultinomialNB()  unigram        True     True   \n",
       "\n",
       "  remove_stopwords lemmatise additional_features  avg_cv_acc      rmse  \\\n",
       "0            False     False               False      0.8290  0.021071   \n",
       "1            False     False               False      0.8395  0.020056   \n",
       "2            False     False               False      0.7685  0.021337   \n",
       "3            False     False               False      0.8050  0.025884   \n",
       "4             True     False               False      0.8340  0.025768   \n",
       "5            False     False               False      0.8325  0.021243   \n",
       "6            False     False               False      0.8315  0.020982   \n",
       "\n",
       "   min_acc  max_acc  \n",
       "0    0.785    0.855  \n",
       "1    0.805    0.865  \n",
       "2    0.745    0.805  \n",
       "3    0.750    0.840  \n",
       "4    0.775    0.865  \n",
       "5    0.790    0.855  \n",
       "6    0.790    0.855  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluation_dataframes['MultinomialNB']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We only see a marginal difference when negation is added to the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Experiment 6: Lemmatisation of data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also included a lemmatisation step which can be toggled when running the model. This step essentially converts each word in the vocabulary to its base form. The lemmatiser which we opted to use is [Wordnet](https://wordnet.princeton.edu/), which is a large, freely available lexical database for the English language. This lemmatiser was downloaded and accessed through the NLTK toolkit. It is implemented in the *lemmatise_data* function.\n",
    "\n",
    "As a second argument to the lemmatiser, we also pass the Wordnet POS tag for the word. We find the [Penn POS tag](https://www.ling.upenn.edu/courses/Fall_2003/ling001/penn_treebank_pos.html) for each word using *nltk.pos_tag(word)* in the *get_pos_tags* function. This tagged data is then passed to the *lemmatise_data* function where the tags are then mapped to the much simpler Wornet POS tags before being passed into the lemmatiser with each word."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating fold 1 of 10\n",
      "--> 0.83\n",
      "Evaluating fold 2 of 10\n",
      "--> 0.835\n",
      "Evaluating fold 3 of 10\n",
      "--> 0.825\n",
      "Evaluating fold 4 of 10\n",
      "--> 0.795\n",
      "Evaluating fold 5 of 10\n",
      "--> 0.805\n",
      "Evaluating fold 6 of 10\n",
      "--> 0.82\n",
      "Evaluating fold 7 of 10\n",
      "--> 0.84\n",
      "Evaluating fold 8 of 10\n",
      "--> 0.825\n",
      "Evaluating fold 9 of 10\n",
      "--> 0.78\n",
      "Evaluating fold 10 of 10\n",
      "--> 0.835\n"
     ]
    }
   ],
   "source": [
    "negation = False\n",
    "lemmatise = True\n",
    "model = PolarityPredictor(clip_counts, negation, remove_stopwords, lemmatise, additional_features, learning_model)\n",
    "avg, rmse, min_acc, max_acc = evaluate_model(model, splits, feature, verbose = True)\n",
    "evaluation_dataframes['MultinomialNB'].loc[evaluation_dataframes['MultinomialNB'].index.max()+1] = [f'param-exp-NB-{index}', learning_model, feature, clip_counts, \n",
    "                                              negation, remove_stopwords, lemmatise, additional_features, avg, rmse, min_acc, max_acc]\n",
    "index += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Comparison of baseline model with and without lemmatisation (RMSE)'}>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlMAAAFuCAYAAAClTZ0GAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAyGUlEQVR4nO3debwWdd3/8dfngLiXJrggsmhaqOCGW1lIKamZlEsuWWE/NU2y7c5sudNSS+vOyrS4tQxNCzPvlJRyXzI1QcMFCUVBRUxwRQQF9PP745qDF4dz4OLMBdfh8Ho+HufBNTPfa+Yzy7nOm/nONROZiSRJktqnqdEFSJIkrcoMU5IkSSUYpiRJkkowTEmSJJVgmJIkSSrBMCVJklSCYUodWkR8KiJuaHQdzSJi7Yj4S0S8EhFXtjL99Ii4bCXXNCoizixefyAiJq/M5ddDRNwWEcfW2DYj4t0llzcxIvauRz31FhHTImKfkvP4a0R8dinTFx0zK1sjfkfqJSLmRMSWq8p8tfIYplYTEXFURIwvfmmfLT5s92p0XcuSmZdn5tBG11HlUGATYKPMPKzRxbSUmX/PzPc0uo6OLjO3y8zbYNX+496WzNw/My8BiIjhEXHnylhuRPQtwm7XlbG8Fam1QJ2Z62XmEx1xvmosw9RqICK+CvwM+AGVINAb+CUwrIFlLVMH/UDuAzyamQsbXYgkqWMwTHVyEfFO4PvASZn5f5n5WmYuyMy/ZObXizZrRsTPImJG8fOziFizmLZ3REyPiFMiYmZxVuvjEXFARDwaES9GxLeqlnd6RPwpIq6IiFcj4v6I2KFq+qkR8Xgx7ZGI+ETVtOER8Y+I+GlEvAicXv2/6qj4aVHHKxHxYERs37yeEXFpRMyKiCcj4jsR0VQ13zsj4n8i4qWImBoR+y9lm/Uv/vf4ctEddFAx/nvAd4HDizN8/6+NWazVzvV/d0TcXqzb8xFxRdW090bEjcX2nhwRn2yj9r0jYnrV8LSI+K9iW71S1LVW1fQDI2JCsa53RcTApWyXjIgvRMRjRf1nRMRWEXF3RMyOiD9GRLeq9sdFxJSi5jER0bNq2r4R8e+ipvOBaLGsz0XEpGJ/XR8Rfdqqq+o9QyLioarhmyLi3qrhOyPi41XbZZ+I2A/4Fm/v0weqZtmnOB5fjYgbIqJ7G8vdMCKuLY69l4rXvaqm31Zsq1bnFRGfLo7ZFyLi20tZv37Ffmo+rn8dETOrpl8WEV+uWuaxEdEfGAnsWazfy1Wz3DAiritq+mdEbFU1r/dFxLhi/4yLiPdVTVusGzIWP7N3R/Hvy8Xy9mxrfarev0dx7L0cEQ9EVfdrsR5nFtPnRKWLfaOIuLw45sZFRN+q9jUfo0vbbxFxFvAB4PxiuedXzf/dxesDovI7/GpEPBMR/1Wn+dbts0wrUWb604l/gP2AhUDXpbT5PnAPsDHQA7gLOKOYtnfx/u8CawDHAbOA3wPrA9sBrwNbFu1PBxZQ6Q5bA/gvYCqwRjH9MKAnlSB/OPAasFkxbXixrC8CXYG1i3F3FtM/AtwHbEDlj2//qvdeClxT1NQXeBT4f1XzXVDU3gU4EZgBRCvbYg1gCpU/sN2ADwGvAu+pWr/LlrIty6z/H4BvF9PWAvYqxq8LPA0cU2yXnYHnge2K6aOAM6v21/SqeqYB9xbLfBcwCTihmLYzMBPYvdguny3ar9nGuiUwBnhHsd/fAG4GtgTeCTwCfLZo+6Gixp2BNYFfAHcU07oDs6u20VeK/X5sMf3jxT7oX6zvd4C7WtTx7lbqWwuYV8y/K/CfYj+vT+VYmkele7Z5u+zT1j4FbgMeB7Yp3nsbcHYb22Uj4BBgnWJZVwJX1zIvYFtgDvDBYjudW2yLfdpY1lPALsXrycATQP+qaTtVLbN5ew6n+B2qms8o4EVgt2JbXQ6MLqa9C3gJ+HQx7chieIlt13L7UfndS5b+eVPdfnPgBeAAKsf9vsVwj6r1mAJsxdvH2KPAPkVtlwK/becxWst+O7aV34F3F6+fBT5QvN4Q2LlO863LZ5k/K/fHM1Od30bA87n0bqlPAd/PzJmZOQv4HpUP0mYLgLMycwEwmsofq59n5quZORGYCFSf0bgvM/9UtD+Xyh+5PQAy88rMnJGZb2XmFcBjVD7Qm83IzF9k5sLMnNeizgVUPmDeS+XDY1JmPhsRXagEk28WNU0DftJiHZ7MzIsy803gEmAzKl2eLe0BrEflj938zLwFuJbKH5RatXf9F1DpRuyZma9nZvN1LgcC0zLzt8V2uR+4ikoYqcV5xTJfBP4C7FiMPw7438z8Z2a+mZVrbN5orrUN52Tm7GK/PwzckJlPZOYrwF+BnYp2nwIuzsz7M/MN4JtUzo70pfKH85GqbfQzKsGn2eeBHxb7dyGV7ukdYxlnpzLzdWA8lWAyCHgQuBN4f7FOj2XmC0vfVIv5bWY+WhyHf+Tt7dZyuS9k5lWZOTczXwXOAgbXOK9DgWsz845iO/038NZSarodGBwRmxbDfyqG+1EJEA+0+c4l/V9m3lts48uravoolW31u+J4+wPwb+BjyzHvWh0NjM3MscXvxI1U9uEBVW1+m5mPVx1jj2fmTUXdV/L2MdespmO0xv22NAuAbSPiHZn5UvF7WWq+df4s00pkmOr8XgC6x9KvP+oJPFk1/GQxbtE8il9cqPzvHuC5qunzqASQZk83v8jMt4DpzfOLiM/E291KLwPbUwlnS7y3pSLYnA9cADwXERdGxDuK93drZR02rxr+T9V85hYvq2tu1hN4uqi7rXktS3vX/xQqZ9zujUr34ueK8X2A3ZvfU7zvU0DzH9RlqQ4qc3l7vfsAX2sx3y1YfN+31HK/t3UcLHZMZeYcKsfi5sW06m2ULL7f+wA/r6rpRSrbpZZ9cDuVs3MfLF7fRuUP2eBieHm0td0WExHrRMT/Fl0ys6l0dW1Q/GFc1rxabovXqGyntlSv3x0svn5/b3HcLsvSanqyRdvl/R2oVR/gsBbH4F5UAkKzWo+55Wpf435bmkOohL4no9I9v2cd5lvPzzKtRIapzu9uKt1wH19KmxlUPtSa9S7GtdcWzS+Kvv5ewIzizMJFwAgqXQYbUPmfY/X1Mrm0GWfmeZm5C5VT+NsAX6fSndR8Vqd6HZ5pR+0zgC2ar1Fo57zatf6Z+Z/MPC4ze1I5O/PL4jqKp4HbM3ODqp/1MvPEdqxftaepnHGsnu86xZmIshY7piJiXSpnSZ+h0j1SvY2ierio6/Mt6lo7M++qYbktw9TtLDtMLfWYq8HXgPcAu2fmO4plQ4vrwNrQclusQ2U7teV2Ktfc7F28bj7zVs/1a/l5AIv/DrxGpQurWXWoX95lPQ38rsW+Xjczz17O+bTHsvbbsj6LxmXmMCqXR1xN5Yxj2fnW87NMK5FhqpMrTm1/F7ggKheOrxMRa0TE/hHxo6LZH4DvRESPqFwY+12gzFfFd4mIg4uzYV+m0nV0D5Vrf5LKNVdExDFUzszUJCJ2jYjdI2INKh/orwNvFmfN/gicFRHrF6Hlq+1ch38W8z6l2E57U+neGL0c82jX+kfEYfH2hcsvFW3fpNLNuE1ULlReo/jZNSoXF5dxEXBCsU0jItaNiI9GxPol5wuVa+qOiYgdo/Jlhh8A/yy6La4DtqvaRiez+B/kkcA3I2I7WHRBbq23obiLyh+y3YB7i66ePlSuC7ujjfc8B/RtEaCXx/pUzni8HBHvAk5bjvf+CTgwIvaKyoXR32cpn8uZ+VixrKOpXIM2u6j/ENoOU88BvaLqywHLMJbK8XZURHSNiMOpXNt1bTF9AnBEcRwOYvHu5llUuilrvWfSZcDHIuIjEdElItaKypcoei3zneUta789RxvrERHdonIPvHcWXdWzqfyulppvnT/LtBIZplYDmXkulV/I71D5sHuaytmRq4smZ1K5TuFB4CHg/mJce11Dpd+/+SLWg7PyDcJHqPT/303lA2UA8I/lmO87qASAl6ic+n4B+J9i2hephKAnqPxv/ffAxctbeGbOBw4C9qfyv8RfAp/JzH8vx2zau/67Av+MiDlULqL9UmZOLa67GAocQeWswX+Ac6hcsNxumTmeynVT5xe1TqFygWtpmXkzlet/rqJy9mUrKvWTmc9TuRD/bCr7cGuqtkNm/pnK+o0uukkeprI/alnua1SO34nFvoTK9n4yM2e28bbmm6++EBH317qOVX5G5cLy56mE5r/V+sYi7J1E5Xh9lsp+mL7UN1VC0wuZ+VTVcAD/aqP9LVSua/xPRDxfQ00vULlO72tU9s8pwIHFfoPKft2qqPV7Re3N751L5RqhfxTddku7/o7MfJrKLVq+xdufTV9n5fxt+hlL328/Bw6Nyrfmzmvl/Z8GphXH6AlUAm495luXzzKtXFG5XEGqj4g4ncq3Uo5eVltJkjoDz0xJkiSVYJiSJEkqwW4+SZKkEjwzJUmSVIJhSpIkqYSl3RV7herevXv27du3UYuXJEmq2X333fd8ZvZobVrDwlTfvn0ZP358oxYvSZJUs4ho+ZilRezmkyRJKsEwJUmSVIJhSpIkqYSGXTMlSdKqbsGCBUyfPp3XX3+90aWoTtZaay169erFGmusUfN7DFOSJLXT9OnTWX/99enbty8R0ehyVFJm8sILLzB9+nT69etX8/vs5pMkqZ1ef/11NtpoI4NUJxERbLTRRst9ptEwJUlSCQapzqU9+9MwJUmSVIJhSpIkqQTDlCRJq5lp06bRv39/jjvuOLbbbjuGDh3KvHnzuOiii9h1113ZYYcdOOSQQ5g7dy4Aw4cP58QTT2TIkCFsueWW3H777Xzuc5+jf//+DB8+fNF8b7jhBvbcc0923nlnDjvsMObMmdOgNVy5DFOSJK2GHnvsMU466SQmTpzIBhtswFVXXcXBBx/MuHHjeOCBB+jfvz+/+c1vFrV/6aWXuOWWW/jpT3/Kxz72Mb7yla8wceJEHnroISZMmMDzzz/PmWeeyU033cT999/PoEGDOPfccxu4hiuPt0aQJK22+p56Xan3X3TQZiyY/nJ9ilkOA5umlpvBczPot0VPdtw4Yca/2GXrzZj24N08vPbrfOdHF/Dy7DnMeW0uHxm8J8zYC+a+yMc+uDvx7AQGbNzEJhttwICNFsJ/HmC7fpsx7f5bmP5AFx55+CHev9vOAMxfsIA9dxkIM/61+LJ77lSu9g7IMCVJ0mpozTW7LXrdpUsT815fyPCvnMbVvzmXHbbbhlFXjOG2u+97u323SvumpqbF3tvU1MTChW/SpamJfT+4O3/45Q9X3kp0EHbzSZIkAF6dM5fNNunOggULuPzPf12u9+6xy0D+Me4Bpkx9CoC58+bx6ONProgyOxzPTEmSJADO+PqJ7H7gZ+jTazMGvPfdvDpnbs3v7bHRhoz66ekcedK3eGP+fADOPOUkttmqz4oqt8OIzGzIggcNGpTjx49vyLIlSYL6XDO1Se8t61RN7UpfM9VIq8A1U5MmTaJ///6LjYuI+zJzUGvt7eaTJEkqwTAlSZJUgtdMrU5Of2ejK2i/019pdAWSJLXKMNUOZfvYG2XaWo2uQJKkzsduPkmSpBIMU5IkSSUYpiRJWoVNe3oG23/osBUy79vuGs+BnzkZgDE33M7Z5/+2LvMdNWoUTU1NPPjgg4vGbb/99kybNg2Avn37MmDAAHbccUcGDBjANddcU5flriheMyVJUp0cdP4/6jq/MSPeX9f5lXHQ0MEcNHRw3ebXq1cvzjrrLK644opWp9966610796dyZMnM3ToUIYNG1a3ZddbTWemImK/iJgcEVMi4tRWpr8zIv4SEQ9ExMSIOKb+pUqSpNYsXPgmn/3Sdxm4zyc59LivM3fePL7/0wvZ9YCj2f5Dh3H8KWfQfJPu837zB7bd+xAG7vNJjjix8if9tbnz+NxXT2fXA45mp6FHcs31ty2xjFFXjGHEt88GYPiXT+Pk