NewsClassification-Chinese.ipynb

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    "colab": {
      "name": "News_sperate.ipynb",
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      "collapsed_sections": [],
      "mount_file_id": "1Ymnq0-tp5nZv5gT7wkmoRjdNghb7MH2B",
      "authorship_tag": "ABX9TyP2nzfjOpzpZ0aLztNLfeFT",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/ahoucbvtw/NewsClassification-Chinese/blob/main/NewsClassification-Chinese.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Lq3LEjqn-57"
      },
      "source": [
        "import zipfile\n",
        "path = \"drive/My Drive/News/chinese_news_trans.zip\"\n",
        "f = zipfile.ZipFile(path)\n",
        "f.extractall(\"train\")\n",
        "path = \"drive/My Drive/News/chinese_news_test.zip\"\n",
        "f = zipfile.ZipFile(path)\n",
        "f.extractall(\"test\")"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sYZCsTzAp5YY"
      },
      "source": [
        "# \"train/chinese_news_trans/*\"\n",
        "import os\n",
        "import pandas as pd\n",
        "import glob\n",
        "contents = []\n",
        "types = []\n",
        "def showdata(path):\n",
        "    for dirName in glob.glob(os.path.join(path, \"*\")):\n",
        "        t = os.path.split(dirName)[-1]\n",
        "        for fn in glob.glob(os.path.join(dirName, \"*.txt\")) + glob.glob(os.path.join(dirName, \"*.TXT\")):\n",
        "            with open(fn) as f:\n",
        "                contents.append(f.read())\n",
        "                types.append(t)\n",
        "    data = pd.DataFrame({\"content\": contents, \"answer\": types})\n",
        "    return data"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6AT4UId_tMk7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "83e74ca5-eade-4218-d423-910511d0b355"
      },
      "source": [
        "train_data = showdata(\"train/chinese_news_trans\")\n",
        "test_data = showdata(\"test/chinese_news_test\")\n",
        "\n",
        "train_data\n",
        "# test_data\n",
        "# print(test_df)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>交通</td>\n",
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              "      <td>交通</td>\n",
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            "text/plain": [
              "                                                content answer\n",
              "0     日月光華 -- SE精華區文章閱讀\\n---------------------------...    計算機\n",
              "1     日月光華 -- Software精華區文章閱讀\\n---------------------...    計算機\n",
              "2     日月光華 -- Hardware精華區文章閱讀\\n---------------------...    計算機\n",
              "3     日月光華 -- Hardware精華區文章閱讀\\n---------------------...    計算機\n",
              "4     日月光華 -- SE精華區文章閱讀\\n---------------------------...    計算機\n",
              "...                                                 ...    ...\n",
              "2632             膠濟鐵路複線二期工程正加緊施工\\n新華社濟南５月１４日電（通訊員王建國...     交通\n",
              "2633  【 日  期 】19960612\\n【 版  號 】9\\n【 標  題 】認真實施分道航行規...     交通\n",
              "2634  \\n    ★★  新華社電訊稿數據庫  檢索命中 44375 篇, 顯示第 3104 篇。...     交通\n",
              "2635             濟南鐵路局積極做好防洪準備工作\\n新華社濟南５月２６日電（通訊員孔鑑、...     交通\n",
              "2636             鳳台淮河公路大橋通車\\n新華社合肥５月２日電（記者陳寶善）淮河上又一座...     交通\n",
              "\n",
