TF-IDF_Ngram_range.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "dd490bc2-0b2b-4583-b01c-7db8a8632ba7",
   "metadata": {},
   "source": [
    "# TF-IDF: N-gram range"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "878db841-b044-482d-a455-6c82f8a552a9",
   "metadata": {},
   "source": [
    "This notebook tests different n-gram ranges (sequences of words/numbers/symbols) when using TF-IDF and a bagging classifier on a [Kaggle essay scores](https://www.kaggle.com/competitions/learning-agency-lab-automated-essay-scoring-2) dataset of ~17,000 essays. The results suggest that including bigrams (an n-gram range of 2) in the TF-IDF vectorizer is sufficient for this task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9b7c5264-5672-4de3-8dd1-7a446e67d466",
   "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>essay_id</th>\n",
       "      <th>full_text</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>000d118</td>\n",
       "      <td>Many people have car where they live. The thin...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000fe60</td>\n",
       "      <td>I am a scientist at NASA that is discussing th...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>001ab80</td>\n",
       "      <td>People always wish they had the same technolog...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>001bdc0</td>\n",
       "      <td>We all heard about Venus, the planet without a...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>002ba53</td>\n",
       "      <td>Dear, State Senator\\n\\nThis is a letter to arg...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  essay_id                                          full_text  score\n",
       "0  000d118  Many people have car where they live. The thin...      3\n",
       "1  000fe60  I am a scientist at NASA that is discussing th...      3\n",
       "2  001ab80  People always wish they had the same technolog...      4\n",
       "3  001bdc0  We all heard about Venus, the planet without a...      4\n",
       "4  002ba53  Dear, State Senator\\n\\nThis is a letter to arg...      3"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "train = pd.read_csv('data/train.csv')\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69f5a768-ce73-4c9d-b813-8f629e18203d",
   "metadata": {},
   "source": [
    "## Preprocess Text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "31635325-de28-4070-9d4d-78adc18ea79a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "import string\n",
    "import nltk\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "\n",
    "def preprocess_text(text):\n",
    "\n",
    "    # removes unprintable characters\n",
    "    unprintable = r\"[^\\w\\s]\"\n",
    "    text = re.sub(unprintable, '', text)\n",
    "\n",
    "    # removes non-breaking space characters\n",
    "    non_breaking = '\\xa0'\n",
    "    text = re.sub(non_breaking, ' ', text)\n",
    "\n",
    "    # removes newline characters\n",
    "    newline = '\\n'\n",
    "    text = re.sub(newline, ' ', text)\n",
    "\n",
    "    # removes punctuation\n",
    "    text = text.translate(str.maketrans('', '', string.punctuation))\n",
    "    \n",
    "    # lowercase the text\n",
    "    text = text.lower()\n",
    "    \n",
    "    # tokenization\n",
    "    tokens = word_tokenize(text)\n",
    "    \n",
    "    # lemmatization\n",
    "    lemmatizer = WordNetLemmatizer()\n",
    "    tokens = [lemmatizer.lemmatize(token) for token in tokens]\n",
    "    \n",
    "    # join tokens back into a string\n",
    "    preprocessed_text = ' '.join(tokens)\n",
    "    \n",
    "    return preprocessed_text\n",
    "\n",
    "train['full_text'] = [preprocess_text(x) for x in train['full_text']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7e4f658-9ddf-4d51-add2-11c766aff5a7",
   "metadata": {},
   "source": [
    "## Drop misspellings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "25ca1d1b-702c-4d75-b310-08e52cbdceaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "from spellchecker import SpellChecker\n",
    "\n",
    "# creates a spellckecker object\n",
    "spell = SpellChecker()\n",
    "\n",
    "# returns a count of misspelled words\n",
    "def misspellings(essay, drop=True):\n",
    "\n",
    "    # the essay is split into words\n",
    "    words = essay.split()\n",
    "\n",
    "    # either drops or corrects unknown words\n",
    "    final_words = []\n",
    "    if drop == True:\n",
    "        # loops through each word\n",
    "        for word in words:\n",
    "            \n",
    "            # checks if the word is known\n",
    "            if spell.known([word]):\n",
    "                \n",
    "                # appends to a list of words to return\n",
    "                final_words.append(word)\n",
    "    \n",
    "    else:\n",
    "        # loops through each word\n",
    "        for word in words:\n",
    "            \n",
    "            # checks if the word is known\n",
    "            if spell.known([word]):\n",
    "                \n",
    "                # appends to a list of words to return\n",
    "                final_words.append(word)\n",
    "\n",
    "            else:\n",
    "                # attempts to correct the word\n",
    "                correction = spell.correction(word)\n",
    "\n",
    "                # appends the correction if one was found\n",
    "                if correction != None:\n",
    "                    # appends to a list of words to return\n",
    "                    final_words.append(correction)\n",
    "\n",
    "    # joins the words back together with a space \n",
    "    return ' '.join(final_words)\n",
    "\n",
    "train['full_text'] = [misspellings(x) for x in train['full_text']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2398751a-51d9-4164-9378-0640f1c4b9c7",
   "metadata": {},
   "source": [
