ToBigs_3wk_NaiveBayes_assignment.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "W3Zkr2JGup19"
   },
   "source": [
    "# Assignment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hJEhM1uAup2I"
   },
   "source": [
    "## 우리의 목표 : 나이브베이즈 문제 해결하기\n",
    "1.1) 입력문서가 {fast, furious, fun} 만을 주요 단어로 가질때, 이 문서는 얼마의 확률로 어떤 문서로 분류되는가?\n",
    "\n",
    "##### 노트북 파일을 따라오면서, 빈칸과 질문에 대한 물음을 모두 채우시면 과제 완료 입니다 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZSwttCJnup2K"
   },
   "source": [
    "### 주어진 dataset 을 확인해 보기위해 엑셀 파일을 읽습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "id": "PHRU587dup2K"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "id": "Bz0IgHzGup2T"
   },
   "outputs": [],
   "source": [
    "documents = pd.read_excel('3wk_NaiveBayes_test_file.xlsx') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "id": "yZt4AtNTup2W",
    "outputId": "f4b0e76e-c63f-45bc-d217-12684e5e77a3"
   },
   "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>label</th>\n",
       "      <th>message</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>comedy</td>\n",
       "      <td>Fun couple, love love</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>action</td>\n",
       "      <td>fast Furious, shoot!!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>comedy</td>\n",
       "      <td>couple^^, fly, fast, fun, fun</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>action</td>\n",
       "      <td>furious, shoot shoot fun</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>action</td>\n",
       "      <td>fly~~ fast shoot, Love</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    label                        message\n",
       "0  comedy          Fun couple, love love\n",
       "1  action          fast Furious, shoot!!\n",
       "2  comedy  couple^^, fly, fast, fun, fun\n",
       "3  action       furious, shoot shoot fun\n",
       "4  action         fly~~ fast shoot, Love"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "documents "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "prP57Inaup2Y"
   },
   "source": [
    "####  Q1. 발견한 **문제점**(전처리가 필요한 부분)을 2가지 이상 적어주세요.\n",
    "- 1. `특수문자들을 제거해야 합니다.(ex ^^, ~~, !!)`\n",
    "- 2. `구분이 잘 안 되어 있어서 띄어쓰기에 따라서 구분 지어 줘야 합니다.`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cXxX9Yx7up2Y"
   },
   "source": [
    "### step 1. 단어를 모두 소문자로 바꾸어 줍니다. \n",
    "#### Q2. 이 과정이 왜 필요할까요?\n",
    "- A. `영어에서 대소문자를 구별하지 않으면, 다른 데이터를 인식할 수도 있기 때문에, 이에 대한 통합시키는 과정이 필요합니다.`\n",
    "\n",
    "#### Q3. 코드속 빈칸을 채워주세요"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "id": "UCX42MsAup2Z",
    "outputId": "208f4928-3251-425f-bad5-721b3a873ae6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['fun couple, love love', 'fast furious, shoot!!', 'couple^^, fly, fast, fun, fun', 'furious, shoot shoot fun', 'fly~~ fast shoot, love']\n"
     ]
    }
   ],
   "source": [
    "lower_documents = []\n",
    "lower_documents = [d.lower() for d in documents['message']]\n",
    "print(lower_documents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QZDgo93Lup2a"
   },
   "source": [
    "### step2. 특수문자를 없애줍니다.\n",
    "#### string.punctuation 에는 특수문자들이 저장되어있는것을 확인할수 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "id": "Nldmu4gvup2a",
    "outputId": "46cd9c50-25af-483e-b9bc-68daeaa445ef"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'!\"#$%&\\'()*+,-./:;<=>?@[\\\\]^_`{|}~'"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import string\n",