              "[2637 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IEFifFEmuogp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "033a95c8-260a-4c0b-8b0a-87c06415597b"
      },
      "source": [
        "# 將dataset中的答案統一區分並列出\n",
        "newsType = train_data[\"answer\"].unique()\n",
        "newsType_dict = { n:i for i,n in enumerate(newsType)}\n",
        "newsType_r_dict = { i:n for i,n in enumerate(newsType_dict)}\n",
        "newsType_dict"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'交通': 9,\n",
              " '政治': 8,\n",
              " '教育': 7,\n",
              " '環境': 1,\n",
              " '經濟': 6,\n",
              " '藝術': 3,\n",
              " '計算機': 0,\n",
              " '軍事': 4,\n",
              " '醫藥': 2,\n",
              " '體育': 5}"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 4
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    {
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      "metadata": {
        "id": "aQgXPP0LvpRM",
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          "height": 419
        },
        "outputId": "9987bb36-b3a1-40a0-e945-fcb939c7498f"
      },
      "source": [
        "#將answer轉換成0123...\n",
        "train_data[\"answer\"] = train_data[\"answer\"].replace(newsType_dict)\n",
        "test_data[\"answer\"] = test_data[\"answer\"].replace(newsType_dict)\n",
        "\n",
        "train_data"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2634</th>\n",
              "      <td>\\n    ★★  新華社電訊稿數據庫  檢索命中 44375 篇, 顯示第 3104 篇。...</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2635</th>\n",
              "      <td>濟南鐵路局積極做好防洪準備工作\\n新華社濟南５月２６日電（通訊員孔鑑、...</td>\n",
              "      <td>9</td>\n",
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              "      <td>9</td>\n",
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            ],
            "text/plain": [
              "                                                content  answer\n",
              "0     日月光華 -- SE精華區文章閱讀\\n---------------------------...       0\n",
              "1     日月光華 -- Software精華區文章閱讀\\n---------------------...       0\n",
              "2     日月光華 -- Hardware精華區文章閱讀\\n---------------------...       0\n",
              "3     日月光華 -- Hardware精華區文章閱讀\\n---------------------...       0\n",
              "4     日月光華 -- SE精華區文章閱讀\\n---------------------------...       0\n",
              "...                                                 ...     ...\n",
              "2632             膠濟鐵路複線二期工程正加緊施工\\n新華社濟南５月１４日電（通訊員王建國...       9\n",
              "2633  【 日  期 】19960612\\n【 版  號 】9\\n【 標  題 】認真實施分道航行規...       9\n",
              "2634  \\n    ★★  新華社電訊稿數據庫  檢索命中 44375 篇, 顯示第 3104 篇。...       9\n",
              "2635             濟南鐵路局積極做好防洪準備工作\\n新華社濟南５月２６日電（通訊員孔鑑、...       9\n",
              "2636             鳳台淮河公路大橋通車\\n新華社合肥５月２日電（記者陳寶善）淮河上又一座...       9\n",
              "\n",
              "[2637 rows x 2 columns]"
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          "metadata": {
            "tags": []
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          "execution_count": 5
        }
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    {
      "cell_type": "code",
      "metadata": {
        "id": "eGYaWEa6wNta",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "outputId": "e654ccf5-d696-45e5-f365-a069864db357"
      },
      "source": [
        "# 新創一Function將新聞內部的換行符號刪除並與之前資料表結合\n",