//4R7ztoOFvu+TH+dO1Ni9r9+FeXsOsBRzNwn09y2v/8qs2aDzzwQCZOnMjkyZOXum6zZ89mww03XM4tsnItM0xFRBfgAmB/YFvgyIjYtkWzk4BHMnMHYG/gJxHRDUmStMJNfnwaxx99MA/e9Efesf66/PKSKxkx/HDGjb2Mh2+5knnz3uDaG+8A4OwLfsu/rv8DD970R0ae/W0Azvr5r/nQ+3dl3NjLuPXKC/n6GT/jtbnzlrrMZ597njuvvphrL/k5p/7wPABuuP1uHpv6FPde9zsm3DCa+x6cxB333Nfq+5uamjjllFP4wQ9+0Or0IUOGsP322zN48GDOPPPM9m6alaKWM1O7AVMy84nMnA+MBlqea0tg/YgIYD3gRWBhXSuVJEmt2qLnprx/1x0BOPrgA7jz3gncetd4dj/wMwz48Ce55a5xTHz0CQAG9t+aT434NpdddR1du3YB4IY77uHsC0ax475HsPehx/H6G/N56plnl7rMj++3N01NTWy7zZY8N+vFynxuv4cbbr+HnYYeyc4fOYp/Pz6Nx6Y+3eY8jjrqKO655x6mTl3y8Ti33norDz/8MA899BAjRoxgzpw57dk0K0Ut10xtDlRvienA7i3anA+MAWYA6wOHZ+ZbdalQkiQtVUTL4eAL3/oh48dexhabb8rpPxnJ62+8AcB1l57HHffcz5gbbueMn/2aibdeSWZy1YU/5j3v7rvYfJpDUmvW7PZ2B1RzF2Jm8s0Rx/D5Tx+6WNsLRl3BRZf/GYCxN966aHzXrl352te+xjnnnNPmcrbaais22WQTHnnkEXbbbbe2N0ID1XJmKloZ1/LpyB8BJgA9gR2B8yPiHUvMKOL4iBgfEeNnzZq1nKVKkqTWPPXMf7h7/AMA/OGa69mrOEvV/V0bMOe1ufzpupsBeOutt3h6xnMMef+u/Og7X+Ll2a8y57V5fGTwnvzit6MXhaJ/PfzvdtXxkb335OIrxjDntbkAPPPsTGY+/yInDT+cCTeOZsKNo+nZs+di7xk+fDg33XQTbeWCmTNnMnXqVPr06dOumlaGWs5MTQe2qBruReUMVLVjgLOzshemRMRU4L3AvdWNMvNC4EKAQYMGtQxkkiSpHfpv3Y9LrryWz596Flv3682Jnz2Ul16ZzYB9PknfXj3ZdYfKpc5vvvkmR3/xO7zy6hwyk68c9yk2eOf6/PeXj+PLp/0PA/c5nMykb6/NuPbS85a7jqGD92TSY1PZ86DhAKy3ztpc9osz2bj7u9p8T7du3Tj55JP50pe+tNj4IUOG0KVLFxYsWMDZZ5/NJptsstz1rCzRnELbbBDRFXgU+DDwDDAOOCozJ1a1+RXwXGaeHhGbAPcDO2Tm823Nd9CgQTl+/Pg6rMLKt+o+TuaoRpfQfj6bT9IKUPbz/KKDNmOT3lvWqZraDWxa8hqjVUbPnRpdwTJNmjSJ/v37LzYuIu7LzEGttV/mmanMXBgRI4DrgS7AxZk5MSJOKKaPBM4ARkXEQ1S6Bb+xtCAlSSuMD/SWtJLVdNPOzBwLjG0xbmTV6xnA0PqWJqmRVt0zsI2uQNLqxsfJSJIklWCYkiRJKsEwJUmSVIJhSpIkqQTDlCRJ6tBGjRpFU1MTDz744KJx22+/PdOmTQOgb9++DBgwgB133JEBAwZwzTXXtDmv2267jR133JHtttuOwYMH16W+mr7NJ0mSlm3gr+t7l+4Hj32yrvNraeHChXTtumpEgV69enHWWWdxxRVXtDr91ltvpXv37kyePJmhQ4cybFjLxwjDyy+/zBe+8AX+9re/0bt3b2bOnFmX2laNLShJklo17ekZ7PepEey+0/b8a+K/2aZfHy497/v8z8jf8Zcb72De62/wvkED+d9zvkNEsPehx/G+XQbyj/EPcNC+g9lmy96ced5vmD9/ARtt+E4uP/8sNumxEaf/ZCRTn5rBszOf59EnnuTc077KPfc/xF9vvYvNN+3BX0b9jDXWWGOJen78q0v4419u5I358/nEfkP43n+dyJ//egsXjPojN47+Ff959lkGDx7MHXfcwd/+9jf+/Oc/88YbbzB16lSOOuooTjvttFbX88ADD+SOO+5g8uTJvOc972lze8yePZsNN9yw1Wm///3vOfjgg+nduzcAG2+8cTu2+JLs5pMkaRU3+fFpHH/0wTx40x95x/rr8stLrmTE8MMZN/YyHr7lSubNe4Nrb7xjUfuXZ8/h9qt+zddO+DR77bYT9/zlEv51wx84YthH+NEvL1nU7vEnp3PdpT/nmot/ytFf/G+GvG9XHrr5j6y91lpcd/OdS9Rxw+1389jUp7j3ut8x4YbR3PfgJO645z4+sf+H2HTjjbhg1BUcd9xxfO9732PTTTcF4N577+Xyyy9nwoQJXHnllbT1dJSmpiZOOeUUfvCDH7Q6fciQIWy//fYMHjyYM888s9U2jz76KC+99BJ77703u+yyC5deemnN23hpPDMlSdIqbouem/L+4uHGRx98AOddPJp+W2zOj351CXPnvc6LL7/Cdu/Zio8NrVwjdPhBb99ne/qzz3H4iafy7MznmT9/Af16v/0g4v2HvI811liDAf3fzZtvvcl+Q94HwID3vptpT7d8TC/ccPs93HD7Pew09EgA5sydy2NTn+aDe+zCL874Btt/+DD2eN8HOPLIIxe9Z99992WjjTYC4OCDD+bOO+9k0KBWn9rCUUcdxVlnncXUqUs+Tqe5m+/xxx/nwx/+MHvvvTfrrbfeYm0WLlzIfffdx80338y8efPYc8892WOPPdhmm22WuY2XxjAlSdIqLqLlcPCFb/2Q8WMvY4vNN+X0n4zk9TfeWDR93XXWXvT6i//9I756/NEcNHQwt901ntPP/d9F09ZcsxtQOSu0RteuRLGgpqZg4Ztv8s/7H+Lz3zgLgO9//UQyk2+OOIbPf/rQJWp85j8zaYomnnvuOd566y2ampoW1dqy9gsuuICLLroIgLFj334AS9euXfna177GOeec0+a22Gqrrdhkk0145JFHGDdu3GLz6dWrF927d2fddddl3XXX5YMf/CAPPPBA6TBlN58kSau4p575D3ePfwCAP1xzPXsVZ6m6v2sD5rw2lz9dd3Ob731l9hw237QHAJdc+ZflWu7uOw9gwo2jmXDjaA4aOpiP7L0nF18xhjmvzQXgmWdnMvP5F1m4cCHHfPV0fn/BWfTv359zzz130TxuvPFGXnzxRebNm8fVV1/N+9//fk466SQmTJjAhAkT6Nmz52LLHD58ODfddBOzZs1qtaaZM2cydepU+vTps8R8hg0bxt///ncWLlzI3Llz+ec//7nEA43bwzNTkiSt4vpv3Y9LrryWz596Flv3682Jnz2Ul16ZzYB9PknfXj3ZdYdt23zv6V/7PId9/htsvmkP9th5AFNb6b6r1dDBezLpsansedBwANZbZ20u+8WZjPzdn/jAbjvxgd13Zsd9Psmuu+7KRz/6UQD22msvPv3pTzNlyhSOOuqoNrv4mnXr1o2TTz6ZL33pS4uNHzJkCF26dGHBggWcffbZbLLJJku8t3///uy3334MHDiQpqYmjj32WLbffvt2r2+zyMzSM2mPQYMGZVsXmXV0q+4DYI9qdAntd/orja5gteNx3gAe5ytd2eP8ooM2Y5PeW9apmtoNbHr7mqFpT8/gwM9+iYdvuXKl19EuPXda9HLUqFGMHz+e888/v4EFLWnSpElLnLGKiPsys9WkZzefJElSCXbzSZK0Cuu7Rc9V56xUC8OHD2f48OGNLqM0z0xJkiSVYJiSJKmdkqRR1x5rxWjP/jRMSZLUTk++vICFc2cbqDqJzOSFF15grbXWWq73ec2UJEnt9It/vsQXgT4bPE8Qy2xfL5Oi9XssrRJemdToCpZqrbXWolevXsv1HsOUJEntNPuNtzjrjhdW+nK9BUjHYjefJElSCTWFqYjYLyImR8SUiDi1lelfj4gJxc/DEfFmRLyr/uVKkiR1LMsMUxHRBbgA2B/YFjgyIha7L31m/jgzd8zMHYFvArdn5osroF5JkqQOpZYzU7sBUzLzicycD4wGhi2l/ZHAH+pRnCRJUkdXS5jaHHi6anh6MW4JEbEOsB9wVfnSJEmSOr5awlRr3/Vs64YaHwP+0VYXX0QcHxHjI2L8rFmr8Nc6JUmSCrWEqenAFlXDvYAZbbQ9gqV08WXmhZk5KDMH9ejRo/YqJUmSOqhawtQ4YOuI6BcR3agEpjEtG0XEO4HBwDX1LVGSJKnjWuZNOzNzYUSMAK4HugAXZ+bEiDihmD6yaPoJ4IbMfG2FVStJktTB1HQH9MwcC4xtMW5ki+FRwKh6FSZJkrQq8A7okiRJJRimJEmSSjBMSZIklWCYkiRJKsEwJUmSVIJhSpIkqQTDlCRJUgmGKUmSpBIMU5IkSSUYpiRJkkowTEmSJJVgmJIkSSrBMCVJklSCYUqSJKkEw5QkSVIJhilJkqQSDFOSJEklGKYkSZJKMExJkiSVYJiSJEkqwTAlSZJUQk1hKiL2i4jJETElIk5to83eETEhIiZGxO31LVOSJKlj6rqsBhHRBbgA2BeYDoyLiDGZ+UhVmw2AXwL7ZeZTEbHxCqpXkiSpQ6nlzNRuwJTMfCIz5wOjgWEt2hwF/F9mPgWQmTPrW6YkSVLHVEuY2hx4ump4ejGu2jbAhhFxW0TcFxGfqVeBkiRJHdkyu/mAaGVctjKfXYAPA2sDd0fEPZn56GIzijgeOB6gd+/ey1+tJElSB1PLmanpwBZVw72AGa20+VtmvpaZzwN3ADu0nFFmXpiZgzJzUI8ePdpbsyRJUodRS5gaB2wdEf0iohtwBDCmRZtrgA9ERNeIWAfYHZhU31IlSZI6nmV282XmwogYAVwPdAEuzsyJEXFCMX1kZk6KiL8BDwJvAb/OzIdXZOGSJEkdQS3XTJGZY4GxLcaNbDH8Y+DH9StNkiSp4/MO6JIkSSUYpiRJkkowTEmSJJVgmJIkSSrBMCVJklSCYUqSJKkEw5QkSVIJhilJkqQSDFOSJEklGKYkSZJKMExJkiSVYJiSJEkqwTAlSZJUgmFKkiSpBMOUJElSCYYpSZKkEgxTkiRJJRimJEmSSjBMSZIklWCYkiRJKsEwJUmSVIJhSpIkqYSawlRE7BcRkyNiSkSc2sr0vSPilYiYUPx8t/6lSpIkdTxdl9UgIroAFwD7AtOBcRExJjMfadH075l54AqoUZIkqcOq5czUbsCUzHwiM+cDo4FhK7YsSZKkVUMtYWpz4Omq4enFuJb2jIgHIuKvEbFdXaqTJEnq4JbZzQdEK+OyxfD9QJ/MnBMRBwBXA1svMaOI44HjAXr37r18lUqSJHVAtZyZmg5sUTXcC5hR3SAzZ2fmnOL1WGCNiOjeckaZeWFmDsrMQT169ChRtiRJUsdQS5gaB2wdEf0iohtwBDCmukFEbBoRUbzerZjvC/UuVpIkqaNZZjdfZi6MiBHA9UAX4OLMnBgRJxTTRwKHAidGxEJgHnBEZrbsCpQkSep0arlmqrnrbmyLcSOrXp8PnF/f0iRJkjo+74AuSZJUgmFKkiSpBMOUJElSCYYpSZKkEgxTkiRJJRimJEmSSjBMSZIklWCYkiRJKsEwJUmSVIJhSpIkqQTDlCRJUgmGKUmSpBIMU5IkSSUYpiRJkkowTEmSJJVgmJIkSSrBMCVJklSCYUqSJKkEw5QkSVIJhilJkqQSDFOSJEkl1BSmImK/iJgcEVMi4tSltNs1It6MiEPrV6IkSVLHtcwwFRFdgAuA/YFtgSMjYts22p0DXF/vIiVJkjqqWs5M7QZMycwnMnM+MBoY1kq7LwJXATPrWJ8kSVKHVkuY2hx4ump4ejFukYjYHPgEMLJ+pUmSJHV8tYSpaGVcthj+GfCNzHxzqTOKOD4ixkfE+FmzZtVYoiRJUsfVtYY204EtqoZ7ATNatBkEjI4IgO7AARGxMDOvrm6UmRcCFwIMGjSoZSCTJEla5dQSpsYBW0dEP+AZ4AjgqOoGmdmv+XVEjAKubRmkJEmSOqNlhqnMXBgRI6h8S68LcHFmToyIE4rpXiclSZJWW7WcmSIzxwJjW4xrNURl5vDyZUmSJK0avAO6JElSCYYpSZKkEgxTkiRJJRimJEmSSjBMSZIklWCYkiRJKsEwJUmSVIJhSpIkqQTDlCRJUgmGKUmSpBIMU5IkSSUYpiRJkkowTEmSJJVgmJIkSSrBMCVJklSCYUqSJKkEw5QkSVIJhilJkqQSDFOSJEklGKYkSZJKMExJkiSVUFOYioj9ImJyREyJiFNbmT4sIh6MiAkRMT4i9qp/qZIkSR1P12U1iIguwAXAvsB0YFxEjMnMR6qa3QyMycyMiIHAH4H3roiCJUmSOpJazkztBkzJzCcycz4wGhhW3SAz52RmFoPrAokkSdJqoJYwtTnwdNXw9GLcYiLiExHxb+A64HP1KU+SJKljqyVMRSvjljjzlJl/zsz3Ah8Hzmh1RhHHF9dUjZ81a9ZyFSpJktQR1RKmpgNbVA33Ama01Tgz7wC2iojurUy7MDMHZeagHj16LHexkiRJHU0tYWocsHVE9IuIbsARwJjqBhHx7oiI4vXOQDfghXoXK0mS1NEs89t8mbkwIkYA1wNdgIszc2JEnFBMHwkcAnwmIhYA84DDqy5IlyRJ6rSWGaYAMnMsMLbFuJFVr88BzqlvaZIkSR2fd0CXJEkqwTAlSZJUgmFKkiSpBMOUJElSCYYpSZKkEgxTkiRJJRimJEmSSjBMSZIklWCYkiRJKsEwJUmSVIJhSpIkqQTDlCRJUgmGKUmSpBIMU5IkSSUYpiRJkkowTEmSJJVgmJIkSSrBMCVJklSCYUqSJKkEw5QkSVIJhilJkqQSagpTEbFfREyOiCkRcWor0z8VEQ8WP3dFxA71L1WSJKnjWWaYioguwAXA/sC2wJERsW2LZlOBwZk5EDgDuLDehUqSJHVEtZyZ2g2YkplPZOZ8YDQwrLpBZt6VmS8Vg/cAvepbpiRJUsdUS5jaHHi6anh6Ma4t/w/4a5miJEmSVhVda2gTrYzLVhtGDKESpvZqY/rxwPEAvXv3rrFESZKkjquWM1PTgS2qhnsBM1o2ioiBwK+BYZn5QmszyswLM3NQZg7q0aNHe+qVJEnqUGoJU+OArSOiX0R0A44AxlQ3iIjewP8Bn87MR+tfpiRJUse0zG6+zFwYESOA64EuwMWZOTEiTiimjwS+C2wE/DIiABZm5qAVV7YkSVLHUMs1U2TmWGBsi3Ejq14fCxxb39IkSZI6Pu+ALkmSVIJhSpIkqQTDlCRJUgmGKUmSpBIMU5IkSSUYpiRJkkowTEmSJJVgmJIkSSrBMCVJklSCYUqSJKkEw5QkSVIJhilJkqQSDFOSJEklGKYkSZJKMExJkiSVYJiSJEkqwTAlSZJUgmFKkiSpBMOUJElSCYYpSZKkEgxTkiRJJRimJEmSSqgpTEXEfhExOSKmRMSprUx/b0TcHRFvRMR/1b9MSZKkjqnrshpERBfgAmBfYDowLiLGZOYjVc1eBE4GPr4iipQkSeqoajkztRswJTOfyMz5wGhgWHWDzJyZmeOABSugRkmSpA6rljC1OfB01fD0YpwkSdJqr5YwFa2My/YsLCKOj4jxETF+1qxZ7ZmFJElSh1JLmJoObFE13AuY0Z6FZeaFmTkoMwf16NGjPbOQJEnqUGoJU+OArSOiX0R0A44AxqzYsiRJklYNy/w2X2YujIgRwPVAF+DizJwYEScU00dGxKbAeOAdwFsR8WVg28ycveJKlyRJarxlhimAzBwLjG0xbmTV6/9Q6f6TJElarXgHdEmSpBIMU5IkSSUYpiRJkkowTEmSJJVgmJIkSSrBMCVJklSCYUqSJKkEw5QkSVIJhilJkqQSDFOSJEklGKYkSZJKMExJkiSVYJiSJEkqwTAlSZJUgmFKkiSpBMOUJElSCYYpSZKkEgxTkiRJJRimJEmSSjBMSZIklWCYkiRJKqGmMBUR+0XE5IiYEhGntjI9IuK8YvqDEbFz/UuVJEnqeJYZpiKiC3ABsD+wLXBkRGzbotn+wNbFz/HAr+pcpyRJUodUy5mp3YApmflEZs4HRgPDWrQZBlyaFfcAG0TEZnWuVZIkqcOpJUxtDjxdNTy9GLe8bSRJkjqdrjW0iVbGZTvaEBHHU+kGBJgTEZNrWL7qJKA78Hyj62iX77V2iElL8jjX6sDjvCH6tDWhljA1HdiiargXMKMdbcjMC4ELa1imVoCIGJ+Zgxpdh7QieZxrdeBx3rHU0s03Dtg6IvpFRDfgCGBMizZjgM8U3+rbA3glM5+tc62SJEkdzjLPTGXmwogYAVwPdAEuzsyJEXFCMX0kMBY4AJgCzAWOWXElS5IkdRyRucSlTeqkIuL4oqtV6rQ8zrU68DjvWAxTkiRJJfg4GUmSpBIMU5IkSSUYpiRJkkowTHVyxS0t1qoaXjsi+jawJGmFiIg9ImL9quH1I2L3RtYk1VNErBsRTVXDTRGxTiNrUoVhqvO7EniravjNYpzU2fwKmFM1/Bo+dF2dy81AdXhaB7ipQbWoimGq8+taPKAagOJ1twbWI60okVVfT87Mt6jtKQ/SqmKtzFz0H4bitWemOgDDVOc3KyIOah6IiGGsqs9zkpbuiYg4OSLWKH6+BDzR6KKkOnotInZuHoiIXYB5DaxHBe8z1clFxFbA5UDPYtR04DOZOaVxVUn1FxEbA+cBH6LyoPWbgS9n5syGFibVSUTsCozm7WffbgYcnpn3Na4qgWFqtRER61HZ3682uhZJUvtExBrAe4AA/p2ZCxpckrCbr9OLiB9ExAaZOSczX42IDSPizEbXJdVbRFwSERtUDW8YERc3sCSpriLiJGDdzHw4Mx8C1ouILzS6LhmmVgf7Z+bLzQOZ+RKVh1JLnc3AVo71nRpXjlR3x7VyjB/XuHLUzDDV+XWJiDWbByJibWDNpbSXVlVNEbFh80BEvAu/zafOpSkionkgIrrgt7M7BD9oOr/LgJsj4rdULsr9HHBJY0uSVoifAHdFxJ+K4cOAsxpYj1Rv1wN/jIiRVD7PTwD+1tiSBF6AvlqIiP2BD1O5YPGGzLy+wSVJK0REbAcMoXKs35yZjzS4JKluiruff56qz3Pg15n5ZkMLk2FKUudS3CJh0SOUMvOpBpYjaTXgNVOdXPG8snERMSci5kfEmxExu9F1SfUWEQdFxGPAVOB2YBrw14YWJdVRRGwdEX+KiEci4onmn0bXJcPU6uB84EjgMWBt4FjgFw2tSFoxzgD2AB7NzH5UukL+0diSpLr6LZXnTS6k0p19KfC7hlYkwDC1Wijudt4lM9/MzN9S+SWUOpsFmfkClW88NWXmrcCODa5Jqqe1M/NmKpfoPJmZp1O5478azG/zdX5zI6IbMCEifgQ8C6zb4JqkFeHl4k7/dwCXR8RMKv+DlzqL14uL0B+LiBHAM8DGDa5JeAF6pxcRfYDnqNyL5CvAO4Ff+mw+dTYRsS6Vh742AZ+icqxfXpytklZ5xbP5JgEbUOnWfgfw48y8p5F1yTC12ouIqzLzkEbXIa1oEXF