    "string.punctuation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "B8uDOYzWup2b"
   },
   "source": [
    "### Q4. string.punctuation 을 이용해서 특수문자를 제거해주세요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "id": "oWaKMt1Mup2b",
    "outputId": "938b31f7-087e-4ea6-90d2-00f354d82c84"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['fun couple love love',\n",
       " 'fast furious shoot',\n",
       " 'couple fly fast fun fun',\n",
       " 'furious shoot shoot fun',\n",
       " 'fly fast shoot love']"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re\n",
    "\n",
    "del_punctuation_documents = []\n",
    "\n",
    "for i in lower_documents:\n",
    "    \n",
    "    new_str = re.sub(f\"[{string.punctuation}]\", '', i)\n",
    "    del_punctuation_documents.append(new_str)\n",
    "    \n",
    "del_punctuation_documents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kjSXpYvcup2e"
   },
   "source": [
    "### step 3. 단어를 하나씩 띄어쓰기 단위로 쪼개어 줍니다.\n",
    "#### Q5. 빈칸을 채워주세요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "id": "DheDzRAGup2f",
    "outputId": "2ba3549b-101d-4ac6-ae61-90a602fb1d8b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['fun', 'couple', 'love', 'love'],\n",
       " ['fast', 'furious', 'shoot'],\n",
       " ['couple', 'fly', 'fast', 'fun', 'fun'],\n",
       " ['furious', 'shoot', 'shoot', 'fun'],\n",
       " ['fly', 'fast', 'shoot', 'love']]"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessed_documents = [[w for w in d.split()] for d in del_punctuation_documents]\n",
    "preprocessed_documents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uUgfKy2Jup2g"
   },
   "source": [
    "### step 4. 각각의 단어가 문서에서 몇번 나왔는지 세봅시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "id": "1oeyBhDPup2h",
    "outputId": "c49073d4-0abb-4919-a8f3-c5bf4fffea1b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Counter({'love': 2, 'fun': 1, 'couple': 1}),\n",
       " Counter({'fast': 1, 'furious': 1, 'shoot': 1}),\n",
       " Counter({'fun': 2, 'couple': 1, 'fly': 1, 'fast': 1}),\n",
       " Counter({'shoot': 2, 'furious': 1, 'fun': 1}),\n",
       " Counter({'fly': 1, 'fast': 1, 'shoot': 1, 'love': 1})]"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frequency_list = []\n",
    "from collections import Counter\n",
    "\n",
    "frequency_list = [Counter(d) for d in preprocessed_documents]\n",
    "frequency_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "S49LF8aoup2i"
   },
   "source": [
    "### step 5. 이제 우리가 하고싶은것은, 문자를 숫자로 변환하는 작업입니다!(그림 참고)\n",
    "- 이를 위해 CountVectorizer를 사용합니다.\n",
    "- CountVectorizer는 문서 집합에서 단어 토큰을 생성하고, 각 단어 수를 세어 BOW (Bag of Words) 인코딩한 벡터를 만들어줍니다.\n",
    "\n",
    "- 문서에 해당단어가 몇번 포함되었는지를 나타낼 때 사용하는 방법입니다.\n",
    "\n",
    "- 참고 : https://datascienceschool.net/view-notebook/3e7aadbf88ed4f0d87a76f9ddc925d69/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZJSEL2Vwup2i"
   },
   "source": [
    "![CountVectorized](https://user-images.githubusercontent.com/68625698/106378540-15d8ed80-63e9-11eb-8604-5c960c274867.PNG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "id": "AfRgjbxhup2i",
    "outputId": "5a0d2491-d8f8-4f94-fe74-7771d5bcfb71"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-9 {color: black;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>CountVectorizer()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">CountVectorizer</label><div class=\"sk-toggleable__content\"><pre>CountVectorizer()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "CountVectorizer()"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "count_vector = CountVectorizer()\n",
