Day17- 計數方法詞向量實作_作業.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 載入所需的Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "from typing import List"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 載入資料"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "  Label                                            Content\n",
       "0   ham  Go until jurong point, crazy.. Available only ...\n",
       "1   ham                      Ok lar... Joking wif u oni...\n",
       "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
       "3   ham  U dun say so early hor... U c already then say...\n",
       "4   ham  Nah I don't think he goes to usf, he lives aro..."
      ],
      "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>Label</th>\n      <th>Content</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>ham</td>\n      <td>Go until jurong point, crazy.. Available only ...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>ham</td>\n      <td>Ok lar... Joking wif u oni...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>spam</td>\n      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>ham</td>\n      <td>U dun say so early hor... U c already then say...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>ham</td>\n      <td>Nah I don't think he goes to usf, he lives aro...</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 2
    }
   ],
   "source": [
    "# read data from spam.csv\n",
    "sms_data= pd.read_csv('spam.csv',usecols=[0,1], encoding='latin')\n",
    "sms_data.columns=['Label','Content']\n",
    "###<your code>###\n",
    "sms_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "       Content\n",
       "Label         \n",
       "ham       4825\n",
       "spam       747"
      ],
      "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>Content</th>\n    </tr>\n    <tr>\n      <th>Label</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>ham</th>\n      <td>4825</td>\n    </tr>\n    <tr>\n      <th>spam</th>\n      <td>747</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "source": [
    "# check how many spams and hams\n",
    "###<your code>###\n",
    "sms_data.groupby('Label').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "  Label                                            Content\n",
       "0     0  Go until jurong point, crazy.. Available only ...\n",
       "1     0                      Ok lar... Joking wif u oni...\n",
       "2     1  Free entry in 2 a wkly comp to win FA Cup fina...\n",
       "3     0  U dun say so early hor... U c already then say...\n",
       "4     0  Nah I don't think he goes to usf, he lives aro..."
      ],
      "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>Label</th>\n      <th>Content</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>Go until jurong point, crazy.. Available only ...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>Ok lar... Joking wif u oni...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>U dun say so early hor... U c already then say...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>Nah I don't think he goes to usf, he lives aro...</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "# change label to from string to number\n",
    "# \"ham\" --> 0, \"spam\" --> 1\n",
    "sms_data.loc[sms_data['Label']== 'ham', 'Label'] =0\n",
    "\n",
    "sms_data.loc[sms_data['Label']== 'spam', 'Label'] =1\n",
    "###<your code>###\n",
    "sms_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 切分資料\n",
    "將資料依據label比例切分為training data與testing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "       Content\nLabel         \n0         3859\n1          598\n       Content\nLabel         \n0          966\n1          149\n"
     ]
    }
   ],
   "source": [
    "###<your code>###\n",
    "sms_train, sms_test = train_test_split(sms_data, test_size=0.2, stratify=sms_data.Label)\n",
    "sms_train.reset_index(inplace=True, drop=True)\n",
    "sms_test.reset_index(inplace=True, drop=True)\n",
    "\n",
    "print(sms_train.groupby(\"Label\").count())\n",
    "print(sms_test.groupby(\"Label\").count())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 資料預處理\n",
    "* 將所有字詞轉為小寫\n",
    "* 移除所有數字、標點符號"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Train:\n  Label                                      Content\n0     0  no one interested may be some business plan\n1     0           yeah why not is the gang all ready\n2     0                    u still going to the mall\n3     0                      prepare to be pleasured\n4     0                            in cbe in chennai\nTest:\n  Label                                            Content\n0     0  dude just saw a parked car with its sunroof po...\n1     0  cool i am lt gt inches long hope you like them...\n2     0  i m going lunch now wif my family then aft dat...\n3     0             babe i lost you will you try rebooting\n4     0                           that is wondar full flim\n"