3Zu7Z6DqkFSUifpGZX2x0Hasjr5nSlo0uQFpJ1lp2E2mV9v5GF7C6MkzJU5NaXXisS1ohDFOSJEklGKYUy24idQoe6+rsPMYbxDDVyUXEJyJizaU0+cZKK0ZqrE83ugCpjIhY4rq/iOheNfjzlViOqhimOr+DgEcj4ncR8dGIWOzeYpl5Q4PqkuoqIg6OiMci4pWImB0Rr1Y/OikzH25kfVIdjIuIPZoHIuIQ4K7m4cwc1Yii5K0RVgsRsQawP3A4sBdwY2Ye29iqpPqKiCnAxzJzUqNrkVaEiBgAXAzcBvQENgKOzczpjaxLhqnVRhGo9gOOAT6QmT0aXJJUVxHxj8z0q+Hq1CLi41Sex/cq8EFvwNwx+DiZTi4i9gOOoPI8vtuAXwOfbGRN0goyPiKuAK4G3mgemZn/17CKpDqKiN8AWwEDgW2Av0TE+Zl5QWMrk2Gq8xsOjAY+n5lvLKOttCp7BzAXGFo1LgHDlDqLh6l06yUwtbh+6twG1yTs5uv0ImJL4NnMnFcMrw1skpnTGlqYJEmdhGGqk4uI8cD7MnN+MdwN+Edm7trYyqT6iIhTMvNHEfELWrnLeWae3ICypLqLiK2BHwLbUvV4pMz0sWANZjdf59e1OUgBZOb8IlBJnUXzt/fG4yNj1Ln9FjgN+CmV62CPwRt1dgiGqc5vVkQclJljACJiGPB8g2uS6iYz/1K8fAT4FtCXtz/bEri0AWVJK8LamXlzRERmPgmcHhF/pxKw1ECGqc7vBODyiDi/GJ6Od4JW53QZ8HXgIeCtBtcirQivR0QT8FhEjACeATZucE3Ca6ZWGxGxHpX9/WqL8Z/NzEsaVJZUNxFxZ2bu1eg6pBUlInal0q29AXAGlW+w/igz/9nIumSYWu1FxP2ZuXOj65DKiogPA0cCN+N9ptQJRcQg4NtAH2CNYnRm5sDGVSWwm09evKjO4xjgvVT+yDR383mfKXUml2NXdodkmJKnJtVZ7JCZAxpdhLQCzWr+MpE6FsOUPDOlzuKeiNg2Mx9pdCHSCnJaRPwau7I7HMOU/tHoAqQ62Qv4bERMpfKHJvB6EnUudmV3UF6A3slFxFdbGf0KcF9mTljJ5UgrTET0aW18cT8eaZUXEQ/Zld0xeWaq8xtU/DTf2PCjwDjghIi4MjN/1LDKpDoyNGk1YFd2B+WZqU4uIq4HDsnMOcXwesCfgE9QOTu1bSPrkyTVJiImAVsBdmV3MJ6Z6vx6A/OrhhcAfTJzXkS80cZ7JEkdz36NLkCtM0x1fr+ncmr4mmL4Y8AfImJdKs8ykyStAuzK7rjs5lsNRMQuVL7pFMCdmTm+wSVJktRpGKY6uYj4OXBFZt7V6FokSeqMmhpdgFa4+4HvRMSUiPhx8WwnSZJUJ56ZWk1ExLuAQ4AjgN6ZuXWDS5IkqVPwzNTq491U7pzbF/h3Y0uRJKnz8MxUJxcR5wAHA48DVwB/zsyXG1qUJEmdiLdG6PymAu8DtgTWBAZGBJl5R2PLkiSpczBMdX5vArcAvYAJwB7A3cCHGliTJEmdhtdMdX4nA7sCT2bmEGAnYFZjS5IkqfMwTHV+r2fm6wARsWZm/ht4T4NrkiSp07Cbr/ObHhEbAFcDN0bES8CMhlYkSVIn4rf5ViMRMRh4J/C3zJy/rPaSJGnZDFOSJEkleM2UJElSCYYpSZKkEgxTkiRJJRimJEmSSjBMSZIklfD/ASpX3eWsmpyeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lemmatisation_comparison = evaluation_dataframes['MultinomialNB'].iloc[[0, 6],]\n",
    "lemmatisation_comparison = lemmatisation_comparison[[\"name\", \"avg_cv_acc\", \"min_acc\", \"max_acc\"]]\n",
    "graph = lemmatisation_comparison.T\n",
    "graph_header = graph.iloc[0]\n",
    "graph = graph[1:]\n",
    "graph.columns = graph_header\n",
    "graph.plot(kind='bar', figsize=(10,5), title=\"Comparison of baseline model with and without lemmatisation\")\n",
    "lemmatisation_comparison_rmse = evaluation_dataframes['MultinomialNB'].iloc[[0, 6],]\n",
    "lemmatisation_comparison_rmse = lemmatisation_comparison_rmse[[\"name\", \"rmse\"]]\n",
    "graph = clip_counts_comparison_rmse.T\n",
    "graph_header = graph.iloc[0]\n",
    "graph = graph[1:]\n",
    "graph.columns = graph_header\n",
    "graph.plot(kind='bar', figsize=(10,5), title=\"Comparison of baseline model with and without lemmatisation (RMSE)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>learning_model</th>\n",
       "      <th>feature</th>\n",
       "      <th>clip_counts</th>\n",
       "      <th>negation</th>\n",
       "      <th>remove_stopwords</th>\n",
       "      <th>lemmatise</th>\n",
       "      <th>additional_features</th>\n",
       "      <th>avg_cv_acc</th>\n",
       "      <th>rmse</th>\n",
       "      <th>min_acc</th>\n",
       "      <th>max_acc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>baseline-NB</td>\n",
       "      <td>MultinomialNB()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.8290</td>\n",
       "      <td>0.021071</td>\n",
       "      <td>0.785</td>\n",
       "      <td>0.855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>param-exp-NB-1</td>\n",
       "      <td>MultinomialNB()</td>\n",
       "      <td>bigram</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.8395</td>\n",
       "      <td>0.020056</td>\n",
       "      <td>0.805</td>\n",
       "      <td>0.865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>param-exp-NB-2</td>\n",
       "      <td>MultinomialNB()</td>\n",
       "      <td>trigram</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.7685</td>\n",
       "      <td>0.021337</td>\n",
       "      <td>0.745</td>\n",
       "      <td>0.805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>param-exp-NB-3</td>\n",
       "      <td>MultinomialNB()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.8050</td>\n",
       "      <td>0.025884</td>\n",
       "      <td>0.750</td>\n",
       "      <td>0.840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>param-exp-NB-4</td>\n",
       "      <td>MultinomialNB()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.8340</td>\n",
       "      <td>0.025768</td>\n",
       "      <td>0.775</td>\n",
       "      <td>0.865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>param-exp-NB-5</td>\n",
       "      <td>MultinomialNB()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.8325</td>\n",
       "      <td>0.021243</td>\n",
       "      <td>0.790</td>\n",
       "      <td>0.855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>param-exp-NB-6</td>\n",
       "      <td>MultinomialNB()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.8315</td>\n",
       "      <td>0.020982</td>\n",
       "      <td>0.790</td>\n",
       "      <td>0.855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>param-exp-NB-7</td>\n",
       "      <td>MultinomialNB()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.8190</td>\n",
       "      <td>0.018547</td>\n",
       "      <td>0.780</td>\n",
       "      <td>0.840</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             name   learning_model  feature clip_counts negation  \\\n",
       "0     baseline-NB  MultinomialNB()  unigram        True    False   \n",
       "1  param-exp-NB-1  MultinomialNB()   bigram        True    False   \n",
       "2  param-exp-NB-2  MultinomialNB()  trigram        True    False   \n",
       "3  param-exp-NB-3  MultinomialNB()  unigram       False    False   \n",
       "4  param-exp-NB-4  MultinomialNB()  unigram        True    False   \n",
       "5  param-exp-NB-5  MultinomialNB()  unigram        True     True   \n",
       "6  param-exp-NB-6  MultinomialNB()  unigram        True     True   \n",
       "7  param-exp-NB-7  MultinomialNB()  unigram        True    False   \n",
       "\n",
       "  remove_stopwords lemmatise additional_features  avg_cv_acc      rmse  \\\n",
       "0            False     False               False      0.8290  0.021071   \n",
       "1            False     False               False      0.8395  0.020056   \n",
       "2            False     False               False      0.7685  0.021337   \n",
       "3            False     False               False      0.8050  0.025884   \n",
       "4             True     False               False      0.8340  0.025768   \n",
       "5            False     False               False      0.8325  0.021243   \n",
       "6            False     False               False      0.8315  0.020982   \n",
       "7            False      True               False      0.8190  0.018547   \n",
       "\n",
       "   min_acc  max_acc  \n",
       "0    0.785    0.855  \n",
       "1    0.805    0.865  \n",
       "2    0.745    0.805  \n",
       "3    0.750    0.840  \n",
       "4    0.775    0.865  \n",
       "5    0.790    0.855  \n",
       "6    0.790    0.855  \n",
       "7    0.780    0.840  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluation_dataframes['MultinomialNB']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use of lemmatisation has also surprisingly resulted in a decrease in accuracy by about 2%. It is possible that lemmatisation and negation could work well together. This is something which is tested as part of our additional experiments."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Experiment 7: Use of additional features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The final thing which we implemented was the creation of 3 new additional features. The first of these is formed from the *doc2negate* vector mentioned above. This vector shows the number of negated terms in each document. The second and third of these additional features represent positive and negative lexicons respectively. Similar to the *doc2negate* vector, they show the number of terms in each document which are found in positive and negative sentiment lexicons.  \n",
    "  \n",
    "The sentiment lexicon which we used was the [MPQA Subjectivity Lexicon](http://mpqa.cs.pitt.edu/lexicons/subj_lexicon/). It contains 1340 positive and 2441 negative terms.  \n",
    "  \n",
    "These 3 vectors are appended to the feature matrix which was created in the *extract_features* function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating fold 1 of 10\n",
      "--> 0.825\n",
      "Evaluating fold 2 of 10\n",
      "--> 0.825\n",
      "Evaluating fold 3 of 10\n",
      "--> 0.855\n",
      "Evaluating fold 4 of 10\n",
      "--> 0.805\n",
      "Evaluating fold 5 of 10\n",
      "--> 0.835\n",
      "Evaluating fold 6 of 10\n",
      "--> 0.82\n",
      "Evaluating fold 7 of 10\n",
      "--> 0.845\n",
      "Evaluating fold 8 of 10\n",
      "--> 0.835\n",
      "Evaluating fold 9 of 10\n",
      "--> 0.805\n",
      "Evaluating fold 10 of 10\n",
      "--> 0.865\n"
     ]
    }
   ],
   "source": [
    "lemmatise = False\n",
    "additional_features = True\n",
    "model = PolarityPredictor(clip_counts, negation, remove_stopwords, lemmatise, additional_features, learning_model)\n",
    "avg, rmse, min_acc, max_acc = evaluate_model(model, splits, feature, verbose = True)\n",
    "evaluation_dataframes['MultinomialNB'].loc[evaluation_dataframes['MultinomialNB'].index.max()+1] = [f'param-exp-NB-{index}', learning_model, feature, clip_counts, \n",