    "count_vector.fit(documents['message'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "id": "EYcgD99Lup2j",
    "outputId": "70834bae-f77a-486f-f028-b09dd8391180"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0, 0, 1, 0, 2, 0],\n",
       "       [0, 1, 0, 0, 1, 0, 1],\n",
       "       [1, 1, 1, 2, 0, 0, 0],\n",
       "       [0, 0, 0, 1, 1, 0, 2],\n",
       "       [0, 1, 1, 0, 0, 1, 1]], dtype=int64)"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doc_array = count_vector.transform(documents['message']).toarray()\n",
    "doc_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "id": "mKpKp6q6up2k",
    "outputId": "72e87734-a918-400e-8a26-d3d2502a62cd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['couple', 'fast', 'fly', 'fun', 'furious', 'love', 'shoot'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_vector.get_feature_names_out()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {
    "id": "ZYyBzSVUup2k",
    "outputId": "3a299a33-09a0-4fb4-b289-20219b4f786e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'fun': 3,\n",
       " 'couple': 0,\n",
       " 'love': 5,\n",
       " 'fast': 1,\n",
       " 'furious': 4,\n",
       " 'shoot': 6,\n",
       " 'fly': 2}"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_vector.vocabulary_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "id": "NxPjKxQTup2l",
    "outputId": "5c6b8d7d-7eb5-4ab1-eaac-2bbad08edae0"
   },
   "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>couple</th>\n",
       "      <th>fast</th>\n",
       "      <th>fly</th>\n",
       "      <th>fun</th>\n",
       "      <th>furious</th>\n",
       "      <th>love</th>\n",
       "      <th>shoot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   couple  fast  fly  fun  furious  love  shoot\n",
       "0       1     0    0    1        0     2      0\n",
       "1       0     1    0    0        1     0      1\n",
       "2       1     1    1    2        0     0      0\n",
       "3       0     0    0    1        1     0      2\n",
       "4       0     1    1    0        0     1      1"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frequency_matrix = pd.DataFrame(doc_array, columns = count_vector.get_feature_names_out())\n",
    "frequency_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "id": "nFDEoN6iup2l"
   },
   "outputs": [],
   "source": [
    "frequency_matrix['count'] = frequency_matrix.sum(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ECND0-Xiup2l"
   },
   "source": [
    "### step 6. 범주형 변수를 dummy변수로 변환해주는 작업(One-Hot Encoding!)을 해주어야합니다.\n",
    "#### Q6. label 을 comedy =1, action =0 으로 변환해주세요"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "id": "A4gTPrNhup2m"
   },
   "outputs": [],
   "source": [
    "documents[\"label\"]=pd.get_dummies(documents[\"label\"]).comedy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {
    "id": "21OXioBWup2m"
   },
   "outputs": [],
   "source": [
    "doc = pd.concat([documents['label'],frequency_matrix],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {
    "id": "Q2835Qg-up2m",
    "outputId": "439892aa-9db7-45a5-8ea8-65ba905af2d8"
   },
   "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>label</th>\n",
       "      <th>couple</th>\n",
       "      <th>fast</th>\n",
       "      <th>fly</th>\n",
       "      <th>fun</th>\n",
       "      <th>furious</th>\n",
       "      <th>love</th>\n",
       "      <th>shoot</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   label  couple  fast  fly  fun  furious  love  shoot  count\n",
