{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fantastic-invitation",
   "metadata": {},
   "source": [
    "# Lab 4: Text Processing\n",
    "*Due: Friday April 1st*\n",
    "\n",
    "\n",
    "In this lab, you will use some of the text similarity concepts presented in lecture for two simple applications: sentence completion, and a basic question-and-answer service."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edf795b7-47d4-44c4-90ef-c8b29315e001",
   "metadata": {},
   "source": [
    "## Part 1: Text similarity fundamentals (20 pts)\n",
    "\n",
    "In this part of the lab, we will walk you through some of the text similarity approaches presented in lecture once again, as a basis for the later parts. The overall goal of this part is to determine the similarity bewtween pairs of sentences drawn from `korn_sen` below."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb001479-220e-4d5b-899b-546dea6c8fd2",
   "metadata": {},
   "source": [
    "### 1.1 Setup\n",
    "First, run the cells below to import necessary packages and define helper functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "128475e5-65dc-4e5f-9ebe-6a5d4e71aebf",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.9/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm as cm\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "import sklearn.feature_extraction\n",
    "import nltk.stem.porter \n",
    "from nltk.corpus import stopwords\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "from sentence_transformers import SentenceTransformer\n",
    "import re, json, requests\n",
    "\n",
    "np.set_printoptions(precision=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "53312bee-43c7-4f64-b024-5f051ef176fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "korn_sen = [\n",
    "    \"Tim loves the band Korn.\",\n",
    "    \"Tim adores the rock group Korn.\",\n",
    "    \"Tim loves eating corn.\",\n",
    "    \"Tim used to love Korn, but now he hates them.\",\n",
    "    \"Tim absolutely loves Korn.\",\n",
    "    \"Tim completely detests the performers named Korn\",\n",
    "    \"Tim has a deep passion for the outfit the goes by the name of Korn\",\n",
    "    \"Tim loves listening to the band Korn while eating corn.\"\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c83cfed6-8328-4152-955f-141e90bd159c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_sim_matrix(m,sens):\n",
    "    cmap = cm.get_cmap('RdYlGn')\n",
    "    fig, ax = plt.subplots(figsize=(8,8))\n",
    "    cax = ax.matshow(m, interpolation='nearest', cmap=cmap)\n",
    "    ax.grid(True)\n",
    "    plt.title('San Francisco Similarity matrix')\n",
    "    plt.xticks(range(len(sens)), sens, rotation=90);\n",
    "    plt.yticks(range(len(sens)), sens);\n",
    "    fig.colorbar(cax, ticks=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, .75,.8,.85,.90,.95,1])\n",
    "    plt.show()\n",
    "\n",
    "stemmer = nltk.stem.porter.PorterStemmer()\n",
    "def stemmed_words(doc):\n",
    "    return (stemmer.stem(w) for w in analyzer(doc))\n",
    "\n",
    "analyzer = sklearn.feature_extraction.text.CountVectorizer(stop_words='english').build_analyzer()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97eca2e1-1ea3-4709-9985-02ed44b57880",
   "metadata": {},
   "source": [
    "### 1.2 Jaccard vs Cosine similarity (10 pts)\n",
    "A simple appoach to tokenization would follow the bag-of-words model, vectorizing each sentence based on token counts. Run the code below to perform this task. Then, compute the matrix of Jaccard and cosine similarities among the sentences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "89bf16d0-b98a-4c9a-9306-ed8ad27f477a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def jaccard(s1, s2):\n",
    "    j = float(len(s1.intersection(s2))) / float(len(s1.union(s2)))\n",
    "    return j\n",
    "\n",
    "def bag_jaccard(sen):\n",
    "    out = np.zeros((len(sen),len(sen)))\n",
    "    for i in range(len(sen)):\n",
    "        s = re.sub(r'[^\\w\\s]', '', sen[i])\n",
    "        sen1 = set(s.split(\" \"))\n",
    "        for j in range(len(sen)):\n",
    "            s = re.sub(r'[^\\w\\s]', '', sen[j])\n",
    "            sen2 = set(s.split(\" \"))\n",
    "\n",
    "            out[i][j] = jaccard(sen1, sen2)\n",
    "\n",
    "    plot_sim_matrix(out, sen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fea0fdfb-1408-4240-b1ce-eb9862102972",
   "metadata": {},
   "outputs": [],
   "source": [
    "def bag_cosine(sen):\n",