     ]
    }
   ],
   "source": [
    "def preprocess(df: pd.DataFrame) -> pd.DataFrame:\n",
    "    \n",
    "    for i in range(len(df)):\n",
    "        # make all content to lowercase\n",
    "        ###<your code>###\n",
    "        content= df.loc[i, 'Content'].lower()\n",
    "        # remove all punctuations\n",
    "        ###<your code>###\n",
    "        content= \" \".join([word for word in re.findall(r\"[a-zA-Z]*\", content) if word != \"\"])\n",
    "        df.loc[i, 'Content']= content\n",
    "\n",
    "    return df\n",
    "\n",
    "processed_train = preprocess(sms_train)\n",
    "processed_test = preprocess(sms_test)\n",
    "\n",
    "print(\"Train:\")\n",
    "print(processed_train.head())\n",
    "print(\"Test:\")\n",
    "print(processed_test.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "7708"
      ]
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "source": [
    "# split data into x_train, y_train, x_test, y_test\n",
    "y_train, x_train = zip(*processed_train.values)\n",
    "y_test, x_test = zip(*processed_test.values)\n",
    "\n",
    "# check numbers of unique word in the corpus\n",
    "len(set(\" \".join(list(x_train + x_test)).split()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TF-IDF\n",
    "可以發現種共有7708個字詞，這裡使用TF-IDF將來選取最高分的前2000個字詞\n",
    "(若忘記的學員可參考先前TF-IDF課程章節或[此篇教學](https://ithelp.ithome.com.tw/articles/10228815?sc=iThelpR))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Number of unique word: 2000\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(<4457x2000 sparse matrix of type '<class 'numpy.float64'>'\n",
       " \twith 51087 stored elements in Compressed Sparse Row format>,\n",
       " <1115x2000 sparse matrix of type '<class 'numpy.float64'>'\n",
       " \twith 12388 stored elements in Compressed Sparse Row format>)"
      ]
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "source": [
    "###<your code>###\n",
    "vectorizer= TfidfVectorizer(max_features=2000)\n",
    "vectorizer.fit(x_train)\n",
    "tfidf_train=vectorizer.transform(x_train)\n",
    "tfidf_test=vectorizer.transform(x_test)\n",
    "\n",
    "print(f\"Number of unique word: {len(vectorizer.vocabulary_)}\")\n",
    "(tfidf_train, tfidf_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 建立共現矩陣"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "finishing 501/4457\n",
      "finishing 1001/4457\n",
      "finishing 1501/4457\n",
      "finishing 2001/4457\n",
      "finishing 2501/4457\n",
      "finishing 3001/4457\n",
      "finishing 3501/4457\n",
      "finishing 4001/4457\n",
      "Done\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[1., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 1., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 1., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 1., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 1., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 1.]])"
      ]
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "source": [
    "def create_co_matrix(corpus: List[str], vocab_list: List[str], word2idx: dict,\n",
    "                     window_size: int=1, use_weighting: bool=False, verbose: bool=False) -> np.ndarray:\n",
    "    '''Function to create co-occurrence matrix\n",
    "    '''\n",
    "    #initialize co-occurrence matrix\n",
    "    ###<your code>###\n",
    "    vocab_size=len(vocab_list)\n",
    "    co_matrix= np.zeros(shape=(vocab_size,vocab_size))\n",
    "\n",
    "    for idx, sms in enumerate(corpus):\n",
    "        ###<your code>###\n",
    "        sms= sms.split()\n",
    "        sms_ids=[word2idx[word] for word in sms if word in vocab_list]\n",
    "\n",
    "        for center_i, center_word_id in enumerate(sms_ids):\n",
    "            ###<your code>###\n",
    "            context_ids= sms_ids[max(0, center_i- window_size):center_word_id]\n",
    "            context_len = len(context_ids)\n",
    "\n",
    "            for left_i, left_word_id in enumerate(context_ids):\n",
    "\n",
    "                ###<your code>###\n",
    "                if use_weighting == 1:\n",
    "                    distance = context_len - left_i\n",
    "                    increment= 1./float(distance)\n",
    "                else:\n",
    "                    increment= 1\n",
    "\n",
    "                co_matrix[center_word_id, left_word_id] += increment\n",
    "                co_matrix[left_word_id, center_word_id] += increment\n",
    "\n",
    "        if verbose:\n",
    "            if idx != 0 and idx%500 == 0:\n",