       "0   True       1     0    0    1        0     2      0      4\n",
       "1  False       0     1    0    0        1     0      1      3\n",
       "2   True       1     1    1    2        0     0      0      5\n",
       "3  False       0     0    0    1        1     0      2      4\n",
       "4  False       0     1    1    0        0     1      1      4"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "J3djcEgzup2n"
   },
   "source": [
    "###  step 7. 나이브 베이즈 계산을 해봅시다!\n",
    "\n",
    "#### Q7. 입력문서가 {fast, furious, fun} 을 주요 단어로 가질때, 이 문서는 얼마의 확률로 어떤 문서로 분류가 될까요? ( 계산과정을 채워주세요) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "id": "GdeYF_4rup2n",
    "outputId": "92082346-2dc3-4650-ca5b-608ff0c18286"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[True, 1, 0, 0, 1, 0, 2, 0, 4],\n",
       "       [False, 0, 1, 0, 0, 1, 0, 1, 3],\n",
       "       [True, 1, 1, 1, 2, 0, 0, 0, 5],\n",
       "       [False, 0, 0, 0, 1, 1, 0, 2, 4],\n",
       "       [False, 0, 1, 1, 0, 0, 1, 1, 4]], dtype=object)"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doc = np.array(doc)           # dataframe을 np-array로 변환해줍니다.\n",
    "                         # return값은 np-matrix가 아니라 np-array입니다.\n",
    "doc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "id": "JgtmVeawup2n",
    "outputId": "22b058bc-4610-4aff-e4a8-b96b414ceaf8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(doc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1qxqVZyXup2o"
   },
   "source": [
    "####  P(Y=comedy), P(Y=action) 계산하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "id": "frcppWOnup2o"
   },
   "outputs": [],
   "source": [
    "# P(Y=comedy)\n",
    "p_comedy = sum(doc[:,0]==1) / len(doc)\n",
    "\n",
    "# P(Y=action)\n",
    "p_action = 1 - p_comedy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "id": "Yeh96yycup2q"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p_comedy :  0.4\n",
      "p_action :  0.6\n"
     ]
    }
   ],
   "source": [
    "print('p_comedy : ',p_comedy)\n",
    "print('p_action : ', p_action)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "85_GjF4Yup2r"
   },
   "source": [
    "#### P(fast=1|comedy=1), P(furious=1|comedy=1), P(fun=1|comedy=1) 계산하기\n",
    "* 참고 : 문서에 두번 등장한 단어 주의"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*  조건부 확률 분모 구하기\n",
    "`doc`의 `count` 위치 : doc array의 8번 째 열 <br>\n",
    "-> sum(doc[doc[:, 0] == 1][:, 8]) : 장르가 comedy(1)에 속하는 전체 단어 개수\n",
    "\n",
    "*  조건부 확률 분자 구하기\n",
    "1. `doc`의 `fast` 위치 : doc array의 2번 째 열 <br>\n",
    "-> sum(doc[doc[:, 0] == 1][:, 2]) : 장르가 comedy(1)인 fast 수\n",
    "2. `doc`의 `furious` 위치 : doc array의 5 째 열 <br>\n",
    "-> sum(doc[doc[:, 0] == 1][:, 5]) : 장르가 comedy(1)인 fly 수 \n",
    "3. `doc`의 `fun` 위치 : doc array의 4번 째 열 <br>\n",
    "-> sum(doc[doc[:, 0] == 1][:, 4]) : 장르가 comedy(1)인 shoot 수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "id": "yHkk52I-up2y"
   },
   "outputs": [],
   "source": [
    "# P(fast=1|comedy=1)\n",
    "p_comedy_fast = sum( doc[doc[:, 0] == 1][:, 2]) / sum(doc[doc[:, 0] == 1][:, 8])\n",
    "\n",
    "# P(furious=1|comedy=1)\n",
    "p_comedy_furious = sum(doc[doc[:, 0] == 1][:, 5]) / sum(doc[doc[:, 0] == 1][:, 8]) \n",
    "\n",
    "# P(fun=1|comedy=1)\n",
    "p_comedy_fun = sum(doc[doc[:, 0] == 1][:, 4]) / sum(doc[doc[:, 0] == 1][:, 8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "id": "_1GyvhmTup21"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p_comedy_fast :  0.1111111111111111\n",