    "                                              negation, remove_stopwords, lemmatise, additional_features, avg, rmse, min_acc, max_acc]\n",
    "index += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Comparison of baseline model with and without additional features (RMSE)'}>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lemmatisation_comparison = evaluation_dataframes['MultinomialNB'].iloc[[0, 7],]\n",
    "lemmatisation_comparison = lemmatisation_comparison[[\"name\", \"avg_cv_acc\", \"min_acc\", \"max_acc\"]]\n",
    "graph = lemmatisation_comparison.T\n",
    "graph_header = graph.iloc[0]\n",
    "graph = graph[1:]\n",
    "graph.columns = graph_header\n",
    "graph.plot(kind='bar', figsize=(10,5), title=\"Comparison of baseline model with and without additional features\")\n",
    "lemmatisation_comparison_rmse = evaluation_dataframes['MultinomialNB'].iloc[[0, 7],]\n",
    "lemmatisation_comparison_rmse = lemmatisation_comparison_rmse[[\"name\", \"rmse\"]]\n",
    "graph = clip_counts_comparison_rmse.T\n",
    "graph_header = graph.iloc[0]\n",
    "graph = graph[1:]\n",
    "graph.columns = graph_header\n",
    "graph.plot(kind='bar', figsize=(10,5), title=\"Comparison of baseline model with and without additional features (RMSE)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The use of additional features also lowers accuracy. Perhaps adding them to the feature matrix in another would have worked better."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Baseline comparison of different learning models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "clip_counts = True\n",
    "negation = False\n",
    "remove_stopwords = False\n",
    "lemmatise = False\n",
    "additional_features = False\n",
    "feature = 'unigram'\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "learning_model = LogisticRegression()\n",
    "evaluation_dataframes['LearningModels'] = pd.DataFrame(columns=['name', 'learning_model', 'feature', 'clip_counts', 'negation', 'remove_stopwords', 'lemmatise', 'additional_features', 'avg_cv_acc', 'rmse', 'min_acc', 'max_acc'])\n",
    "row_to_add = evaluation_dataframes['MultinomialNB'].iloc[[0],]\n",
    "evaluation_dataframes['LearningModels'] = evaluation_dataframes['LearningModels'].append(row_to_add)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating fold 1 of 10\n",
      "--> 0.855\n",
      "Evaluating fold 2 of 10\n",
      "--> 0.865\n",
      "Evaluating fold 3 of 10\n",
      "--> 0.88\n",
      "Evaluating fold 4 of 10\n",
      "--> 0.835\n",
      "Evaluating fold 5 of 10\n",
      "--> 0.87\n",
      "Evaluating fold 6 of 10\n",
      "--> 0.905\n",
      "Evaluating fold 7 of 10\n",
      "--> 0.885\n",
      "Evaluating fold 8 of 10\n",
      "--> 0.895\n",
      "Evaluating fold 9 of 10\n",
      "--> 0.845\n",
      "Evaluating fold 10 of 10\n",
      "--> 0.915\n"
     ]
    }
   ],
   "source": [
    "index = 1\n",
    "model = PolarityPredictor(clip_counts, negation, remove_stopwords, lemmatise, additional_features, learning_model)\n",
    "avg, rmse, min_acc, max_acc = evaluate_model(model, splits, feature, verbose = True)\n",
    "evaluation_dataframes['LearningModels'].loc[evaluation_dataframes['LearningModels'].index.max()+1] = [f'lm-exp-{index}', learning_model, feature, clip_counts, \n",
    "                                              negation, remove_stopwords, lemmatise, additional_features, avg, rmse, min_acc, max_acc]\n",
    "index += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating fold 1 of 10\n",
      "--> 0.645\n",
      "Evaluating fold 2 of 10\n",
      "--> 0.615\n",
      "Evaluating fold 3 of 10\n",
      "--> 0.61\n",
      "Evaluating fold 4 of 10\n",
      "--> 0.665\n",
      "Evaluating fold 5 of 10\n",
      "--> 0.61\n",
      "Evaluating fold 6 of 10\n",
      "--> 0.595\n",
      "Evaluating fold 7 of 10\n",
      "--> 0.61\n",
      "Evaluating fold 8 of 10\n",
      "--> 0.635\n",
      "Evaluating fold 9 of 10\n",
      "--> 0.57\n",
      "Evaluating fold 10 of 10\n",
      "--> 0.65\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "learning_model = DecisionTreeClassifier()\n",
    "model = PolarityPredictor(clip_counts, negation, remove_stopwords, lemmatise, additional_features, learning_model)\n",
    "avg, rmse, min_acc, max_acc = evaluate_model(model, splits, feature, verbose = True)\n",
    "evaluation_dataframes['LearningModels'].loc[evaluation_dataframes['LearningModels'].index.max()+1] = [f'lm-exp-{index}', learning_model, feature, clip_counts, \n",
    "                                              negation, remove_stopwords, lemmatise, additional_features, avg, rmse, min_acc, max_acc]\n",
    "index += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating fold 1 of 10\n",
      "--> 0.86\n",
      "Evaluating fold 2 of 10\n",
      "--> 0.865\n",
      "Evaluating fold 3 of 10\n",
      "--> 0.85\n",
      "Evaluating fold 4 of 10\n",
      "--> 0.815\n",
      "Evaluating fold 5 of 10\n",
      "--> 0.845\n",
      "Evaluating fold 6 of 10\n",
      "--> 0.835\n",
      "Evaluating fold 7 of 10\n",
      "--> 0.9\n",
      "Evaluating fold 8 of 10\n",
      "--> 0.885\n",
      "Evaluating fold 9 of 10\n",
      "--> 0.85\n",
      "Evaluating fold 10 of 10\n",
      "--> 0.88\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "learning_model = SVC()\n",
    "model = PolarityPredictor(clip_counts, negation, remove_stopwords, lemmatise, additional_features, learning_model)\n",
    "avg, rmse, min_acc, max_acc = evaluate_model(model, splits, feature, verbose = True)\n",
    "evaluation_dataframes['LearningModels'].loc[evaluation_dataframes['LearningModels'].index.max()+1] = [f'lm-exp-{index}', learning_model, feature, clip_counts, \n",
    "                                              negation, remove_stopwords, lemmatise, additional_features, avg, rmse, min_acc, max_acc]\n",
    "index += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating fold 1 of 10\n",
      "--> 0.85\n",
      "Evaluating fold 2 of 10\n",
      "--> 0.83\n",
      "Evaluating fold 3 of 10\n",
      "--> 0.85\n",
      "Evaluating fold 4 of 10\n",
      "--> 0.805\n",
      "Evaluating fold 5 of 10\n",
      "--> 0.79\n",
      "Evaluating fold 6 of 10\n",
      "--> 0.805\n",
      "Evaluating fold 7 of 10\n",
      "--> 0.835\n",
      "Evaluating fold 8 of 10\n",
      "--> 0.825\n",
      "Evaluating fold 9 of 10\n",
      "--> 0.785\n",
      "Evaluating fold 10 of 10\n",
      "--> 0.83\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "learning_model = RandomForestClassifier()\n",
    "model = PolarityPredictor(clip_counts, negation, remove_stopwords, lemmatise, additional_features, learning_model)\n",
    "avg, rmse, min_acc, max_acc = evaluate_model(model, splits, feature, verbose = True)\n",
    "evaluation_dataframes['LearningModels'].loc[evaluation_dataframes['LearningModels'].index.max()+1] = [f'lm-exp-{index}', learning_model, feature, clip_counts, \n",
    "                                              negation, remove_stopwords, lemmatise, additional_features, avg, rmse, min_acc, max_acc]\n",
    "index += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Comparison different learning model accuracy (RMSE)'}>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learning_model_comparison = evaluation_dataframes['LearningModels']\n",
    "learning_model_comparison = learning_model_comparison[[\"name\", \"avg_cv_acc\", \"min_acc\", \"max_acc\"]]\n",
    "graph = learning_model_comparison.T\n",
    "graph_header = graph.iloc[0]\n",
    "graph = graph[1:]\n",
    "graph.columns = graph_header\n",
    "graph.plot(kind='bar', figsize=(10,5), title=\"Comparison of different learning model accuracy\")\n",
    "learning_model_comparison_rmse = evaluation_dataframes['LearningModels']\n",
    "learning_model_comparison_rmse = learning_model_comparison_rmse[[\"name\", \"rmse\"]]\n",
    "graph = learning_model_comparison_rmse.T\n",
    "graph_header = graph.iloc[0]\n",
    "graph = graph[1:]\n",
    "graph.columns = graph_header\n",
    "graph.plot(kind='bar', figsize=(10,5), title=\"Comparison different learning model accuracy (RMSE)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>learning_model</th>\n",
       "      <th>feature</th>\n",
       "      <th>clip_counts</th>\n",
       "      <th>negation</th>\n",
       "      <th>remove_stopwords</th>\n",
       "      <th>lemmatise</th>\n",
       "      <th>additional_features</th>\n",
       "      <th>avg_cv_acc</th>\n",
       "      <th>rmse</th>\n",
       "      <th>min_acc</th>\n",
       "      <th>max_acc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>baseline-NB</td>\n",
       "      <td>MultinomialNB()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.8290</td>\n",
       "      <td>0.021071</td>\n",
       "      <td>0.785</td>\n",
       "      <td>0.855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>lm-exp-1</td>\n",
       "      <td>LogisticRegression()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.8750</td>\n",
       "      <td>0.024597</td>\n",
       "      <td>0.835</td>\n",
       "      <td>0.915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>lm-exp-2</td>\n",
       "      <td>DecisionTreeClassifier()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.6205</td>\n",
       "      <td>0.026875</td>\n",
       "      <td>0.570</td>\n",
       "      <td>0.665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>lm-exp-3</td>\n",