    "                    print(f\"finishing {idx+1}/{len(corpus)}\")\n",
    "    print(\"Done\")\n",
    "    if use_weighting:\n",
    "        # if use weighting, then we set the co-occurrence with the word itself to 1.0\n",
    "        ###<your code>###\n",
    "        np.fill_diagonal(co_matrix, 1.)\n",
    "    return co_matrix\n",
    "\n",
    "co_matrix = create_co_matrix(x_train, vectorizer.get_feature_names(), vectorizer.vocabulary_,\n",
    "                            window_size=3, use_weighting=True, verbose=True)\n",
    "\n",
    "co_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 建立PPMI矩陣"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[12.67095  ,  0.       ,  0.       , ...,  0.       ,  0.       ,\n",
       "         0.       ],\n",
       "       [ 0.       ,  2.7873013,  0.       , ...,  0.       ,  0.       ,\n",
       "         0.       ],\n",
       "       [ 0.       ,  0.       ,  7.75043  , ...,  0.       ,  0.       ,\n",
       "         0.       ],\n",
       "       ...,\n",
       "       [ 0.       ,  0.       ,  0.       , ...,  9.841574 ,  0.       ,\n",
       "         0.       ],\n",
       "       [ 0.       ,  0.       ,  0.       , ...,  0.       ,  4.6689057,\n",
       "         0.       ],\n",
       "       [ 0.       ,  0.       ,  0.       , ...,  0.       ,  0.       ,\n",
       "         6.7269936]], dtype=float32)"
      ]
     },
     "metadata": {},
     "execution_count": 12
    }
   ],
   "source": [
    "#定義正向點間互資訊\n",
    "\n",
    "def ppmi(co_matrix: np.ndarray, eps: float=1e-8, verbose: bool=False):\n",
    "    ###<your code>###\n",
    "    M=np.zeros_like(co_matrix, dtype= np.float32)\n",
    "    N=np.sum(co_matrix)\n",
    "    s=np.sum(co_matrix,axis=0)\n",
    "    total = co_matrix.shape[0] * co_matrix.shape[1]\n",
    "\n",
    "    for i in range(co_matrix.shape[0]):\n",
    "        for j in range(co_matrix.shape[1]):\n",
    "            ###<your code>###\n",
    "            pmi= np.log2(co_matrix[i,j]*N / (s[i]*s[j]+eps))\n",
    "            M[i,j]=max(0,pmi)\n",
    "\n",
    "            if verbose:\n",
    "                cnt += 1\n",
    "                if cnt % 10 == 0 or cnt == total:\n",
    "                    print(f\"{cnt}/{total} Done\")\n",
    "    \n",
    "    return M\n",
    "\n",
    "ppmi_matrix = ppmi(co_matrix, verbose=False)\n",
    "ppmi_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 使用SVD降維\n",
    "利用sklearn中的TruncatedSVD對co-occurrence matrix進行降維，並利用variance來找出最適合的維度\n",
    "[參考文獻](https://medium.com/swlh/truncated-singular-value-decomposition-svd-using-amazon-food-reviews-891d97af5d8d)\n",
    "\n",
    "(讀者可以嘗試使用SVD對PPMI進行降維)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Number of components = 10 and explained variance = 0.8634616199540918\n",
      "Number of components = 20 and explained variance = 0.8977025945638728\n",
      "Number of components = 30 and explained variance = 0.9159969012987645\n",
      "Number of components = 40 and explained variance = 0.9280340387041585\n",
      "Number of components = 50 and explained variance = 0.9370712509815103\n",
      "Number of components = 60 and explained variance = 0.9440621622776902\n",
      "Number of components = 70 and explained variance = 0.949812773823569\n",
      "Number of components = 80 and explained variance = 0.9546699247898331\n",
      "Number of components = 90 and explained variance = 0.9587799291878358\n",
      "Number of components = 100 and explained variance = 0.9623196155443983\n",
      "Number of components = 110 and explained variance = 0.9654229995810161\n",
      "Number of components = 120 and explained variance = 0.968189984311529\n",
      "Number of components = 130 and explained variance = 0.9705848555880928\n",
      "Number of components = 140 and explained variance = 0.9727152917705074\n"
     ]
    },
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Program to find the optimal number of components for Truncated SVD\n",
    "n_comp = range(10,150,10) # list containing different values of components\n",
    "variance_sum = [] # explained variance ratio for each component of Truncated SVD\n",
    "\n",
    "for dim in n_comp:\n",
    "    ###<your code>###\n",
    "    svd = TruncatedSVD(n_components=dim)\n",
    "    svd.fit(co_matrix)\n",
    "    variance_sum.append(svd.explained_variance_ratio_.sum())\n",
    "    print(f\"Number of components = {dim} and explained variance = {svd.explained_variance_ratio_.sum()}\")\n",
    "\n",
    "plt.plot(n_comp, variance_sum)\n",
    "plt.xlabel('Number of components')\n",
    "plt.ylabel(\"Explained Variance\")\n",
    "plt.title(\"Plot of Number of components v/s explained variance\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# choose 140 as final dimension to reduce to \n",