        "import jieba\n",
        "from urllib.request import urlretrieve\n",
        "url = \"https://github.com/fxsjy/jieba/raw/master/extra_dict/dict.txt.big\"\n",
        "urlretrieve(url, \"dict.txt.big\")\n",
        "jieba.set_dictionary(\"dict.txt.big\")\n",
        "def newscut(news):\n",
        "    news = news.replace(\"\\r\", \"\").replace(\"\\n\", \"\").replace(\"\\n\\n\",\"\")\n",
        "    return \" \".join(jieba.cut(news))\n",
        "\n",
        "train_data[\"content\"] = train_data[\"content\"].apply(newscut)\n",
        "test_data[\"content\"] = test_data[\"content\"].apply(newscut)\n",
        "train_data"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Building prefix dict from /content/dict.txt.big ...\n",
            "Dumping model to file cache /tmp/jieba.u501edca284da514cb68b53a20324f4e3.cache\n",
            "Loading model cost 1.739 seconds.\n",
            "Prefix dict has been built successfully.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>膠濟 鐵路 複線 二期工程 正 加緊 施工 新華...</td>\n",
              "      <td>9</td>\n",
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              "      <td>【   日     期   】 19960612 【   版     號   】 9 【  ...</td>\n",
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              "      <th>2634</th>\n",
              "      <td>★ ★     新華社 電訊稿 數據庫     檢索 命中   44375 ...</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2635</th>\n",
              "      <td>濟南鐵路局 積極 做好 防洪 準備 工作 新華社...</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2636</th>\n",
              "      <td>鳳台 淮河 公路 大橋 通車 新華社 合肥 ５ ...</td>\n",
              "      <td>9</td>\n",
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              "<p>2637 rows × 2 columns</p>\n",
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            "text/plain": [
              "                                                content  answer\n",
              "0     日月 光華   --   SE 精華區 文章 閱讀 --------------------...       0\n",
              "1     日月 光華   --   Software 精華區 文章 閱讀 --------------...       0\n",
              "2     日月 光華   --   Hardware 精華區 文章 閱讀 --------------...       0\n",
              "3     日月 光華   --   Hardware 精華區 文章 閱讀 --------------...       0\n",
              "4     日月 光華   --   SE 精華區 文章 閱讀 --------------------...       0\n",
              "...                                                 ...     ...\n",
              "2632                        膠濟 鐵路 複線 二期工程 正 加緊 施工 新華...       9\n",
              "2633  【   日     期   】 19960612 【   版     號   】 9 【  ...       9\n",
              "2634          ★ ★     新華社 電訊稿 數據庫     檢索 命中   44375 ...       9\n",
              "2635                        濟南鐵路局 積極 做好 防洪 準備 工作 新華社...       9\n",
              "2636                        鳳台 淮河 公路 大橋 通車 新華社 合肥 ５ ...       9\n",
              "\n",
              "[2637 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cPSq_OzIxd3w",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2e3bf23b-81ef-42b7-9bd2-d1af8904d41e"
      },
      "source": [
        "# countvectorizer\n",
        "# 訓練資料=> fit(統計有多少分詞標籤)/transform(統計這些分詞標籤出現次數),所以訓練資料2個都要 = fit_transform\n",
        "# 測試資料=> transform(測試資料不須再做分詞標籤，因為只須透過訓練分詞來分辨判斷，新的分詞就不理)\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "vec = CountVectorizer()\n",
        "train_count =  vec.fit_transform(train_data[\"content\"])\n",