       "      <td>SVC()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.8585</td>\n",
       "      <td>0.023880</td>\n",
       "      <td>0.815</td>\n",
       "      <td>0.900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>lm-exp-4</td>\n",
       "      <td>(DecisionTreeClassifier(max_features='auto', r...</td>\n",
       "      <td>unigram</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.8205</td>\n",
       "      <td>0.021960</td>\n",
       "      <td>0.785</td>\n",
       "      <td>0.850</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          name                                     learning_model  feature  \\\n",
       "0  baseline-NB                                    MultinomialNB()  unigram   \n",
       "1     lm-exp-1                               LogisticRegression()  unigram   \n",
       "2     lm-exp-2                           DecisionTreeClassifier()  unigram   \n",
       "3     lm-exp-3                                              SVC()  unigram   \n",
       "4     lm-exp-4  (DecisionTreeClassifier(max_features='auto', r...  unigram   \n",
       "\n",
       "  clip_counts negation remove_stopwords lemmatise additional_features  \\\n",
       "0        True    False            False     False               False   \n",
       "1        True    False            False     False               False   \n",
       "2        True    False            False     False               False   \n",
       "3        True    False            False     False               False   \n",
       "4        True    False            False     False               False   \n",
       "\n",
       "   avg_cv_acc      rmse  min_acc  max_acc  \n",
       "0      0.8290  0.021071    0.785    0.855  \n",
       "1      0.8750  0.024597    0.835    0.915  \n",
       "2      0.6205  0.026875    0.570    0.665  \n",
       "3      0.8585  0.023880    0.815    0.900  \n",
       "4      0.8205  0.021960    0.785    0.850  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluation_dataframes['LearningModels']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### From the above visualisations and table, we can see that *lm-exp-1* or the LogisticRegression model achieved the highest maximum accuracy of 91.5%. This model also achieved the highest maximum average cross validation accuracy. However, the baseline Muiltinomial Naive Bayes model achieved the lowest RMSE. Another impressive result is that of the SVM Classifier, which achieved the second highest maximum accuracy. The Decision Tree classifier performed poorly with an average cross validation accuracy of just over 62%."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Additional Experiments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also performed a total of 57 additional experiments, the results of which can be viewed below.  \n",
    "Many different configurations of the model can be used, and those below are a selection of the ones we had time to test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "multinomialnb_experiments = pd.read_csv(\"output/multinomialnb.csv\")\n",
    "multinomialnb_experiments = multinomialnb_experiments.drop(['Unnamed: 0'], axis=1)\n",
    "decision_tree_experiments = pd.read_csv(\"output/decisiontree.csv\")\n",
    "decision_tree_experiments = decision_tree_experiments.drop(['Unnamed: 0'], axis=1)\n",
    "logistic_regression_experiments = pd.read_csv(\"output/logisticregression.csv\")\n",
    "logistic_regression_experiments = logistic_regression_experiments.drop(['Unnamed: 0'], axis=1)\n",
    "random_forest_experiments = pd.read_csv(\"output/randomforest.csv\")\n",
    "random_forest_experiments = random_forest_experiments.drop(['Unnamed: 0'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multinomial NB Experiments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>learning_model</th>\n",
       "      <th>feature</th>\n",
       "      <th>clip_counts</th>\n",
       "      <th>negation</th>\n",
       "      <th>remove_stopwords</th>\n",
       "      <th>lemmatise</th>\n",
       "      <th>additional_features</th>\n",
       "      <th>avg_cv_acc</th>\n",
       "      <th>rmse</th>\n",
       "      <th>min_acc</th>\n",
       "      <th>max_acc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>baseline-NB</td>\n",
       "      <td>MultinomialNB()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.8290</td>\n",
       "      <td>0.021071</td>\n",
       "      <td>0.785</td>\n",
       "      <td>0.855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>param-exp-NB-1</td>\n",
       "      <td>MultinomialNB()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0.8160</td>\n",
       "      <td>0.023854</td>\n",
       "      <td>0.775</td>\n",
       "      <td>0.860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>param-exp-NB-2</td>\n",
       "      <td>MultinomialNB()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.8240</td>\n",
       "      <td>0.022672</td>\n",
       "      <td>0.790</td>\n",
       "      <td>0.855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>param-exp-NB-3</td>\n",
       "      <td>MultinomialNB()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.8120</td>\n",
       "      <td>0.020273</td>\n",
       "      <td>0.780</td>\n",
       "      <td>0.845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>param-exp-NB-4</td>\n",
       "      <td>MultinomialNB()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.8110</td>\n",
       "      <td>0.019723</td>\n",
       "      <td>0.775</td>\n",
       "      <td>0.835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>param-exp-NB-5</td>\n",
       "      <td>MultinomialNB()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0.8145</td>\n",
       "      <td>0.026311</td>\n",
       "      <td>0.775</td>\n",
       "      <td>0.860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>param-exp-NB-6</td>\n",
       "      <td>MultinomialNB()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.8120</td>\n",
       "      <td>0.024515</td>\n",
       "      <td>0.770</td>\n",
       "      <td>0.850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>param-exp-NB-7</td>\n",
       "      <td>MultinomialNB()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.8140</td>\n",
       "      <td>0.021307</td>\n",
       "      <td>0.780</td>\n",
       "      <td>0.845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>param-exp-NB-8</td>\n",
       "      <td>MultinomialNB()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.8070</td>\n",
       "      <td>0.020273</td>\n",
       "      <td>0.770</td>\n",
       "      <td>0.835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>param-exp-NB-9</td>\n",
       "      <td>MultinomialNB()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0.7785</td>\n",
       "      <td>0.030418</td>\n",
       "      <td>0.730</td>\n",
       "      <td>0.820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>param-exp-NB-10</td>\n",
       "      <td>MultinomialNB()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.7945</td>\n",
       "      <td>0.021148</td>\n",
       "      <td>0.760</td>\n",
       "      <td>0.830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>param-exp-NB-11</td>\n",
       "      <td>MultinomialNB()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.7765</td>\n",
       "      <td>0.036128</td>\n",
       "      <td>0.705</td>\n",
       "      <td>0.825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>param-exp-NB-12</td>\n",
       "      <td>MultinomialNB()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.7845</td>\n",
       "      <td>0.025539</td>\n",
       "      <td>0.740</td>\n",
       "      <td>0.820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>param-exp-NB-13</td>\n",
       "      <td>MultinomialNB()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0.7820</td>\n",
       "      <td>0.022494</td>\n",
       "      <td>0.745</td>\n",
       "      <td>0.815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>param-exp-NB-14</td>\n",
       "      <td>MultinomialNB()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.7790</td>\n",
       "      <td>0.023216</td>\n",
       "      <td>0.740</td>\n",
       "      <td>0.810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>param-exp-NB-15</td>\n",
       "      <td>MultinomialNB()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.7855</td>\n",
       "      <td>0.025045</td>\n",
       "      <td>0.750</td>\n",
       "      <td>0.820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>param-exp-NB-16</td>\n",
       "      <td>MultinomialNB()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.7825</td>\n",
       "      <td>0.026856</td>\n",
       "      <td>0.735</td>\n",
       "      <td>0.820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>param-exp-NB-17</td>\n",
       "      <td>MultinomialNB()</td>\n",
       "      <td>bigram</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0.7990</td>\n",
       "      <td>0.024779</td>\n",
       "      <td>0.755</td>\n",
       "      <td>0.840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>param-exp-NB-18</td>\n",
       "      <td>MultinomialNB()</td>\n",
       "      <td>bigram</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.8190</td>\n",
       "      <td>0.020469</td>\n",
       "      <td>0.785</td>\n",
       "      <td>0.850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>param-exp-NB-19</td>\n",