    "# 利用上述找到的最適合dimension來對co-occurrence matrix進行降維\n",
    "###<your code>###\n",
    "svd=TruncatedSVD(n_components=140)\n",
    "svd.fit(co_matrix)\n",
    "re_co_matrix = svd.transform(co_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 使用KNN模型進行分類\n",
    "在進行分類之前，先利用簡單的詞向量平均來計算文本向量\n",
    "\n",
    "[參考文獻](https://medium.com/ai-academy-taiwan/nlp-%E4%B8%8D%E5%90%8C%E8%A9%9E%E5%90%91%E9%87%8F%E5%9C%A8%E6%96%87%E6%9C%AC%E5%88%86%E9%A1%9E%E4%B8%8A%E7%9A%84%E8%A1%A8%E7%8F%BE%E8%88%87%E5%AF%A6%E4%BD%9C-e72a2daecfc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "document 423 doesn't contain word in vocab_list\n",
      "ultimately tor motive tui achieve korli\n",
      "\n",
      "\n",
      "document 485 doesn't contain word in vocab_list\n",
      "\n",
      "\n",
      "\n",
      "document 584 doesn't contain word in vocab_list\n",
      "raviyog peripherals bhayandar east\n",
      "\n",
      "\n",
      "document 708 doesn't contain word in vocab_list\n",
      "\n",
      "\n",
      "\n",
      "document 930 doesn't contain word in vocab_list\n",
      "\n",
      "\n",
      "\n",
      "document 996 doesn't contain word in vocab_list\n",
      "garbage bags eggs jam bread hannaford wheat chex\n",
      "\n",
      "\n",
      "document 1090 doesn't contain word in vocab_list\n",
      "east coast\n",
      "\n",
      "\n",
      "document 2282 doesn't contain word in vocab_list\n",
      "audrie lousy autocorrect\n",
      "\n",
      "\n",
      "document 2355 doesn't contain word in vocab_list\n",
      "erutupalam thandiyachu\n",
      "\n",
      "\n",
      "document 2447 doesn't contain word in vocab_list\n",
      "beerage\n",
      "\n",
      "\n",
      "document 2617 doesn't contain word in vocab_list\n",
      "k k congratulation\n",
      "\n",
      "\n",
      "document 2992 doesn't contain word in vocab_list\n",
      "u\n",
      "\n",
      "\n",
      "document 3486 doesn't contain word in vocab_list\n",
      "gibbs unsold mike hussey\n",
      "\n",
      "\n",
      "document 3840 doesn't contain word in vocab_list\n",
      "hanks lotsly\n",
      "\n",
      "\n",
      "document 4421 doesn't contain word in vocab_list\n",
      "tf p\n",
      "\n",
      "\n",
      "==================================================\n",
      "document 665 doesn't contain word in vocab_list\n",
      "g w r\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# get doc vector via take mean of all word vectors inside the corresponding document\n",
    "\n",
    "def make_doc_vectors(corpus: List[str], word2idx: dict, vocab_list: List) -> List[np.ndarray]:\n",
    "    \n",
    "    # vectorizing data \n",
    "    # and make document vector by take mean to all word vecto\n",
    "    doc_vec = []\n",
    "    empty_doc_list = []\n",
    "    for i, sms_msg in enumerate(corpus):\n",
    "        sms_msg = [word2idx[word] for word in sms_msg.split() if word in vocab_list] #tokenize\n",
    "        if len(sms_msg) > 0:\n",
    "            sms_msg = np.array([re_co_matrix[ids] for ids in sms_msg]) #vectorize\n",
    "            doc_vec.append(sms_msg.mean(axis=0))\n",
    "        else:\n",
    "            empty_doc_list.append(i)\n",
    "            print(f\"document {i} doesn't contain word in vocab_list\")\n",
    "            print(corpus[i])\n",
    "            print(\"\\n\")\n",
    "        \n",
    "    return np.vstack(doc_vec), empty_doc_list\n",
    "\n",
    "word2idx = vectorizer.vocabulary_\n",
    "vocab_list = vectorizer.get_feature_names()\n",
    "\n",
    "doc_vec_train, missing_train_list = make_doc_vectors(x_train, word2idx, vocab_list)\n",
    "print(\"=\"*50)\n",
    "doc_vec_test, missing_test_list = make_doc_vectors(x_test, word2idx, vocab_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "# training\n",
    "y_train_filter = np.delete(np.array(y_train), missing_train_list)\n",
    "\n",
    "###<your code>###\n",
    "knn= KNeighborsClassifier(n_neighbors=5)\n",
    "knn.fit(doc_vec_train, y_train_filter)\n",
    "train_pred= knn.predict(doc_vec_train)\n",
    "\n",
    "# testing\n",
    "y_test_filter = np.delete(np.array(y_test), missing_test_list)\n",
    "###<your code>###\n",
    "test_pred= knn.predict(doc_vec_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "train acc: 0.9716343989194057\ntest acc: 0.9631956912028725\n"
     ]
    }
   ],
   "source": [
    "print(f\"train acc: {np.sum(train_pred == y_train_filter) / len(y_train_filter)}\")\n",
    "print(f\"test acc: {np.sum(test_pred == y_test_filter) / len(y_test_filter)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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