{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    import nltk\n",
    "except ModuleNotFoundError:\n",
    "    !pip install nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "## This code downloads the required packages.\n",
    "## You can run `nltk.download('all')` to download everything.\n",
    "\n",
    "nltk_packages = [\n",
    "    (\"reuters\", \"corpora/reuters.zip\")\n",
    "]\n",
    "\n",
    "for pid, fid in nltk_packages:\n",
    "    try:\n",
    "        nltk.data.find(fid)\n",
    "    except LookupError:\n",
    "        nltk.download(pid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting up corpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.corpus import reuters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting up train/test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_documents, train_categories = zip(*[(reuters.raw(i), reuters.categories(i)) for i in reuters.fileids() if i.startswith('training/')])\n",
    "test_documents, test_categories = zip(*[(reuters.raw(i), reuters.categories(i)) for i in reuters.fileids() if i.startswith('test/')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_categories = sorted(list(set(reuters.categories())))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following cell defines a function **tokenize** that performs following actions:\n",
    "- Receive a document as an argument to the function\n",
    "- Tokenize the document using `nltk.word_tokenize()`\n",
    "- Use `PorterStemmer` provided by the `nltk` to remove morphological affixes from each token\n",
    "- Append stemmed token to an already defined list `stems`\n",
    "- Return the list `stems`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.stem.porter import PorterStemmer\n",
    "def tokenize(text):\n",
    "    tokens = nltk.word_tokenize(text)\n",
    "    stems = []\n",
    "    for item in tokens:\n",
    "        stems.append(PorterStemmer().stem(item))\n",
    "    return stems"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To begin, I first used TF-IDF for feature selection on both train as well as test data using `TfidfVectorizer`.\n",
    "\n",
    "But first, What `TfidfVectorizer` actually does?\n",
    "- `TfidfVectorizer` converts a collection of raw documents to a matrix of **TF-IDF** features.\n",
    "\n",
    "**TF-IDF**?\n",
    "- TFIDF (abbreviation of the term *frequency–inverse document frequency*) is a numerical statistic that is intended to reflect how important a word is to a document in a collection or corpus. [tf–idf](https://en.wikipedia.org/wiki/Tf%E2%80%93idf)\n",
    "\n",
    "**Why `TfidfVectorizer`**?\n",
    "- `TfidfVectorizer` scale down the impact of tokens that occur very frequently (e.g., “a”, “the”, and “of”) in a given corpus. [Feature Extraction and Transformation](https://spark.apache.org/docs/latest/mllib-feature-extraction.html#tf-idf)\n",
    "\n",
    "I gave following two arguments to `TfidfVectorizer`:\n",
    "- tokenizer: `tokenize` function\n",
    "- stop_words\n",
    "\n",
    "Then I used `fit_transform` and `transform` on the train and test documents repectively.\n",
    "\n",
    "**Why `fit_transform` for training data while `transform` for test data**?\n",
    "\n",
    "To avoid data leakage during cross-validation, imputer computes the statistic on the train data during the `fit`, **stores it** and uses the same on the test data, during the `transform`. This also prevents the test data from appearing in `fit` operation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "vectorizer = TfidfVectorizer(tokenizer = tokenize, stop_words = 'english')\n",
    "\n",
    "vectorised_train_documents = vectorizer.fit_transform(train_documents)\n",
    "vectorised_test_documents = vectorizer.transform(test_documents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the **efficient implementation** of machine learning algorithms, many machine learning algorithms **requires all input variables and output variables to be numeric**. This means that categorical data must be converted to a numerical form.\n",
    "\n",
    "For this purpose, I used `MultiLabelBinarizer` from `sklearn.preprocessing`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MultiLabelBinarizer\n",
    "\n",
    "mlb = MultiLabelBinarizer()\n",
    "train_labels = mlb.fit_transform(train_categories)\n",
    "test_labels = mlb.transform(test_categories)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, To **train** the classifier, I used `LinearSVC` in combination with the `OneVsRestClassifier` function in the scikit-learn package.\n",
    "\n",
    "The strategy of `OneVsRestClassifier` is of **fitting one classifier per label** and the `OneVsRestClassifier` can efficiently do this task and also outputs are easy to interpret. Since each label is represented by **one and only one classifier**, it is possible to gain knowledge about the label by inspecting its corresponding classifier. [OneVsRestClassifier](http://scikit-learn.org/stable/modules/multiclass.html#one-vs-the-rest)\n",
    "\n",
    "The reason I combined `LinearSVC` with `OneVsRestClassifier` is because `LinearSVC` supports **Multi-class**, while we want to perform **Multi-label** classification."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "classifier = OneVsRestClassifier(LinearSVC())\n",
    "classifier.fit(vectorised_train_documents, train_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After fitting the classifier, I decided to use `cross_val_score` to **measure score** of the classifier by **cross validation** on the training data. But the only problem was, I wanted to **shuffle** data to use with `cross_val_score`, but it does not support shuffle argument.\n",
    "\n",
