(치종)대분류_spell_check의_사본의_사본.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/KorStats/classification-project/blob/main/(%EC%B9%98%EC%A2%85)%EB%8C%80%EB%B6%84%EB%A5%98_spell_check%EC%9D%98_%EC%82%AC%EB%B3%B8%EC%9D%98_%EC%82%AC%EB%B3%B8.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U3wrPXQ_ORLb"
      },
      "source": [
        "## 구글드라이브에서 파일 읽어오기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dqJbzoq_OPxK",
        "outputId": "5ba513dc-a02f-4552-8114-78e4df67c4cc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wmtIZqMGOdVX",
        "outputId": "50feed29-8ccf-4865-9726-e5cdbd93b9a8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n",
            " 데이터\t\t   '답안 작성용 파일.csv'     dabunru_inc_model.pt   labels.zip\n",
            " 산업분류자동화     dabunru_inc_model_22.pt   df_H_inc_노이.csv     'My Drive'\n",
            "'Colab Notebooks'   dabunru_inc_model_33.pt   df_P_inc_노이.csv      val.txt\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')\n",
        "!ln -s /content/gdrive/My\\ Drive/ /mydrive\n",
        "!ls /mydrive"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xV_iSLl7QFgx",
        "outputId": "b1e63826-9778-4b48-dbab-cee12b071644"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  /content/gdrive/MyDrive/산업분류자동화/모델 개발용 자료.zip\n",
            "2. 모델개발용자료.txt:  mismatching \"local\" filename (2. ыкиыН╕ъ░Ьы░ЬьЪйьЮРыгМ.txt),\n",
            "         continuing with \"central\" filename version\n",
            "  inflating: /content/dataset/2. 모델개발용자료.txt  \n"
          ]
        }
      ],
      "source": [
        "!unzip '/content/gdrive/MyDrive/산업분류자동화/모델 개발용 자료.zip' -d /content/dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "-lGZy4fGpXlo"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "df=pd.read_csv('/content/dataset/2. 모델개발용자료.txt', sep = \"|\", encoding = \"euc-kr\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "geFjTXHh5-dy"
      },
      "source": [
        "## 데이터 전처리"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "3ISvtyKhpKd2",
        "outputId": "4a875a6f-60ff-41b4-fb2d-c10c6199ef29"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
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              "           AI_id  digit_1  digit_2  digit_3  text_obj   text_mthd text_deal\n",
              "0      id_000001      NaN      NaN      NaN   치킨전문점에서    고객의주문에의해      치킨판매\n",
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              "3      id_000004      NaN      NaN      NaN     영업장에서       고객요구로     자동차튜닝\n",
              "4      id_000005      NaN      NaN      NaN  실내포장마차에서   접객시설을 갖추고   소주,맥주제공\n",
              "...          ...      ...      ...      ...       ...         ...       ...\n",
              "99995  id_099996      NaN      NaN      NaN     사업장에서     일반인대상으로      버섯농장\n",
              "99996  id_099997      NaN      NaN      NaN     한의원에서     외래환자위주고        치료\n",
              "99997  id_099998      NaN      NaN      NaN    일반점포에서       소비자에게      그림판매\n",
              "99998  id_099999      NaN      NaN      NaN     사업장에서  일반인.학생대상으로    학습공간제공\n",
              "99999  id_100000      NaN      NaN      NaN     사업장에서    대리현대아파트를        관리\n",
              "\n",
              "[100000 rows x 7 columns]"
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          "metadata": {},
          "execution_count": 5
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            "text/plain": [
              "           AI_id  digit_1  digit_2  digit_3                        text\n",
              "0      id_000001      NaN      NaN      NaN       치킨전문점에서 고객의주문에의해 치킨판매\n",
              "1      id_000002      NaN      NaN      NaN       산업공구 다른 소매업자에게 철물 수공구\n",
              "2      id_000003      NaN      NaN      NaN         절에서 신도을 대상으로 불교단체운영\n",
              "3      id_000004      NaN      NaN      NaN           영업장에서 고객요구로 자동차튜닝\n",
              "4      id_000005      NaN      NaN      NaN  실내포장마차에서 접객시설을 갖추고 소주,맥주제공\n",
              "...          ...      ...      ...      ...                         ...\n",
              "99995  id_099996      NaN      NaN      NaN          사업장에서 일반인대상으로 버섯농장\n",
              "99996  id_099997      NaN      NaN      NaN            한의원에서 외래환자위주고 치료\n",
              "99997  id_099998      NaN      NaN      NaN           일반점포에서 소비자에게 그림판매\n",
              "99998  id_099999      NaN      NaN      NaN     사업장에서 일반인.학생대상으로 학습공간제공\n",
              "99999  id_100000      NaN      NaN      NaN           사업장에서 대리현대아파트를 관리\n",
              "\n",
              "[100000 rows x 5 columns]"
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>실내포장마차에서 접객시설을 갖추고 소주,맥주제공</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99995</th>\n",
              "      <td>id_099996</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>사업장에서 일반인대상으로 버섯농장</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99996</th>\n",