        "test_count = vec.transform(test_data[\"content\"])\n",
        "#訓練資料裡每首詩對每個分詞欄位出現的次數\n",
        "print(train_count)\n",
        "# vec.vocabulary_ => 字典\n",
        "# 利用vec.vocabulary_[\",\"]等等標點符號或換行是否還在裡面\n",
        "#vec.vocabulary_ (每個train_count中分詞欄位對應的分詞文字)\n",
        "\n",
        "#測試資料裡每首詩對每個分詞欄位出現的次數\n",
        "print(test_count)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  (0, 39035)\t4\n",
            "  (0, 14348)\t4\n",
            "  (0, 5567)\t3\n",
            "  (0, 54943)\t1\n",
            "  (0, 38259)\t1\n",
            "  (0, 69887)\t1\n",
            "  (0, 50877)\t1\n",
            "  (0, 4177)\t4\n",
            "  (0, 50271)\t2\n",
            "  (0, 65866)\t2\n",
            "  (0, 62649)\t7\n",
            "  (0, 50878)\t1\n",
            "  (0, 818)\t1\n",
            "  (0, 25)\t2\n",
            "  (0, 59)\t1\n",
            "  (0, 202)\t1\n",
            "  (0, 1467)\t1\n",
            "  (0, 0)\t1\n",
            "  (0, 39335)\t1\n",
            "  (0, 12931)\t1\n",
            "  (0, 11491)\t1\n",
            "  (0, 38181)\t1\n",
            "  (0, 65867)\t1\n",
            "  (0, 62492)\t2\n",
            "  (0, 19422)\t1\n",
            "  :\t:\n",
            "  (2636, 15401)\t2\n",
            "  (2636, 46096)\t4\n",
            "  (2636, 11197)\t1\n",
            "  (2636, 61805)\t1\n",
            "  (2636, 46094)\t2\n",
            "  (2636, 20701)\t1\n",
            "  (2636, 47953)\t3\n",
            "  (2636, 25337)\t4\n",
            "  (2636, 66766)\t3\n",
            "  (2636, 52729)\t2\n",
            "  (2636, 65353)\t1\n",
            "  (2636, 70612)\t1\n",
            "  (2636, 56569)\t1\n",
            "  (2636, 42880)\t1\n",
            "  (2636, 66857)\t1\n",
            "  (2636, 38335)\t1\n",
            "  (2636, 74149)\t3\n",
            "  (2636, 28041)\t1\n",
            "  (2636, 71035)\t1\n",
            "  (2636, 65077)\t1\n",
            "  (2636, 18309)\t1\n",
            "  (2636, 63528)\t1\n",
            "  (2636, 42044)\t1\n",
            "  (2636, 31832)\t1\n",
            "  (2636, 58179)\t1\n",
            "  (0, 0)\t1\n",
            "  (0, 19)\t1\n",
            "  (0, 25)\t2\n",
            "  (0, 59)\t1\n",
            "  (0, 129)\t1\n",
            "  (0, 202)\t1\n",
            "  (0, 443)\t1\n",
            "  (0, 801)\t1\n",
            "  (0, 818)\t1\n",
            "  (0, 842)\t1\n",
            "  (0, 906)\t1\n",
            "  (0, 1400)\t1\n",
            "  (0, 1467)\t1\n",
            "  (0, 1527)\t1\n",
            "  (0, 1646)\t1\n",
            "  (0, 1699)\t1\n",
            "  (0, 2033)\t1\n",
            "  (0, 2193)\t1\n",
            "  (0, 2404)\t1\n",
            "  (0, 2596)\t2\n",
            "  (0, 2865)\t1\n",
            "  (0, 2916)\t2\n",
            "  (0, 2922)\t1\n",
            "  (0, 3329)\t2\n",
            "  (0, 3406)\t1\n",
            "  :\t:\n",
            "  (2737, 53886)\t2\n",
            "  (2737, 54487)\t1\n",
            "  (2737, 56248)\t2\n",
            "  (2737, 58471)\t3\n",
            "  (2737, 58729)\t1\n",
            "  (2737, 60874)\t1\n",
            "  (2737, 61128)\t1\n",
            "  (2737, 61220)\t2\n",
            "  (2737, 62085)\t1\n",
            "  (2737, 62619)\t1\n",
            "  (2737, 63567)\t1\n",
            "  (2737, 65120)\t5\n",