       "      <td>MultinomialNB()</td>\n",
       "      <td>bigram</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.8070</td>\n",
       "      <td>0.019519</td>\n",
       "      <td>0.780</td>\n",
       "      <td>0.840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>param-exp-NB-20</td>\n",
       "      <td>MultinomialNB()</td>\n",
       "      <td>bigram</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.8220</td>\n",
       "      <td>0.026096</td>\n",
       "      <td>0.785</td>\n",
       "      <td>0.875</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               name   learning_model  feature  clip_counts  negation  \\\n",
       "0       baseline-NB  MultinomialNB()  unigram         True     False   \n",
       "1    param-exp-NB-1  MultinomialNB()  unigram         True      True   \n",
       "2    param-exp-NB-2  MultinomialNB()  unigram         True      True   \n",
       "3    param-exp-NB-3  MultinomialNB()  unigram         True      True   \n",
       "4    param-exp-NB-4  MultinomialNB()  unigram         True      True   \n",
       "5    param-exp-NB-5  MultinomialNB()  unigram         True     False   \n",
       "6    param-exp-NB-6  MultinomialNB()  unigram         True     False   \n",
       "7    param-exp-NB-7  MultinomialNB()  unigram         True     False   \n",
       "8    param-exp-NB-8  MultinomialNB()  unigram         True     False   \n",
       "9    param-exp-NB-9  MultinomialNB()  unigram        False      True   \n",
       "10  param-exp-NB-10  MultinomialNB()  unigram        False      True   \n",
       "11  param-exp-NB-11  MultinomialNB()  unigram        False      True   \n",
       "12  param-exp-NB-12  MultinomialNB()  unigram        False      True   \n",
       "13  param-exp-NB-13  MultinomialNB()  unigram        False     False   \n",
       "14  param-exp-NB-14  MultinomialNB()  unigram        False     False   \n",
       "15  param-exp-NB-15  MultinomialNB()  unigram        False     False   \n",
       "16  param-exp-NB-16  MultinomialNB()  unigram        False     False   \n",
       "17  param-exp-NB-17  MultinomialNB()   bigram         True      True   \n",
       "18  param-exp-NB-18  MultinomialNB()   bigram         True      True   \n",
       "19  param-exp-NB-19  MultinomialNB()   bigram         True      True   \n",
       "20  param-exp-NB-20  MultinomialNB()   bigram         True      True   \n",
       "\n",
       "    remove_stopwords  lemmatise  additional_features  avg_cv_acc      rmse  \\\n",
       "0              False      False                False      0.8290  0.021071   \n",
       "1              False       True                 True      0.8160  0.023854   \n",
       "2              False       True                False      0.8240  0.022672   \n",
       "3              False      False                 True      0.8120  0.020273   \n",
       "4              False      False                False      0.8110  0.019723   \n",
       "5              False       True                 True      0.8145  0.026311   \n",
       "6              False       True                False      0.8120  0.024515   \n",
       "7              False      False                 True      0.8140  0.021307   \n",
       "8              False      False                False      0.8070  0.020273   \n",
       "9              False       True                 True      0.7785  0.030418   \n",
       "10             False       True                False      0.7945  0.021148   \n",
       "11             False      False                 True      0.7765  0.036128   \n",
       "12             False      False                False      0.7845  0.025539   \n",
       "13             False       True                 True      0.7820  0.022494   \n",
       "14             False       True                False      0.7790  0.023216   \n",
       "15             False      False                 True      0.7855  0.025045   \n",
       "16             False      False                False      0.7825  0.026856   \n",
       "17             False       True                 True      0.7990  0.024779   \n",
       "18             False       True                False      0.8190  0.020469   \n",
       "19             False      False                 True      0.8070  0.019519   \n",
       "20             False      False                False      0.8220  0.026096   \n",
       "\n",
       "    min_acc  max_acc  \n",
       "0     0.785    0.855  \n",
       "1     0.775    0.860  \n",
       "2     0.790    0.855  \n",
       "3     0.780    0.845  \n",
       "4     0.775    0.835  \n",
       "5     0.775    0.860  \n",
       "6     0.770    0.850  \n",
       "7     0.780    0.845  \n",
       "8     0.770    0.835  \n",
       "9     0.730    0.820  \n",
       "10    0.760    0.830  \n",
       "11    0.705    0.825  \n",
       "12    0.740    0.820  \n",
       "13    0.745    0.815  \n",
       "14    0.740    0.810  \n",
       "15    0.750    0.820  \n",
       "16    0.735    0.820  \n",
       "17    0.755    0.840  \n",
       "18    0.785    0.850  \n",
       "19    0.780    0.840  \n",
       "20    0.785    0.875  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multinomialnb_experiments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>learning_model</th>\n",
       "      <th>feature</th>\n",
       "      <th>clip_counts</th>\n",
       "      <th>negation</th>\n",
       "      <th>remove_stopwords</th>\n",
       "      <th>lemmatise</th>\n",
       "      <th>additional_features</th>\n",
       "      <th>avg_cv_acc</th>\n",
       "      <th>rmse</th>\n",
       "      <th>min_acc</th>\n",
       "      <th>max_acc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>baseline-LR</td>\n",
       "      <td>LogisticRegression(C=1.0, class_weight=None, d...</td>\n",
       "      <td>unigram</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.8660</td>\n",
       "      <td>0.021541</td>\n",
       "      <td>0.840</td>\n",
       "      <td>0.915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>param-exp-LR-1</td>\n",
       "      <td>LogisticRegression(C=1.0, class_weight=None, d...</td>\n",
       "      <td>unigram</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0.8160</td>\n",
       "      <td>0.023854</td>\n",
       "      <td>0.775</td>\n",
       "      <td>0.860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>param-exp-LR-2</td>\n",
       "      <td>LogisticRegression(C=1.0, class_weight=None, d...</td>\n",
       "      <td>unigram</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.8160</td>\n",
       "      <td>0.023854</td>\n",
       "      <td>0.775</td>\n",
       "      <td>0.860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>param-exp-LR-3</td>\n",
       "      <td>LogisticRegression(C=1.0, class_weight=None, d...</td>\n",
       "      <td>unigram</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.8120</td>\n",
       "      <td>0.020273</td>\n",
       "      <td>0.780</td>\n",
       "      <td>0.845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>param-exp-LR-4</td>\n",
       "      <td>LogisticRegression(C=1.0, class_weight=None, d...</td>\n",
       "      <td>unigram</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.7960</td>\n",
       "      <td>0.021886</td>\n",
       "      <td>0.765</td>\n",
       "      <td>0.835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>param-exp-LR-5</td>\n",
       "      <td>LogisticRegression(C=1.0, class_weight=None, d...</td>\n",
       "      <td>unigram</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0.8145</td>\n",
       "      <td>0.026311</td>\n",
       "      <td>0.775</td>\n",
       "      <td>0.860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>param-exp-LR-6</td>\n",
       "      <td>LogisticRegression(C=1.0, class_weight=None, d...</td>\n",
       "      <td>unigram</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.8145</td>\n",
       "      <td>0.026311</td>\n",
       "      <td>0.775</td>\n",
       "      <td>0.860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>param-exp-LR-7</td>\n",
       "      <td>LogisticRegression(C=1.0, class_weight=None, d...</td>\n",
       "      <td>unigram</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.8140</td>\n",
       "      <td>0.021307</td>\n",
       "      <td>0.780</td>\n",
       "      <td>0.845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>param-exp-LR-8</td>\n",
       "      <td>LogisticRegression(C=1.0, class_weight=None, d...</td>\n",
       "      <td>unigram</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.8140</td>\n",
       "      <td>0.021307</td>\n",
       "      <td>0.780</td>\n",
       "      <td>0.845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>param-exp-LR-9</td>\n",
       "      <td>LogisticRegression(C=1.0, class_weight=None, d...</td>\n",
       "      <td>unigram</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0.7785</td>\n",
       "      <td>0.030418</td>\n",
       "      <td>0.730</td>\n",
       "      <td>0.820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>param-exp-LR-10</td>\n",
       "      <td>LogisticRegression(C=1.0, class_weight=None, d...</td>\n",
       "      <td>unigram</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.7785</td>\n",
       "      <td>0.030418</td>\n",
       "      <td>0.730</td>\n",
       "      <td>0.820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>param-exp-LR-11</td>\n",