              "      <td>id_099997</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>한의원에서 외래환자위주고 치료</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99997</th>\n",
              "      <td>id_099998</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>일반점포에서 소비자에게 그림판매</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99998</th>\n",
              "      <td>id_099999</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>사업장에서 일반인.학생대상으로 학습공간제공</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99999</th>\n",
              "      <td>id_100000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>사업장에서 대리현대아파트를 관리</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100000 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-673553cb-d0c8-46f9-bd42-cd6c1d852be4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-673553cb-d0c8-46f9-bd42-cd6c1d852be4 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-673553cb-d0c8-46f9-bd42-cd6c1d852be4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "df['text']=df['text_obj'].map(str)+\" \"+df['text_mthd'].map(str)+\" \"+df['text_deal'].map(str)\n",
        "i=[]\n",
        "for sentence in df['text']:\n",
        "    a=sentence.replace('nan', '')\n",
        "    i.append(a)\n",
        "df['text']=pd.DataFrame(i)\n",
        "df=df.drop(['text_obj','text_mthd', 'text_deal'], axis=1) # 기존에 있던 세개의 text변수 삭제\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yzAnY0FvePIR"
      },
      "source": [
        "## 맞춤법 교정"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install git+https://github.com/ssut/py-hanspell"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U0g6lgW8yFAt",
        "outputId": "5330addc-0632-49b9-e80e-8c79af47a033"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting git+https://github.com/ssut/py-hanspell\n",
            "  Cloning https://github.com/ssut/py-hanspell to /tmp/pip-req-build-j468_57d\n",
            "  Running command git clone -q https://github.com/ssut/py-hanspell /tmp/pip-req-build-j468_57d\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from py-hanspell==1.1) (2.23.0)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->py-hanspell==1.1) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->py-hanspell==1.1) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->py-hanspell==1.1) (2021.10.8)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->py-hanspell==1.1) (2.10)\n",
            "Building wheels for collected packages: py-hanspell\n",
            "  Building wheel for py-hanspell (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for py-hanspell: filename=py_hanspell-1.1-py3-none-any.whl size=4868 sha256=a7c42034f7eabf4cd5341cad12d3146989b4a4d383ed7ef9a3d5ed5b064d439f\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-oce9bn4a/wheels/31/c2/c7/ddbbd9a84ddb0a67c2e52e3324d4b7b47dcf9581af9cd784de\n",
            "Successfully built py-hanspell\n",
            "Installing collected packages: py-hanspell\n",
            "Successfully installed py-hanspell-1.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from hanspell import spell_checker\n",
        "\n",
        "temp_result=[]\n",
        "for sentence in df['text']:\n",
        "  try:\n",
        "    result=spell_checker.check(sentence)\n",
        "    temp_result.append(result.checked)\n",
        "  except:\n",
        "    temp_result.append(sentence)\n",
        "\n",
        "df['text']=pd.DataFrame(temp_result)  "
      ],
      "metadata": {
        "id": "8IykblSnyRsV"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.to_csv('ts_spellcheck.csv', index=False, encoding='utf-8-sig')"
      ],
      "metadata": {
        "id": "y6d2WHbeN_23"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "8Sbfmc8wk2rM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "jaX3WX6AbMir"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "_LNfL2uBbMXM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "aiu1NmRRbMQ5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "lUGNUdyVEZ0O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "lLiEghs_ipwE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "Ucz8KNC5phBG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "tjDlXoXdwmkv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "NtxTv4Ne3eBx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "poU_tQ9Y-HkP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "RLPboKlHruIH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "VX1VKM0ZHLgT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "22ua8DLzVIwr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "ACNjJ6s4cANs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "blyaQJOHHLv2"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "background_execution": "on",
      "collapsed_sections": [],
      "machine_shape": "hm",
      "name": "대분류 spell check의 사본의 사본",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}