            "  (2737, 65124)\t1\n",
            "  (2737, 65131)\t1\n",
            "  (2737, 65144)\t2\n",
            "  (2737, 65346)\t2\n",
            "  (2737, 65957)\t1\n",
            "  (2737, 66742)\t2\n",
            "  (2737, 66744)\t2\n",
            "  (2737, 66749)\t1\n",
            "  (2737, 68052)\t1\n",
            "  (2737, 70309)\t1\n",
            "  (2737, 71604)\t2\n",
            "  (2737, 73093)\t1\n",
            "  (2737, 74333)\t1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vVzTHsy4yqXM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "912353de-50e8-4f87-f01a-78ac4313fc25"
      },
      "source": [
        "# 3種單純貝氏'\n",
        "# Gaussian: 連續特徵=>高斯分佈(常態分佈:可以少少的資料就做出一條全部符合此常態方程式,ex:x=4.1、4.11、4.111...都可以透過此常態方程式算出各個機率)\n",
        "# Muitinomial: 整數特徵(文字)\n",
        "# Beronull: 只有「是否」2種特徵\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "clf = MultinomialNB()\n",
        "clf.fit(train_count, train_data[\"answer\"])"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b9TJukS8zSII",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b40fc42f-90b9-49c1-9989-9fe6addd8fa2"
      },
      "source": [
        "#利用測試資料驗證計算出來的模型公式好壞\n",
        "from sklearn.metrics import accuracy_score\n",
        "pre = clf.predict(test_count)\n",
        "print(\"預測結果:\", list(pre))\n",
        "print(\"正確結果:\", list(test_data[\"answer\"]))\n",
        "print(\"正確率:\", accuracy_score(pre, test_data[\"answer\"]))"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "預測結果: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 6, 1, 1, 8, 1, 1, 1, 1, 8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 8, 1, 1, 8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 7, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 8, 2, 2, 6, 2, 2, 2, 2, 2, 2, 2, 6, 2, 2, 2, 2, 2, 2, 2, 2, 2, 8, 2, 2, 7, 2, 2, 2, 6, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 8, 6, 2, 2, 2, 2, 2, 2, 8, 2, 2, 2, 2, 6, 8, 8, 2, 2, 2, 8, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 6, 2, 2, 2, 2, 2, 7, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 8, 3, 3, 3, 3, 3, 8, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 8, 4, 8, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 8, 4, 8, 8, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 8, 8, 4, 4, 4, 4, 3, 4, 8, 4, 8, 4, 8, 4, 4, 4, 4, 4, 4, 4, 4, 8, 4, 8, 4, 4, 4, 4, 7, 4, 8, 8, 4, 8, 4, 4, 8, 4, 4, 4, 4, 4, 4, 4, 4, 4, 8, 8, 4, 4, 8, 4, 8, 8, 4, 4, 4, 8, 4, 7, 4, 4, 4, 4, 4, 4, 4, 7, 8, 4, 8, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 8, 4, 8, 4, 4, 8, 8, 4, 4, 4, 8, 4, 8, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 8, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 8, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 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            "正確結果: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9]\n",
            "正確率: 0.9696859021183345\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IgiOT96Qz4iX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "73ff21ea-3c96-4b68-ffdd-1e703d8d5f22"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "pd.DataFrame(confusion_matrix(test_data[\"answer\"], pre), columns = [n + (\"predict\") for n in newsType], index = [n + (\"true\") for n in newsType])"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>計算機predict</th>\n",
