{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "942ac4eb",
   "metadata": {},
   "source": [
    "# Module 4 — Lab 2: Bag of Words and TF-IDF\n",
    "\n",
    "\n",
    "**Topics:** CountVectorizer, TfidfVectorizer, sparse matrices, n-grams, leakage\n",
    "\n",
    "> **Solution version:** This notebook contains the complete solution. Run the cells in order and compare the results with your Lab notebook.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "554dc6ef",
   "metadata": {},
   "source": [
    "## Learning objectives\n",
    "\n",
    "After completing this lab, you will be able to:\n",
    "\n",
    "- explain how Bag of Words maps documents to numeric vectors;\n",
    "- inspect a document-term matrix and its vocabulary;\n",
    "- fit TF-IDF on training text and transform unseen text;\n",
    "- describe why text matrices are high-dimensional and sparse;\n",
    "- compare unigram features with unigram-plus-bigram features.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dee543fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "DATA_PATH = Path(\"data/SMSSpamCollection\")\n",
    "RANDOM_STATE = 42\n",
    "\n",
    "if not DATA_PATH.exists():\n",
    "    raise FileNotFoundError(\n",
    "        \"Missing data/SMSSpamCollection. Follow data/README.md to download the official dataset.\"\n",
    "    )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "637b0eaa",
   "metadata": {},
   "source": [
    "## 1. Make Bag of Words visible\n",
    "\n",
    "Use three short messages so that every row, token, and count can be inspected directly.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c3d5152",
   "metadata": {},
   "outputs": [],
   "source": [
    "tiny_messages = [\n",
    "    \"free prize now\",\n",
    "    \"free meeting today\",\n",
    "    \"project meeting today\",\n",
    "]\n",
    "\n",
    "count_vectorizer = CountVectorizer()\n",
    "tiny_bow = count_vectorizer.fit_transform(tiny_messages)\n",
    "tiny_bow_df = pd.DataFrame(\n",
    "    tiny_bow.toarray(),\n",
    "    columns=count_vectorizer.get_feature_names_out(),\n",
    "    index=[\"message_1\", \"message_2\", \"message_3\"],\n",
    ")\n",
    "tiny_bow_df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3e98734",
   "metadata": {},
   "source": [
    "## 2. Prepare the UCI messages\n",
    "\n",
    "Remove exact duplicates before splitting. Do not fit a vocabulary on the complete dataset because the held-out test messages must remain unseen.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9841f9b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(DATA_PATH, sep=\"\\t\", header=None, names=[\"label\", \"message\"])\n",
    "df[\"label\"] = df[\"label\"].astype(\"string\").str.strip().str.lower()\n",
    "df[\"message\"] = df[\"message\"].astype(\"string\").str.strip()\n",
    "df = df.dropna(subset=[\"label\", \"message\"])\n",
    "df = df[df[\"label\"].isin([\"ham\", \"spam\"]) & df[\"message\"].ne(\"\")]\n",
    "\n",
    "conflicts = df.groupby(\"message\")[\"label\"].nunique()\n",
    "conflict_texts = conflicts[conflicts.gt(1)].index\n",
    "df = df[~df[\"message\"].isin(conflict_texts)]\n",
    "df = df.drop_duplicates(subset=[\"label\", \"message\"]).reset_index(drop=True)\n",
    "\n",
    "X = df[\"message\"]\n",
    "y = df[\"label\"].map({\"ham\": 0, \"spam\": 1}).astype(int)\n",
    "print(\"Clean messages:\", len(df))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92848cea",
   "metadata": {},
   "source": [
    "## 3. Fit TF-IDF without leakage\n",
    "\n",
    "`fit_transform` learns a vocabulary and inverse-document-frequency weights from training messages. The test set receives only `transform`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5a89189",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X,\n",
    "    y,\n",