    "\n",
    "    f = sklearn.feature_extraction.text.CountVectorizer(analyzer=stemmed_words)\n",
    "    \n",
    "    #Count vectorizer translates each document into a vector of counts\n",
    "    X = f.fit_transform(sen)\n",
    "\n",
    "    print(f.get_feature_names_out())\n",
    "\n",
    "    #cosine_similarity computes the cosine similarity between\n",
    "    #a set of vectors\n",
    "    cos_sim = cosine_similarity(X)\n",
    "\n",
    "    plot_sim_matrix(cos_sim, sen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d9ef3041-3b15-4828-8c90-5b25df3c6b9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bag_jaccard(korn_sen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e5ab1cdc-d062-4ca0-b908-8c2769f2d384",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['absolut' 'ador' 'band' 'complet' 'corn' 'deep' 'detest' 'eat' 'goe'\n",
      " 'group' 'hate' 'korn' 'listen' 'love' 'name' 'outfit' 'passion' 'perform'\n",
      " 'rock' 'tim' 'use']\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bag_cosine(korn_sen)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f7d83c9-2b7b-4fc3-9064-a6b4eafb74bc",
   "metadata": {},
   "source": [
    "**Question (10 pts)**: Contrast the two similarity matrices above, explaining the technical source of the discrepancies. You don't need to explain every pair of sentences for which they differ; provide examples and the general principle. What types of input would each similarity metric be best suited for?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4be32a7a-c300-4d13-9e2e-dca10d1295ac",
   "metadata": {},
   "source": [
    "### 1.3 TF/IDF (5 pts)\n",
    "\n",
    "A more elaborate approach, in contrast to bag-of-words, is TF/IDF. Below are two ways to implement it in Python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "03c69fe1-1ba9-40be-b19c-fd53d4ec0a88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def do_tf_idf_full(sen):\n",
    "    #from scratch\n",
    "    f = sklearn.feature_extraction.text.CountVectorizer(analyzer=stemmed_words)\n",
    "    X = f.fit_transform(sen)\n",
    "    cnts_ar = np.array(X.toarray()).sum(axis=0)\n",
    "\n",
    "    doc_len_ar = np.array(X.toarray()).sum(axis=1)\n",
    "    #should count number of documents where term appears, not just sum, \n",
    "    # but ok because our corpus doesn't repeat words\n",
    "    idf_ar = np.log((1.0/cnts_ar) * len(sen)) + 1\n",
    "    tdf_out = (np.array(X.toarray()).T/(doc_len_ar)).T * idf_ar\n",
    "    cos_sim = cosine_similarity(tdf_out)\n",
    "\n",
    "def do_tf_idf_sklearn(sen):\n",
    "    #using sklearn\n",
    "    f  = sklearn.feature_extraction.text.TfidfVectorizer(analyzer=stemmed_words,smooth_idf=False,norm='l1')\n",
    "    X = f.fit_transform(sen)\n",
    "    cos_sim = cosine_similarity(X)\n",
    "    plot_sim_matrix(cos_sim, sen)\n",
    "\n",
    "do_tf_idf_sklearn(korn_sen)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cff39e8a-1e57-45e3-8151-acbbe780fbf8",
   "metadata": {},
   "source": [
    "**Question (5 pts)**: Describe the quality of the output and compare it to part 1.2. Which of the drawbacks of bag-of-words can TF/IDF help mitigate, if any? "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d1c82f0-aa69-423e-9f36-88712556a108",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 1.4 BERT (5 pts)\n",
    "\n",
    "Finally, we can use a more sophisticated model like BERT to produce the similarity matrix among the given sentences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "883f2413-bdb6-43f6-82c6-477091bd5987",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
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     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def do_bert(sen):\n",
    "\n",
    "    model = SentenceTransformer('all-mpnet-base-v2')\n",
    "    sen_embeddings = model.encode(sen)\n",
    "\n",
    "    #let's calculate cosine similarity for sentence 0:\n",
    "    cos_sim = cosine_similarity(sen_embeddings)\n",
    "\n",
    "    plot_sim_matrix(cos_sim, sen)\n",
    "\n",
    "do_bert(korn_sen)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66161ac6-be3e-4d27-ab76-1b2987959f37",
   "metadata": {},
   "source": [
    "**Question (5 pts)**: Describe the quality of the output and compare it to the previous 2 parts. Are there any sentences that remain difficult for BERT?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49b20748-c192-469e-ae08-1101c6da6f47",
   "metadata": {},
   "source": [