              "      <th>環境predict</th>\n",
              "      <th>醫藥predict</th>\n",
              "      <th>藝術predict</th>\n",
              "      <th>軍事predict</th>\n",
              "      <th>體育predict</th>\n",
              "      <th>經濟predict</th>\n",
              "      <th>教育predict</th>\n",
              "      <th>政治predict</th>\n",
              "      <th>交通predict</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>計算機true</th>\n",
              "      <td>182</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>環境true</th>\n",
              "      <td>0</td>\n",
              "      <td>187</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>醫藥true</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>187</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>藝術true</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>240</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>軍事true</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>199</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>37</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>體育true</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>448</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>經濟true</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>306</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>教育true</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>212</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>政治true</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>504</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>交通true</th>\n",
              "      <td>0</td>\n",
              "      <td>0</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>1</td>\n",
              "      <td>190</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         計算機predict  環境predict  醫藥predict  ...  教育predict  政治predict  交通predict\n",
              "計算機true         182          0          0  ...          0          0          0\n",
              "環境true            0        187          0  ...          0          4          0\n",
              "醫藥true            0          0        187  ...          3          7          0\n",
              "藝術true            0          0          0  ...          0          2          0\n",
              "軍事true            0          0          0  ...          3         37          0\n",
              "體育true            0          0          0  ...          0          1          0\n",
              "經濟true            0          0          0  ...          0          3          0\n",
              "教育true            0          0          0  ...        212          1          0\n",
              "政治true            0          0          0  ...          0        504          0\n",
              "交通true            0          0          0  ...          0          1        190\n",
              "\n",
              "[10 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cO3JKeJtz8as",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "67029d97-8daa-437a-9b4d-a5b78a2a1e39"
      },
      "source": [
        "#利用使用者輸入新聞內容來預測是屬於哪類文章? 環境、經濟...\n",
        "s = input(\"輸入新聞內容:\")\n",
        "#當要predict時一定要先轉群集(list)\n",
        "inputs = [newscut(s)]\n",
        "x = vec.transform(inputs)\n",
        "#依照使用者輸入的預測為屬於哪類文章\n",
        "probality = clf.predict_proba(x)[0]\n",