    "    test_size=0.20,\n",
    "    random_state=RANDOM_STATE,\n",
    "    stratify=y,\n",
    ")\n",
    "\n",
    "tfidf_vectorizer = TfidfVectorizer(lowercase=True)\n",
    "X_train_tfidf = tfidf_vectorizer.fit_transform(X_train)\n",
    "X_test_tfidf = tfidf_vectorizer.transform(X_test)\n",
    "\n",
    "density = X_train_tfidf.nnz / (X_train_tfidf.shape[0] * X_train_tfidf.shape[1])\n",
    "print(\"Training matrix shape:\", X_train_tfidf.shape)\n",
    "print(\"Test matrix shape:\", X_test_tfidf.shape)\n",
    "print(\"Training density:\", f\"{density:.4%}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4230176",
   "metadata": {},
   "source": [
    "## 4. Inspect the learned feature space\n",
    "\n",
    "A high mean TF-IDF value does not automatically mean a term is a spam indicator. This step only describes the training corpus. A classifier will learn which terms distinguish the classes.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "90e679d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_tfidf = np.asarray(X_train_tfidf.mean(axis=0)).ravel()\n",
    "top_terms = pd.Series(\n",
    "    mean_tfidf,\n",
    "    index=tfidf_vectorizer.get_feature_names_out(),\n",
    "    name=\"mean_tfidf\",\n",
    ").sort_values(ascending=False).head(15)\n",
    "top_terms\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ebe7eee",
   "metadata": {},
   "source": [
    "## 5. Add short phrases with bigrams\n",
    "\n",
    "A unigram represents one token. A bigram can preserve short phrases such as `call now` or `free entry`, but it also increases the number of features.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c1de19b",
   "metadata": {},
   "outputs": [],
   "source": [
    "ngram_vectorizer = TfidfVectorizer(ngram_range=(1, 2), min_df=1)\n",
    "X_train_ngram = ngram_vectorizer.fit_transform(X_train)\n",
    "ngram_features = ngram_vectorizer.get_feature_names_out()\n",
    "example_bigrams = [term for term in ngram_features if \" \" in term][:10]\n",
    "\n",
    "print(\"Unigram features:\", X_train_tfidf.shape[1])\n",
    "print(\"Unigram + bigram features:\", X_train_ngram.shape[1])\n",
    "print(\"Example bigrams:\", example_bigrams)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64432265",
   "metadata": {},
   "source": [
    "## Self-check\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36f18994",
   "metadata": {},
   "outputs": [],
   "source": [
    "assert tiny_bow.shape == (3, len(count_vectorizer.get_feature_names_out()))\n",
    "assert tiny_bow_df.loc[\"message_1\", \"free\"] == 1\n",
    "assert len(X_train) + len(X_test) == len(df)\n",
    "assert X_train_tfidf.shape[0] == len(X_train)\n",
    "assert X_test_tfidf.shape[0] == len(X_test)\n",
    "assert X_train_tfidf.shape[1] == X_test_tfidf.shape[1]\n",
    "assert X_train_tfidf.nnz < X_train_tfidf.shape[0] * X_train_tfidf.shape[1]\n",
    "assert X_train_ngram.shape[1] >= X_train_tfidf.shape[1]\n",
    "assert len(top_terms) == 15\n",
    "print(\"✓ Lab 2 completed\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "991fa410",
   "metadata": {},
   "source": [
    "## Suggested answers\n",
    "\n",
    "1. **Why is a text feature matrix usually sparse?**  \n",
    "   Each message uses only a small fraction of the complete vocabulary, so most document-term cells are zero.\n",
    "2. **Why must the vectorizer be fitted only on training messages?**  \n",
    "   Fitting on all messages reveals test-set vocabulary and frequency information during training, which is data leakage and can make evaluation optimistic.\n",
    "3. **What trade-off is introduced by adding bigrams?**  \n",
    "   Bigrams preserve useful short phrases but expand the feature space, consume more memory, and can overfit when phrases are rare.\n"
   ]
  }
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