    "## Part 2: Sentence completion (35 pts)\n",
    "\n",
    "In this part of the lab, you will use text similarity in the context of a sentence completion task. Given a dataset of sentences, and the beginning of a sentence, your goal will be to determine the most similar sentence in the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "806f7c27-c396-4c24-adfc-707d3b6ed850",
   "metadata": {},
   "source": [
    "### 2.1 The Dataset\n",
    "\n",
    "For this part, we will use a crowdsourced dataset including the first sentence of different novels, available [here](https://github.com/janelleshane/novel-first-lines-dataset).\n",
    "\n",
    "Run the cell below to download the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4834f374-e437-4bb7-835f-f8acd4896ef8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Enter the first sentence of a novel.'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = \"https://raw.githubusercontent.com/janelleshane/novel-first-lines-dataset/master/crowdsourced_all.txt\"\n",
    "resp = requests.get(url)\n",
    "\n",
    "sentences = resp.text.split(\"\\n\")\n",
    "sentences[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "697d8a14-bdcf-4741-9322-6e690a48d646",
   "metadata": {},
   "source": [
    "### 2.2 Pre-processing (10 pts)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b054fcf-4f80-40eb-880b-609eaf64c26e",
   "metadata": {},
   "source": [
    "**Task (5 pts)**: To determine sentence similarity, we would like to pre-process the sentences. However, we still want to retain a copy of the original sentences around, for our final reply to the user. Create a copy of `sentences` in `match`, retaining the sort order of the sentences. Then, remove any duplicate sentences from both `sentences` and `match`, and stem the words in `match`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "425ed08b-c6a4-4f68-89f2-1d44b83f58cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "match = ...\n",
    "\n",
    "# Your de-duplication code here\n",
    "\n",
    "# Your stemming code here"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b6f8493-ce22-412d-9d89-0fb198002564",
   "metadata": {},
   "source": [
    "**Task (5 pts)**: Remove stopwords and punctuation from `match` . Provide some indicative metrics related to this pre-processing step (e.g. the most frequent X words before and after stopword removal, or the proportion of words that were stopwords)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0397eec6-e9b6-413e-b830-018ca473fe63",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Your punctuation and stopword removal code here"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72c63ebf-688d-47c2-87cf-9916ec94addc",
   "metadata": {},
   "source": [
    "### 2.3 Completing the sentences (15 pts)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cb21e3a-a847-4a4a-b5d6-02f72a663c2e",
   "metadata": {},
   "source": [
    "**Task (3 pts)**: Fill in the function below, to apply the same preproecssing to incoming sentences, as you applied to the sentences in `match`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c1fe37c4-b233-46d4-b9af-b906cb340b6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_sentence(s):\n",
    "    processed_s = s\n",
    "    \n",
    "    # Your code here\n",
    "    \n",
    "    return processed_s"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c51cf737-97b8-47f8-9ce2-ba396b11a7ae",
   "metadata": {},
   "source": [
    "**Task (12 pts)**: Fill in the function below, to retrieve the sentence most similar to `s`. Your implementation should allow toggling between cosine and Jaccard similarity in the bag-of-words model, as well as switching to TF/IDF or BERT, using the value of `option` (one of the strings in `options`). You are free to define additional variables outside `complete`, if needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1d8547a9-a1ed-4cf6-b26a-dd21b7a3362f",
   "metadata": {},
   "outputs": [],
   "source": [
    "options = [\"BAG_COSINE\", \"BAG_JACCARD\", \"TF/IDF\", \"BERT\"]\n",
    "\n",
    "# Your variables here\n",
    "\n",
    "def complete(s, option):\n",
    "    \n",
    "    # Your code here\n",
    "    \n",
    "    return sentence"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa728361-bb0a-432a-bff3-09d4c25ef0c9",
   "metadata": {},
   "source": [
    "### 2.4 Evaluating alternatives (10 pts)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "544719b9-bf0b-44aa-ba35-93f7c4ee3e13",
   "metadata": {},
   "source": [
