TF-IDF_max_features.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "dd490bc2-0b2b-4583-b01c-7db8a8632ba7",
   "metadata": {},
   "source": [
    "# TF-IDF: Max Features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "878db841-b044-482d-a455-6c82f8a552a9",
   "metadata": {},
   "source": [
    "This notebook tests different max feature values 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 seem to suggest that a higher max features value leads to better results."
   ]
  },
  {
   "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",
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      "[LibSVM]Building estimator 7 of 10 for this parallel run (total 10)...\n",
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      "[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]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(max_features):\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=(1, 2),\n",
    "    norm='l1', \n",
    "    use_idf=False, \n",
    "    smooth_idf=False,\n",
    "    max_features=max_features,\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 max feature, vectorising text, fitting a model and evaluating performance\n",
    "max_features = [100, 200, 300, 400, 500]\n",
    "averages = []\n",
    "for max_feature in max_features:\n",
    "    score = fit_model(max_feature)\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 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# plots the results\n",
    "plt.figure(figsize=(12, 6));\n",
    "plt.plot(max_features, averages, marker='o', linestyle='-');\n",
    "plt.xlabel('Max features');\n",
    "plt.ylabel('Cohen\\'s Kappa Score');\n",
    "plt.title('Max features', 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
}