      "p_comedy_furious :  0.0\n",
      "p_comedy_fun :  0.3333333333333333\n"
     ]
    }
   ],
   "source": [
    "print('p_comedy_fast : ' , p_comedy_fast)\n",
    "print('p_comedy_furious : ' , p_comedy_furious)\n",
    "print('p_comedy_fun : ' , p_comedy_fun)   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qefcvWQ-up22"
   },
   "source": [
    "#### P(fast=1|action=1), P(furious=1|action=1), P(fun=1|action=1) 계산하기\n",
    "* 참고 : 문서에 두번 등장한 단어 주의"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*  조건부 확률 분모 구하기\n",
    "`doc`의 `count` 위치 : doc array의 8번 째 열 <br>\n",
    "-> sum(doc[doc[:, 0] == 0][:, 8]) : 장르가 action(0)에 속하는 전체 단어 개수\n",
    "\n",
    "*  조건부 확률 분자 구하기\n",
    "1. `doc`의 `fast` 위치 : doc array의 2번 째 열 <br>\n",
    "-> sum(doc[doc[:, 0] == 0][:, 2]) : 장르가 action(0)인 fast 수\n",
    "2. `doc`의 `furious` 위치 : doc array의 5번 째 열 <br>\n",
    "-> sum(doc[doc[:, 0] == 0][:, 5]) : 장르가 action(0)인 furious 수 \n",
    "3. `doc`의 `fun` 위치 : doc array의 4번 째 열 <br>\n",
    "-> sum(doc[doc[:, 0] == 0][:, 4]) : 장르가 action(0)인 fun 수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "id": "wCUO0jxcup23"
   },
   "outputs": [],
   "source": [
    "# P(fast=1|action=1)\n",
    "p_action_fast = sum(doc[doc[:, 0] == 0][:, 2]) / sum(doc[doc[:, 0] == 0][:, 8])\n",
    "\n",
    "# P(furious=1|action=1)\n",
    "p_action_furious = sum(doc[doc[:, 0] == 0][:, 5]) / sum(doc[doc[:, 0] == 0][:, 8])\n",
    "\n",
    "# P(fun=1|action=1)\n",
    "p_action_fun = sum(doc[doc[:, 0] == 0][:, 4]) / sum(doc[doc[:, 0] == 0][:, 8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "id": "qZlOkRG7up23"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p_action_fast :  0.18181818181818182\n",
      "p_action_furious :  0.18181818181818182\n",
      "p_action_fun :  0.09090909090909091\n"
     ]
    }
   ],
   "source": [
    "print('p_action_fast : ' , p_action_fast)\n",
    "print('p_action_furious : ' , p_action_furious)\n",
    "print('p_action_fun : ' , p_action_fun)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sts9BLUeup23"
   },
   "source": [
    "#### P(Y = comedy| X = fast, furious, fun) , P(Y = action=1| X = fast, furious, fun) 값 계산하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "id": "sVl-vZv4up23"
   },
   "outputs": [],
   "source": [
    "#P(Y = comedy| X = fast, furious, fun)\n",
    "proba_comedy = p_comedy_fast * p_comedy_furious * p_comedy_fun * (2/5)\n",
    "\n",
    "#P(Y = action=1| X = fast, furious, fun)\n",
    "proba_action = p_action_fast * p_action_furious * p_action_fun * (3/5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "id": "dU21-UF6up24"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "proba_comedy 0.0\n",
      "proba_action 0.0018031555221637867\n"
     ]
    }
   ],
   "source": [
    "print('proba_comedy', proba_comedy)\n",
    "print('proba_action', proba_action)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ypxC3cfrup24"
   },
   "source": [
    "### step 8. 다음 값을 비교해봅시다.\n",
    "1. P(Y = comedy| X = fast, furious, fun)\n",
    "2. P(Y = action=1| X = fast, furious, fun)\n",
    "\n",
    "\n",
    "Q7. 어떤 문제점을 발견할수 있나요? 문제점을 해결하기 위한 방법으로는 어떤게 있을까요?\n",
    "- A. likelihood가 0이 되는 문제점이 발생할 수 있다. (한 번도 일어나지 않은 경우가 있기 때문에) <br>\n",
    "- 따라서 이를 해결하기 위해서는 `라플라스 스무딩`을 통해 최소 한 번은 발생한 것으로 간주하여, 최소한의 확률을 구한다."
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "week3_NaiveBayes_과제.ipynb",
   "provenance": []
  },
  "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}