        "print(\"所有類型文章的預測機率:\", probality)\n",
        "print(\"使用者輸入:\", inputs)\n",
        "\n",
        "for name, prob in zip(newsType, probality):\n",
        "    print(name, \"的預測機率:\", round(prob, 2))\n",
        "userpre = clf.predict(x)[0]\n",
        "# argmax: 找最大值的index\n",
        "print(f\"預測結果此文章類型為『{newsType_r_dict[probality.argmax()]}』最高！！\")"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "輸入新聞內容:國內新增本土個案13例，新北獅子會群聚新增9例個案，四例與台北市萬華區茶藝館群聚相關。指揮中心指揮官陳時中表示，今天新增的案例基本上都和萬華進出有關係。  陳時中表示，案1245、案1246、案1248、案1250、案1251、案1253及案1255至案1257等9人，為7男2女，年齡介於30多歲至80多歲，其中4人無症狀，5人曾有症狀，發病日介於5月6日至5月11日。9人因曾與案1203於同餐廳用餐，匡列為接觸者並進行採檢，於今日確診；截至目前案1203相關群聚共計20人確診。  案1247、案1252及案1254為40多歲女性、30多歲女性及50多歲女性，分別於臺北市萬華區3家不同之茶藝館工作，3人於5月8日至5月10日間陸續出現症狀，並曾至診所就醫，後續分別於5月11日、5月12日至醫院採檢，於今日確診。  另外，案1249為60多歲男性，5月5日出現乾咳、打噴嚏情形，5月7日出現發燒、肌肉痠痛、喉嚨痛及頭痛症狀，5月10日至診所就醫；個案因持續發燒，5月12日至醫院就醫採檢，於今日確診。衛生單位初步調查，個案會定期於臺北市萬華區活動，研判個案與萬華區茶藝館群聚有地緣相關性；已初步掌握個案接觸者3人，列居家隔離並安排採檢。截至目前萬華茶藝館相關群聚共計7人確診。  陳時中進一步表示，案1203群聚相關個案中，有部分個案曾至臺北市萬華區茶藝館活動；另萬華區茶藝館群聚相關個案中，有個案曾至宜蘭縣礁溪鄉信義路「巨城電玩」遊藝場及宜蘭市新民路「大地球」遊藝場活動，因該2家遊藝場為羅東「銀河百家樂」遊藝場之分店，衛生單位將持續深入調查，以釐清該三起群聚事件的關聯性。  陳時中提醒，個案於可傳染期間曾有公共場所活動史，請曾於附件所列時間及場所活動的民眾，進行14天自我健康監測，如有疑似症狀，儘速戴口罩至指定社區採檢院所就醫，不得搭乘大眾運輸，就醫時請主動告知活動史、接觸史等資訊。  截至目前國內有1256例確診，分別為1068例境外移入，135例本土病例，36例敦睦艦隊、2例航空器感染、1例不明及14例調查中；另案530移除為空號。確診個案中12人死亡、、142人住院隔離中。\n",
            "所有類型文章的預測機率: [3.41103953e-30 3.76793699e-15 1.00000000e+00 6.54538881e-34\n",
            " 1.18613628e-27 2.63895171e-31 9.27300284e-23 4.41759554e-37\n",
            " 1.61851238e-68 2.63694256e-21]\n",
            "使用者輸入: ['國內 新增 本土 個案 13 例 ， 新北 獅子會 群聚 新增 9 例 個案 ， 四例 與 台北市 萬華區 茶藝館 群聚 相關 。 指揮中心 指揮官 陳 時 中 表示 ， 今天 新增 的 案例 基本上 都 和 萬華 進出 有 關係 。     陳 時 中 表示 ， 案 1245 、 案 1246 、 案 1248 、 案 1250 、 案 1251 、 案 1253 及案 1255 至案 1257 等 9 人 ， 為 7 男 2 女 ， 年齡 介於 30 多歲 至 80 多歲 ， 其中 4 人 無症狀 ， 5 人 曾 有 症狀 ， 發病 日 介於 5 月 6 日至 5 月 11 日 。 9 人因 曾 與 案 1203 於 同 餐廳 用餐 ， 匡列 為 接觸 者並 進行 採檢 ， 於今 日 確診 ； 截至 目前 案 1203 相關 群聚 共計 20 人 確診 。     案 1247 、 案 1252 及案 1254 為 40 多歲 女性 、 30 多歲 女性 及 50 多歲 女性 ， 分別 於 臺北市 萬華區 3 家 不同 之 茶藝館 工作 ， 3 人 於 5 月 8 日至 5 月 10 日間 陸續 出現 症狀 ， 並曾 至 診所 就醫 ， 後續 分別 於 5 月 11 日 、 5 月 12 日至 醫院 採檢 ， 於今 日 確診 。     另外 ， 案 1249 為 60 多歲 男性 ， 5 月 5 日 出現 乾咳 、 打噴嚏 情形 ， 5 月 7 日 出現 發燒 、 肌肉 痠痛 、 喉嚨痛 及 頭痛 症狀 ， 5 月 10 日至 診所 就醫 ； 個案 因 持續 發燒 ， 5 月 12 日至 醫院 就醫 採檢 ， 於今 日 確診 。 衛生 單位 初步 調查 ， 個案 會 定期 於 臺北市 萬華區 活動 ， 研判 個案 與 萬華區 茶藝館 群聚 有 地緣 相關性 ； 已 初步 掌握 個案 接觸 者 3 人 ， 列 居家 隔離 並 安排 採檢 。 截至 目前 萬華 茶藝館 相關 群聚 共計 7 人 確診 。     陳 時 中 進一步 表示 ， 案 1203 群聚 相關 個案 中 ， 有 部分 個案 曾 至 臺北市 萬華區 茶藝館 活動 ； 另 萬華區 茶藝館 群聚 相關 個案 中 ， 有 個案 曾 至 宜蘭縣 礁溪 鄉 信義路 「 巨城 電玩 」 遊藝場 及 宜蘭市 新民路 「 大 地球 」 遊藝場 活動 ， 因該 2 家 遊藝場 為 羅東 「 銀河 百家樂 」 遊藝場 之 分店 ， 衛生 單位 將 持續 深入調查 ， 以 釐清 該 三起 群聚 事件 的 關聯性 。     陳 時 中 提醒 ， 個案 於 可 傳染 期間 曾 有 公共場所 活動 史 ， 請 曾 於 附件 所 列 時間 及 場所 活動 的 民眾 ， 進行 14 天 自我 健康 監測 ， 如 有 疑似 症狀 ， 儘速 戴 口罩 至 指定 社區 採檢 院所 就醫 ， 不得 搭乘 大眾 運輸 ， 就醫 時請 主動 告知 活動 史 、 接觸 史 等 資訊 。     截至 目前 國內 有 1256 例 確診 ， 分別 為 1068 例 境外 移入 ， 135 例 本土 病例 ， 36 例 敦睦 艦隊 、 2 例 航空器 感染 、 1 例 不明 及 14 例 調查 中 ； 另案 530 移除 為 空號 。 確診 個案 中 12 人 死亡 、 、 142 人 住院 隔離 中 。']\n",
            "計算機 的預測機率: 0.0\n",
            "環境 的預測機率: 0.0\n",
            "醫藥 的預測機率: 1.0\n",
            "藝術 的預測機率: 0.0\n",
            "軍事 的預測機率: 0.0\n",
            "體育 的預測機率: 0.0\n",
            "經濟 的預測機率: 0.0\n",
            "教育 的預測機率: 0.0\n",
            "政治 的預測機率: 0.0\n",
            "交通 的預測機率: 0.0\n",
            "預測結果此文章類型為『醫藥』最高！！\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}