    "So, I decided to use `KFold` with `cross_val_score` as `KFold` supports shuffling the data.\n",
    "\n",
    "I also enabled `random_state`, because `random_state` will guarantee the same output in each run. By setting the `random_state`, it is guaranteed that the pseudorandom number generator will generate the same sequence of random integers each time, which in turn will affect the split.\n",
    "\n",
    "Why **42**?\n",
    "- [Why '42' is the preferred number when indicating something random?](https://softwareengineering.stackexchange.com/questions/507/why-42-is-the-preferred-number-when-indicating-something-random)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "from sklearn.model_selection import KFold, cross_val_score\n",
    "\n",
    "kf = KFold(n_splits=10, random_state = 42, shuffle = True)\n",
    "scores = cross_val_score(classifier, vectorised_train_documents, train_labels, cv = kf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-validation scores: [0.83655084 0.86743887 0.8043758  0.83011583 0.83655084 0.81724582\n",
      " 0.82754183 0.8030888  0.80694981 0.82731959]\n",
      "Cross-validation accuracy: 0.8257 (+/- 0.0368)\n"
     ]
    }
   ],
   "source": [
    "print('Cross-validation scores:', scores)\n",
    "print('Cross-validation accuracy: {:.4f} (+/- {:.4f})'.format(scores.mean(), scores.std() * 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the end, I used different methods (`accuracy_score`, `precision_score`, `recall_score`, `f1_score` and `confusion_matrix`) provided by scikit-learn **to evaluate** the classifier. (both *Macro-* and *Micro-averages*)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix\n",
    "\n",
    "predictions = classifier.predict(vectorised_test_documents)\n",
    "\n",
    "accuracy = accuracy_score(test_labels, predictions)\n",
    "\n",
    "macro_precision = precision_score(test_labels, predictions, average='macro')\n",
    "macro_recall = recall_score(test_labels, predictions, average='macro')\n",
    "macro_f1 = f1_score(test_labels, predictions, average='macro')\n",
    "\n",
    "micro_precision = precision_score(test_labels, predictions, average='micro')\n",
    "micro_recall = recall_score(test_labels, predictions, average='micro')\n",
    "micro_f1 = f1_score(test_labels, predictions, average='micro')\n",
    "\n",
    "cm = confusion_matrix(test_labels.argmax(axis = 1), predictions.argmax(axis = 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8099\n",
      "Precision:\n",
      "- Macro: 0.6076\n",
      "- Micro: 0.9471\n",
      "Recall:\n",
      "- Macro: 0.3708\n",
      "- Micro: 0.7981\n",
      "F1-measure:\n",
      "- Macro: 0.4410\n",
      "- Micro: 0.8662\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy: {:.4f}\\nPrecision:\\n- Macro: {:.4f}\\n- Micro: {:.4f}\\nRecall:\\n- Macro: {:.4f}\\n- Micro: {:.4f}\\nF1-measure:\\n- Macro: {:.4f}\\n- Micro: {:.4f}\".format(accuracy, macro_precision, micro_precision, macro_recall, micro_recall, macro_f1, micro_f1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In below cell, I used `matplotlib.pyplot` to **plot the confusion matrix** (of first *few results only* to keep the readings readable) using `heatmap` of `seaborn`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24d8cf39f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "import pandas as pd\n",
    "\n",
    "cm_plt = pd.DataFrame(cm[:73])\n",
    "\n",
    "plt.figure(figsize = (25, 25))\n",
    "ax = plt.axes()\n",
    "\n",
    "sb.heatmap(cm_plt, annot=True)\n",
    "\n",
    "ax.xaxis.set_ticks_position('top')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, I took the data from [Coconut - Wikipedia](https://en.wikipedia.org/wiki/Coconut) to check if the classifier is able to **correctly** predict the label(s) or not.\n",
    "\n",
    "And here is the output:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Example labels: [('coconut', 'oilseed')]\n"
     ]
    }
   ],
   "source": [
    "example_text = '''The coconut tree (Cocos nucifera) is a member of the family Arecaceae (palm family) and the only species of the genus Cocos.\n",
    "The term coconut can refer to the whole coconut palm or the seed, or the fruit, which, botanically, is a drupe, not a nut.\n",
    "The spelling cocoanut is an archaic form of the word.\n",
    "The term is derived from the 16th-century Portuguese and Spanish word coco meaning \"head\" or \"skull\", from the three indentations on the coconut shell that resemble facial features.\n",
    "Coconuts are known for their versatility ranging from food to cosmetics.\n",
    "They form a regular part of the diets of many people in the tropics and subtropics.\n",
    "Coconuts are distinct from other fruits for their endosperm containing a large quantity of water (also called \"milk\"), and when immature, may be harvested for the potable coconut water.\n",
    "When mature, they can be used as seed nuts or processed for oil, charcoal from the hard shell, and coir from the fibrous husk.\n",
    "When dried, the coconut flesh is called copra.\n",
    "The oil and milk derived from it are commonly used in cooking and frying, as well as in soaps and cosmetics.\n",
    "The husks and leaves can be used as material to make a variety of products for furnishing and decorating.\n",
    "The coconut also has cultural and religious significance in certain societies, particularly in India, where it is used in Hindu rituals.'''\n",
    "\n",
    "example_preds = classifier.predict(vectorizer.transform([example_text]))\n",
    "example_labels = mlb.inverse_transform(example_preds)\n",
    "print(\"Example labels: {}\".format(example_labels))"
   ]
  }
 ],
 "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}