       "      <td>LogisticRegression(C=1.0, class_weight=None, d...</td>\n",
       "      <td>unigram</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.7765</td>\n",
       "      <td>0.036128</td>\n",
       "      <td>0.705</td>\n",
       "      <td>0.825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>param-exp-LR-12</td>\n",
       "      <td>LogisticRegression(C=1.0, class_weight=None, d...</td>\n",
       "      <td>unigram</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.7685</td>\n",
       "      <td>0.039437</td>\n",
       "      <td>0.700</td>\n",
       "      <td>0.825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>param-exp-LR-13</td>\n",
       "      <td>LogisticRegression(C=1.0, class_weight=None, d...</td>\n",
       "      <td>unigram</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0.7820</td>\n",
       "      <td>0.022494</td>\n",
       "      <td>0.745</td>\n",
       "      <td>0.815</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               name                                     learning_model  \\\n",
       "0       baseline-LR  LogisticRegression(C=1.0, class_weight=None, d...   \n",
       "1    param-exp-LR-1  LogisticRegression(C=1.0, class_weight=None, d...   \n",
       "2    param-exp-LR-2  LogisticRegression(C=1.0, class_weight=None, d...   \n",
       "3    param-exp-LR-3  LogisticRegression(C=1.0, class_weight=None, d...   \n",
       "4    param-exp-LR-4  LogisticRegression(C=1.0, class_weight=None, d...   \n",
       "5    param-exp-LR-5  LogisticRegression(C=1.0, class_weight=None, d...   \n",
       "6    param-exp-LR-6  LogisticRegression(C=1.0, class_weight=None, d...   \n",
       "7    param-exp-LR-7  LogisticRegression(C=1.0, class_weight=None, d...   \n",
       "8    param-exp-LR-8  LogisticRegression(C=1.0, class_weight=None, d...   \n",
       "9    param-exp-LR-9  LogisticRegression(C=1.0, class_weight=None, d...   \n",
       "10  param-exp-LR-10  LogisticRegression(C=1.0, class_weight=None, d...   \n",
       "11  param-exp-LR-11  LogisticRegression(C=1.0, class_weight=None, d...   \n",
       "12  param-exp-LR-12  LogisticRegression(C=1.0, class_weight=None, d...   \n",
       "13  param-exp-LR-13  LogisticRegression(C=1.0, class_weight=None, d...   \n",
       "\n",
       "    feature  clip_counts  negation  remove_stopwords  lemmatise  \\\n",
       "0   unigram         True     False             False      False   \n",
       "1   unigram         True      True             False       True   \n",
       "2   unigram         True      True             False       True   \n",
       "3   unigram         True      True             False      False   \n",
       "4   unigram         True      True             False      False   \n",
       "5   unigram         True     False             False       True   \n",
       "6   unigram         True     False             False       True   \n",
       "7   unigram         True     False             False      False   \n",
       "8   unigram         True     False             False      False   \n",
       "9   unigram        False      True             False       True   \n",
       "10  unigram        False      True             False       True   \n",
       "11  unigram        False      True             False      False   \n",
       "12  unigram        False      True             False      False   \n",
       "13  unigram        False     False             False       True   \n",
       "\n",
       "    additional_features  avg_cv_acc      rmse  min_acc  max_acc  \n",
       "0                 False      0.8660  0.021541    0.840    0.915  \n",
       "1                  True      0.8160  0.023854    0.775    0.860  \n",
       "2                 False      0.8160  0.023854    0.775    0.860  \n",
       "3                  True      0.8120  0.020273    0.780    0.845  \n",
       "4                 False      0.7960  0.021886    0.765    0.835  \n",
       "5                  True      0.8145  0.026311    0.775    0.860  \n",
       "6                 False      0.8145  0.026311    0.775    0.860  \n",
       "7                  True      0.8140  0.021307    0.780    0.845  \n",
       "8                 False      0.8140  0.021307    0.780    0.845  \n",
       "9                  True      0.7785  0.030418    0.730    0.820  \n",
       "10                False      0.7785  0.030418    0.730    0.820  \n",
       "11                 True      0.7765  0.036128    0.705    0.825  \n",
       "12                False      0.7685  0.039437    0.700    0.825  \n",
       "13                 True      0.7820  0.022494    0.745    0.815  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logistic_regression_experiments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Tree Experiments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>learning_model</th>\n",
       "      <th>feature</th>\n",
       "      <th>clip_counts</th>\n",
       "      <th>negation</th>\n",
       "      <th>remove_stopwords</th>\n",
       "      <th>lemmatise</th>\n",
       "      <th>additional_features</th>\n",
       "      <th>avg_cv_acc</th>\n",
       "      <th>rmse</th>\n",
       "      <th>min_acc</th>\n",
       "      <th>max_acc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>baseline-DT</td>\n",
       "      <td>DecisionTreeClassifier()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.6220</td>\n",
       "      <td>0.040878</td>\n",
       "      <td>0.535</td>\n",
       "      <td>0.675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>param-exp-DT-1</td>\n",
       "      <td>DecisionTreeClassifier()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0.8160</td>\n",
       "      <td>0.023854</td>\n",
       "      <td>0.775</td>\n",
       "      <td>0.860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>param-exp-DT-2</td>\n",
       "      <td>DecisionTreeClassifier()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.8160</td>\n",
       "      <td>0.023854</td>\n",
       "      <td>0.775</td>\n",
       "      <td>0.860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>param-exp-DT-3</td>\n",
       "      <td>DecisionTreeClassifier()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.8120</td>\n",
       "      <td>0.020273</td>\n",
       "      <td>0.780</td>\n",
       "      <td>0.845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>param-exp-DT-4</td>\n",
       "      <td>DecisionTreeClassifier()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.7960</td>\n",
       "      <td>0.021886</td>\n",
       "      <td>0.765</td>\n",
       "      <td>0.835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>param-exp-DT-5</td>\n",
       "      <td>DecisionTreeClassifier()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0.8145</td>\n",
       "      <td>0.026311</td>\n",
       "      <td>0.775</td>\n",
       "      <td>0.860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>param-exp-DT-6</td>\n",
       "      <td>DecisionTreeClassifier()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.8145</td>\n",
       "      <td>0.026311</td>\n",
       "      <td>0.775</td>\n",
       "      <td>0.860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>param-exp-DT-7</td>\n",
       "      <td>DecisionTreeClassifier()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.8140</td>\n",
       "      <td>0.021307</td>\n",
       "      <td>0.780</td>\n",
       "      <td>0.845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>param-exp-DT-8</td>\n",
       "      <td>DecisionTreeClassifier()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.8140</td>\n",
       "      <td>0.021307</td>\n",
       "      <td>0.780</td>\n",
       "      <td>0.845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>param-exp-DT-9</td>\n",
       "      <td>DecisionTreeClassifier()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0.7785</td>\n",
       "      <td>0.030418</td>\n",
       "      <td>0.730</td>\n",
       "      <td>0.820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>param-exp-DT-10</td>\n",
       "      <td>DecisionTreeClassifier()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.7785</td>\n",
       "      <td>0.030418</td>\n",
       "      <td>0.730</td>\n",
       "      <td>0.820</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               name            learning_model  feature  clip_counts  negation  \\\n",
       "0       baseline-DT  DecisionTreeClassifier()  unigram         True     False   \n",
       "1    param-exp-DT-1  DecisionTreeClassifier()  unigram         True      True   \n",
       "2    param-exp-DT-2  DecisionTreeClassifier()  unigram         True      True   \n",
       "3    param-exp-DT-3  DecisionTreeClassifier()  unigram         True      True   \n",
       "4    param-exp-DT-4  DecisionTreeClassifier()  unigram         True      True   \n",
       "5    param-exp-DT-5  DecisionTreeClassifier()  unigram         True     False   \n",
       "6    param-exp-DT-6  DecisionTreeClassifier()  unigram         True     False   \n",
       "7    param-exp-DT-7  DecisionTreeClassifier()  unigram         True     False   \n",
       "8    param-exp-DT-8  DecisionTreeClassifier()  unigram         True     False   \n",
       "9    param-exp-DT-9  DecisionTreeClassifier()  unigram        False      True   \n",
       "10  param-exp-DT-10  DecisionTreeClassifier()  unigram        False      True   \n",
       "\n",