    "We now provide a set of unfinished sentences. Run the cell below to import them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "46543d84-304e-4f47-85c6-1dbb2598147e",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"data/example_sentences.txt\") as f:\n",
    "    d = f.read().splitlines()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdd3905d-82b9-4f79-ac0c-8acdb7ea3616",
   "metadata": {},
   "source": [
    "**Task (10 pts)**: Evaluate your code on `sentence_starts`, using each of the 4 `options`. For some example sentences, print the unfinished fragment you were given and the sentence as completed by the system. Report and discuss your results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "493acabe-bf85-4c6a-a9ab-d7ce5cfca1e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Your code here"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "870ba179-c4ea-46c6-ba75-aa4433db880b",
   "metadata": {},
   "source": [
    "## Part 3: Question and Answer (45 pts)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffd6a45a-e05d-4b58-bb81-d2faf7e168f4",
   "metadata": {},
   "source": [
    "In this part of the lab, you will use text similarity in the context of a question-and-answer task. Given a dataset of question-answer pairs, and a novel question, your goal will be to determine the most similar question in the dataset, and provide the user with the corresponding answer."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46c9b51d-ef14-40a4-8952-fd500af0c515",
   "metadata": {},
   "source": [
    "### 3.1: The Dataset\n",
    "\n",
    "For this part, we will use the [WebQuestions](http://nlp.stanford.edu/software/sempre/) dataset, used for benchmarking QA engines, especially ones that work on structured knowledge bases.\n",
    "\n",
    "Run the cell below to download the 4 splits comprising the dataset and store them in variables of the form `data_[SPLIT_NAME]`. You can read more about the inteded purpose of each split [here](https://github.com/brmson/dataset-factoid-webquestions#splits)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "376d9581-2bfc-4f2f-bba9-1c1e4e001362",
   "metadata": {},
   "outputs": [],
   "source": [
    "files = [\"devtest\", \"val\", \"trainmodel\", \"test\"]\n",
    "\n",
    "for f in files:\n",
    "    url = f\"https://raw.githubusercontent.com/brmson/dataset-factoid-webquestions/master/main/{f}.json\"\n",
    "    resp = requests.get(url)\n",
    "    name = f\"data_{f}\"\n",
    "    globals()[name] = json.loads(resp.text)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32852c96-07cd-4c76-920a-641d13aa4004",
   "metadata": {},
   "source": [
    "Now, run the cell below to examine an example entry. Each entry contains a unique `qId` and a list of one or more `answers` to a question, labelled `qText`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e1f3c99e-0cf1-4227-a55a-33251fc4e384",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'qId': 'wqr000000',\n",
       " 'answers': ['Jazmyn Bieber', 'Jaxon Bieber'],\n",
       " 'qText': 'what is the name of justin bieber brother?'}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_devtest[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8587d894-4934-4f2f-a5eb-eb6341195826",
   "metadata": {},
   "source": [
    "### 3.2 Pre-processing (15 pts)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d89689c5-f007-4af3-9259-046476b8011e",
   "metadata": {},
   "source": [
    "**Task (5 pts)**: The current partitioning of the data makes sense in the context of training an NLP model, but is inconvenient for our approach. Re-partition the data so that all the questions are accumulated separately from all the answers. Then fill in the function `answers_to_existing()`, which should provide a mapping from an element of `questions` to the correct elements of `answers`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "53a438bd-a720-4522-8741-0b6fa4b53d11",
   "metadata": {},
   "outputs": [],
   "source": [
    "questions = ...\n",
    "answers = ...\n",
    "\n",
    "def answers_to_existing(#Your parameters here \n",
    "):\n",
    "    # Your code here\n",
    "    \n",
    "    return ans"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca4e8506-619a-4d30-a3b8-12c14baebee9",
   "metadata": {},
   "source": [