    "## Fit model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7b07babd-4a86-4990-8fbb-ab22f1384359",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Building estimator 1 of 10 for this parallel run (total 10)...\n",
      "[LibSVM]Building estimator 2 of 10 for this parallel run (total 10)...\n",
      "[LibSVM]Building estimator 3 of 10 for this parallel run (total 10)...\n",
      "[LibSVM]Building estimator 4 of 10 for this parallel run (total 10)...\n",
      "[LibSVM]Building estimator 5 of 10 for this parallel run (total 10)...\n",
      "[LibSVM]Building estimator 6 of 10 for this parallel run (total 10)...\n",
      "[LibSVM]Building estimator 7 of 10 for this parallel run (total 10)...\n",
      "[LibSVM]Building estimator 8 of 10 for this parallel run (total 10)...\n",
      "[LibSVM]Building estimator 9 of 10 for this parallel run (total 10)...\n",
      "[LibSVM]Building estimator 10 of 10 for this parallel run (total 10)...\n",
      "[LibSVM]Building estimator 1 of 10 for this parallel run (total 10)...\n",
      "[LibSVM]Building estimator 2 of 10 for this parallel run (total 10)...\n",
      "[LibSVM]Building estimator 3 of 10 for this parallel run (total 10)...\n",
      "[LibSVM]Building estimator 4 of 10 for this parallel run (total 10)...\n",
      "[LibSVM]Building estimator 5 of 10 for this parallel run (total 10)...\n",
      "[LibSVM]Building estimator 6 of 10 for this parallel run (total 10)...\n",
      "[LibSVM]Building estimator 7 of 10 for this parallel run (total 10)...\n",
      "[LibSVM]Building estimator 8 of 10 for this parallel run (total 10)...\n",
      "[LibSVM]Building estimator 9 of 10 for this parallel run (total 10)...\n",
      "[LibSVM]Building estimator 10 of 10 for this parallel run (total 10)...\n",
      "[LibSVM]Building estimator 1 of 10 for this parallel run (total 10)...\n",
      "[LibSVM]Building estimator 2 of 10 for this parallel run (total 10)...\n",
      "[LibSVM]Building estimator 3 of 10 for this parallel run (total 10)...\n",
      "[LibSVM]Building estimator 4 of 10 for this parallel run (total 10)...\n",
      "[LibSVM]Building estimator 5 of 10 for this parallel run (total 10)...\n",
      "[LibSVM]Building estimator 6 of 10 for this parallel run (total 10)...\n",
      "[LibSVM]Building estimator 7 of 10 for this parallel run (total 10)...\n",
      "[LibSVM]Building estimator 8 of 10 for this parallel run (total 10)...\n",
      "[LibSVM]Building estimator 9 of 10 for this parallel run (total 10)...\n",
      "[LibSVM]Building estimator 10 of 10 for this parallel run (total 10)...\n",
      "[LibSVM]"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import BaggingClassifier  \n",
    "from sklearn.svm import SVC  \n",
    "from sklearn.metrics import cohen_kappa_score\n",
    "\n",
    "def fit_model(ngram_range):\n",
    "\n",
    "    # a text vectorizer with a variable n-gram range is specified\n",
    "    text_vectorizer = TfidfVectorizer(\n",
    "    stop_words='english',\n",
    "    sublinear_tf=False,\n",
    "    strip_accents='unicode',\n",
    "    binary=True,\n",
    "    analyzer='word',\n",
    "    token_pattern=r'\\w{2,}',  \n",
    "    ngram_range=eval(ngram_range),\n",
    "    norm='l1', \n",
    "    use_idf=False, \n",
    "    smooth_idf=False,\n",
    "    max_features=600000,\n",
    "    min_df=30)\n",
    "\n",
    "    # vectorises the train text\n",
    "    x = text_vectorizer.fit_transform(train['full_text'])\n",
    "    y = train['score']\n",
    "\n",
    "    # splits the data into training and test sets\n",
    "    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=123)\n",
    "    \n",
    "    # fits a bagging model \n",
    "    model = BaggingClassifier(estimator=SVC(C=1.75, \n",
    "                                            kernel='rbf', \n",
    "                                            gamma='scale', \n",
    "                                            decision_function_shape='ovr', \n",
    "                                            random_state=123, \n",
    "                                            tol=1e-5, \n",
    "                                            shrinking=True, \n",
    "                                            verbose=True, \n",
    "                                            break_ties=True), \n",
    "                              n_estimators=10, \n",
    "                              random_state=123, \n",
    "                              verbose=3) \n",
    "    \n",
    "    model.fit(x_train, y_train.values.ravel())\n",
    "\n",
    "    # predicts test values\n",
    "    y_pred_bag = model.predict(x_test)\n",
    "\n",
    "    # evaluates predictions with the quadratic weighted Cohen Kappa\n",
    "    score = cohen_kappa_score(y_test.values.ravel(), y_pred_bag.ravel(), weights='quadratic')\n",
    "    \n",
    "    return score\n",
    "    \n",
    "# loops through each ngram range by vectorising text, fitting a model and evaluating performance\n",
    "ngram_ranges = ['(1, 1)', '(1, 2)', '(1, 3)']\n",
    "confusion_matrices = []\n",
    "averages = []\n",
    "for ngram_range in ngram_ranges:\n",
    "    score = fit_model(ngram_range)\n",
    "    averages.append(score)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfe1772f-8a6d-4c9a-8454-6582e4ae462c",
   "metadata": {},
   "source": [
    "## Visualize results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "945e2c4e-5368-4f53-903b-286bb4af2168",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# plots the results\n",
    "plt.figure(figsize=(10, 6));\n",
    "plt.plot(ngram_ranges, averages, marker='o', linestyle='-');\n",
    "plt.xlabel('N-Gram Range');\n",
    "plt.ylabel('Cohen\\'s Kappa Score');\n",
    "plt.title('N-Gram Range', size=30);\n",
    "plt.grid(True);\n",
    "plt.show();"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}