       "    remove_stopwords  lemmatise  additional_features  avg_cv_acc      rmse  \\\n",
       "0              False      False                False      0.6220  0.040878   \n",
       "1              False       True                 True      0.8160  0.023854   \n",
       "2              False       True                False      0.8160  0.023854   \n",
       "3              False      False                 True      0.8120  0.020273   \n",
       "4              False      False                False      0.7960  0.021886   \n",
       "5              False       True                 True      0.8145  0.026311   \n",
       "6              False       True                False      0.8145  0.026311   \n",
       "7              False      False                 True      0.8140  0.021307   \n",
       "8              False      False                False      0.8140  0.021307   \n",
       "9              False       True                 True      0.7785  0.030418   \n",
       "10             False       True                False      0.7785  0.030418   \n",
       "\n",
       "    min_acc  max_acc  \n",
       "0     0.535    0.675  \n",
       "1     0.775    0.860  \n",
       "2     0.775    0.860  \n",
       "3     0.780    0.845  \n",
       "4     0.765    0.835  \n",
       "5     0.775    0.860  \n",
       "6     0.775    0.860  \n",
       "7     0.780    0.845  \n",
       "8     0.780    0.845  \n",
       "9     0.730    0.820  \n",
       "10    0.730    0.820  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decision_tree_experiments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest Experiments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>learning_model</th>\n",
       "      <th>feature</th>\n",
       "      <th>clip_counts</th>\n",
       "      <th>negation</th>\n",
       "      <th>remove_stopwords</th>\n",
       "      <th>lemmatise</th>\n",
       "      <th>additional_features</th>\n",
       "      <th>avg_cv_acc</th>\n",
       "      <th>rmse</th>\n",
       "      <th>min_acc</th>\n",
       "      <th>max_acc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>baseline-RF</td>\n",
       "      <td>RandomForestClassifier()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.8180</td>\n",
       "      <td>0.029343</td>\n",
       "      <td>0.780</td>\n",
       "      <td>0.875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>param-exp-RF-1</td>\n",
       "      <td>RandomForestClassifier()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0.8160</td>\n",
       "      <td>0.023854</td>\n",
       "      <td>0.775</td>\n",
       "      <td>0.860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>param-exp-RF-2</td>\n",
       "      <td>RandomForestClassifier()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.8160</td>\n",
       "      <td>0.023854</td>\n",
       "      <td>0.775</td>\n",
       "      <td>0.860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>param-exp-RF-3</td>\n",
       "      <td>RandomForestClassifier()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.8120</td>\n",
       "      <td>0.020273</td>\n",
       "      <td>0.780</td>\n",
       "      <td>0.845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>param-exp-RF-4</td>\n",
       "      <td>RandomForestClassifier()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.7960</td>\n",
       "      <td>0.021886</td>\n",
       "      <td>0.765</td>\n",
       "      <td>0.835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>param-exp-RF-5</td>\n",
       "      <td>RandomForestClassifier()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0.8145</td>\n",
       "      <td>0.026311</td>\n",
       "      <td>0.775</td>\n",
       "      <td>0.860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>param-exp-RF-6</td>\n",
       "      <td>RandomForestClassifier()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.8145</td>\n",
       "      <td>0.026311</td>\n",
       "      <td>0.775</td>\n",
       "      <td>0.860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>param-exp-RF-7</td>\n",
       "      <td>RandomForestClassifier()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.8140</td>\n",
       "      <td>0.021307</td>\n",
       "      <td>0.780</td>\n",
       "      <td>0.845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>param-exp-RF-8</td>\n",
       "      <td>RandomForestClassifier()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.8140</td>\n",
       "      <td>0.021307</td>\n",
       "      <td>0.780</td>\n",
       "      <td>0.845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>param-exp-RF-9</td>\n",
       "      <td>RandomForestClassifier()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0.7785</td>\n",
       "      <td>0.030418</td>\n",
       "      <td>0.730</td>\n",
       "      <td>0.820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>param-exp-RF-10</td>\n",
       "      <td>RandomForestClassifier()</td>\n",
       "      <td>unigram</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.7785</td>\n",
       "      <td>0.030418</td>\n",
       "      <td>0.730</td>\n",
       "      <td>0.820</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               name            learning_model  feature  clip_counts  negation  \\\n",
       "0       baseline-RF  RandomForestClassifier()  unigram         True     False   \n",
       "1    param-exp-RF-1  RandomForestClassifier()  unigram         True      True   \n",
       "2    param-exp-RF-2  RandomForestClassifier()  unigram         True      True   \n",
       "3    param-exp-RF-3  RandomForestClassifier()  unigram         True      True   \n",
       "4    param-exp-RF-4  RandomForestClassifier()  unigram         True      True   \n",
       "5    param-exp-RF-5  RandomForestClassifier()  unigram         True     False   \n",
       "6    param-exp-RF-6  RandomForestClassifier()  unigram         True     False   \n",
       "7    param-exp-RF-7  RandomForestClassifier()  unigram         True     False   \n",
       "8    param-exp-RF-8  RandomForestClassifier()  unigram         True     False   \n",
       "9    param-exp-RF-9  RandomForestClassifier()  unigram        False      True   \n",
       "10  param-exp-RF-10  RandomForestClassifier()  unigram        False      True   \n",
       "\n",
       "    remove_stopwords  lemmatise  additional_features  avg_cv_acc      rmse  \\\n",
       "0              False      False                False      0.8180  0.029343   \n",
       "1              False       True                 True      0.8160  0.023854   \n",
       "2              False       True                False      0.8160  0.023854   \n",
       "3              False      False                 True      0.8120  0.020273   \n",
       "4              False      False                False      0.7960  0.021886   \n",
       "5              False       True                 True      0.8145  0.026311   \n",
       "6              False       True                False      0.8145  0.026311   \n",
       "7              False      False                 True      0.8140  0.021307   \n",
       "8              False      False                False      0.8140  0.021307   \n",
       "9              False       True                 True      0.7785  0.030418   \n",
       "10             False       True                False      0.7785  0.030418   \n",
       "\n",
       "    min_acc  max_acc  \n",
       "0     0.780    0.875  \n",
       "1     0.775    0.860  \n",
       "2     0.775    0.860  \n",
       "3     0.780    0.845  \n",
       "4     0.765    0.835  \n",
       "5     0.775    0.860  \n",
       "6     0.775    0.860  \n",
       "7     0.780    0.845  \n",
       "8     0.780    0.845  \n",
       "9     0.730    0.820  \n",
       "10    0.730    0.820  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_forest_experiments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### From the evaluation tables above, we can see that the baseline logistic regression model worked best"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>learning_model</th>\n",
       "      <th>feature</th>\n",
       "      <th>clip_counts</th>\n",
       "      <th>negation</th>\n",
       "      <th>remove_stopwords</th>\n",
       "      <th>lemmatise</th>\n",
       "      <th>additional_features</th>\n",
       "      <th>avg_cv_acc</th>\n",
       "      <th>rmse</th>\n",
       "      <th>min_acc</th>\n",
       "      <th>max_acc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>baseline-LR</td>\n",
       "      <td>LogisticRegression(C=1.0, class_weight=None, d...</td>\n",
       "      <td>unigram</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.866</td>\n",
       "      <td>0.021541</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.915</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          name                                     learning_model  feature  \\\n",
       "0  baseline-LR  LogisticRegression(C=1.0, class_weight=None, d...  unigram   \n",
       "\n",
       "   clip_counts  negation  remove_stopwords  lemmatise  additional_features  \\\n",
       "0         True     False             False      False                False   \n",
       "\n",
       "   avg_cv_acc      rmse  min_acc  max_acc  \n",
       "0       0.866  0.021541     0.84    0.915  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logistic_regression_experiments.iloc[[0],]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}