    "**Task (5 pts)**: Apply stemming to the questions. Provide some indicative metrics as to the impact of this pre-processing step (e.g. the number of distinct words before and after stemming). Should we also stem the answers? Why or why not?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "683dfe13-fb99-4f3a-88c3-557f9bf5e575",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Your stemming code & answers here"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afc9cbf1-3728-4107-bd24-cdc434c3b68b",
   "metadata": {},
   "source": [
    "**Task (5 pts)**: Remove stopwords from the questions. Again, provide some indicative metrics related to this step (e.g. the most frequent X words before and after stopword removal, or the proportion of words that were stopwords)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "9865e0fe-870a-4bd0-9650-b2d22d14794e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Your stopword removal code here"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f3a9784-b38d-4505-bdbc-cf3209838aba",
   "metadata": {},
   "source": [
    "### 3.3 Finding the most similar question (20 pts)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "314394d6-b129-4383-8a88-32d77f374f20",
   "metadata": {},
   "source": [
    "**Task (3 pts)**: Fill in the function below, to apply the same preproecssing to incoming questions, as you applied to existing ones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9e867745-f4f4-4d1b-9da4-8dbc3a0c5f9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_question(q):\n",
    "    processed_q = q\n",
    "    \n",
    "    # Your code here\n",
    "    \n",
    "    return processed_q"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "542ecc58-dd85-47c7-8f44-1021b778068c",
   "metadata": {},
   "source": [
    "**Task (17 pts)**: Fill in the function below, to retrieve the answers to the question most similar to `q`. Your implementation should allow toggling between cosine and Jaccard similarity in the bag-of-words model, as well as switching to TF/IDF or BERT, using the value of `option` (one of the strings in `options`). You are free to define additional variables outside `answers_to`, if needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "8dbd2e7c-00ff-4467-9859-0c20df82ffab",
   "metadata": {},
   "outputs": [],
   "source": [
    "options = [\"BAG_COSINE\", \"BAG_JACCARD\", \"TF/IDF\", \"BERT\"]\n",
    "\n",
    "# Your variables here\n",
    "\n",
    "def answers_to(q, option):\n",
    "    \n",
    "    # Your code here\n",
    "    \n",
    "    return ans"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd4cc1e4-dc71-415e-af9b-6420f1130940",
   "metadata": {},
   "source": [
    "### 3.4 Evaluating alternatives (10 pts)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "566a38ce-d345-4081-8b74-bb307fb77c69",
   "metadata": {},
   "source": [
    "We now provide a set of \"user\" questions. For each of the first 20 questions in the original `devtest` split, we provide 5 variants. Run the cells below to import the questions and inspect the variants for one of the questions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "48e9beaf-d40e-4a6c-bcab-4af0e1748d15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'what is the name of justin bieber brother?'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open(\"data/user_questions.json\") as f:\n",
    "    d = json.load(f)\n",
    "    \n",
    "data_devtest[0][\"qText\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b35d1e82-a568-4e41-892b-f5259e435888",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'qText1': 'who is the brother of justin bieber?',\n",
       " 'qText2': 'does justin bieber have a brother?',\n",
       " 'qText3': 'justin bieber siblings',\n",
       " 'qText4': 'what do they call the brother of justin bieber?',\n",
       " 'qText5': 'justin bieber names brothers'}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "209f8a8c-df03-4f32-9426-fbfa3eb63982",
   "metadata": {},
   "source": [
    "**Task (10 pts)**: Evaluate your code on `user_questions` and report the F1 micro and F1 macro scores, using each of the 4 `options`. Treat each set of 5 variants as a separate class. A prediction should be considered \"correct\" whenever the user questions are mapped to the appropriate question in `devtest`, leading to the correct answer. Report and discuss your results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "58991108-11ba-4121-9638-a31f64e9fb18",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Your code here"
   ]
  }
 ],
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   "file_extension": ".py",
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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