(다인용) F_증분 다시함.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/(%EB%8B%A4%EC%9D%B8%EC%9A%A9)%20F_%EC%A6%9D%EB%B6%84%20%EB%8B%A4%EC%8B%9C%ED%95%A8.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": "54fcb1a4-2113-4fee-83e8-c0b59454feca"
      },
      "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": "4f1cab26-3ef1-4be9-80bd-ca55736bed70"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n",
            " 산업분류자동화\t\t   df_A_inc.csv   df_O_inc.csv\n",
            " A_inc_model.pt\t\t   df_B_inc.csv   df_Q_inc.csv\n",
            " B_inc_model.pt\t\t   df_D_inc.csv   df_R_inc.csv\n",
            " classes.txt\t\t   df_E_inc.csv   E_inc_model.pt\n",
            "'Colab Notebooks'\t   df_J_inc.csv   labels.zip\n",
            "'답안 작성용 파일.csv'\t   df_K_inc.csv  'My Drive'\n",
            " dabunru_inc_model_22.pt   df_M_inc.csv   spell_check\n",
            " dabunru_inc_model.pt\t   df_N_inc.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": 10,
      "metadata": {
        "id": "EalsLxZk59jN"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "df=pd.read_csv('/content/gdrive/MyDrive/spell_check/F_spell_check.csv')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "geFjTXHh5-dy"
      },
      "source": [
        "## 데이터 전처리"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=df.reset_index(drop=True)"
      ],
      "metadata": {
        "id": "9k0K1YRJFjSo"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FpA7i6nJ9wgV",
        "outputId": "7b4a20cf-5cae-4d04-bed5-2c211371bd70"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "424    11152\n",
              "422     5744\n",
              "421     4724\n",
              "423     4360\n",
              "411     3582\n",
              "412     3363\n",
              "426     1566\n",
              "425      559\n",
              "Name: digit_3, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "df['digit_3'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "GV4cXBgUea3j"
      },
      "outputs": [],
      "source": [
        "# 데이터 증분 코드\n",
        "import random\n",
        "import pickle\n",
        "import re\n",
        "def get_only_hangul(line):\n",
        "\tparseText= re.compile('/ ^[ㄱ-ㅎㅏ-ㅣ가-힣]*$/').sub('',line)\n",
        "\treturn parseText\n",
        "\n",
        "def synonym_replacement(words, n):\n",
        "\tnew_words = words.copy()\n",
        "\trandom_word_list = list(set([word for word in words]))\n",
        "\trandom.shuffle(random_word_list)\n",
        "\tnum_replaced = 0\n",
        "\tfor random_word in random_word_list:\n",
        "\t\tsynonyms = get_synonyms(random_word)\n",
        "\t\tif len(synonyms) >= 1:\n",
        "\t\t\tsynonym = random.choice(list(synonyms))\n",
        "\t\t\tnew_words = [synonym if word == random_word else word for word in new_words]\n",
        "\t\t\tnum_replaced += 1\n",
        "\t\tif num_replaced >= n:\n",
        "\t\t\tbreak\n",
        "\tif len(new_words) != 0:\n",
        "\t\tsentence = ' '.join(new_words)\n",
        "\t\tnew_words = sentence.split(\" \")\n",
        "\telse:\n",
        "\t\tnew_words = \"\"\n",
        "\treturn new_words\n",
        "\n",
        "def get_synonyms(word):\n",
        "\tsynomyms = []\n",
        "\ttry:\n",
        "\t\tfor syn in wordnet[word]:\n",
        "\t\t\tfor s in syn:\n",
        "\t\t\t\tsynomyms.append(s)\n",
        "\texcept:\n",
        "\t\tpass\n",
        "\treturn synomyms\n",
        "\n",
        "def random_deletion(words, p):\n",
        "\tif len(words) == 1:\n",
        "\t\treturn words\n",
        "\n",
        "\tnew_words = []\n",
        "\tfor word in words:\n",
        "\t\tr = random.uniform(0, 1)\n",
        "\t\tif r > p:\n",
        "\t\t\tnew_words.append(word)\n",
        "\tif len(new_words) == 0:\n",
        "\t\trand_int = random.randint(0, len(words)-1)\n",
        "\t\treturn [words[rand_int]]\n",
        "\treturn new_words\n",
        "\n",
        "########################################################################\n",
        "# Random swap\n",
        "# Randomly swap two words in the sentence n times\n",
        "########################################################################\n",
        "def random_swap(words, n):\n",
        "\tnew_words = words.copy()\n",
        "\tfor _ in range(n):\n",
        "\t\tnew_words = swap_word(new_words)\n",
        "\treturn new_words\n",
        "\n",
        "def swap_word(new_words):\n",
        "\trandom_idx_1 = random.randint(0, len(new_words)-1)\n",
        "\trandom_idx_2 = random_idx_1\n",
        "\tcounter = 0\n",
        "\twhile random_idx_2 == random_idx_1:\n",
        "\t\trandom_idx_2 = random.randint(0, len(new_words)-1)\n",
        "\t\tcounter += 1\n",
        "\t\tif counter > 3:\n",
        "\t\t\treturn new_words\n",
        "\tnew_words[random_idx_1], new_words[random_idx_2] = new_words[random_idx_2], new_words[random_idx_1]\n",
        "\treturn new_words\n",
        "\n",
        "########################################################################\n",
        "# Random insertion\n",
        "# Randomly insert n words into the sentence\n",
        "########################################################################\n",
        "def random_insertion(words, n):\n",
        "\tnew_words = words.copy()\n",
        "\tfor _ in range(n):\n",
        "\t\tadd_word(new_words)\t\n",
        "\treturn new_words\n",
        "\n",
        "def add_word(new_words):\n",
        "\tsynonyms = []\n",
        "\tcounter = 0\n",
        "\twhile len(synonyms) < 1:\n",
        "\t\tif len(new_words) >= 1:\n",
        "\t\t\trandom_word = new_words[random.randint(0, len(new_words)-1)]\n",
        "\t\t\tsynonyms = get_synonyms(random_word)\n",
        "\t\t\tcounter += 1\n",
        "\t\telse:\n",
        "\t\t\trandom_word = \"\"\n",
        "\t\tif counter >= 10:\n",
        "\t\t\treturn\n",
        "\t\t\n",
        "\trandom_synonym = synonyms[0]\n",
        "\trandom_idx = random.randint(0, len(new_words)-1)\n",
        "\tnew_words.insert(random_idx, random_synonym)\n",
        "\n",
        "def EDA(sentence, alpha_sr=0.1, alpha_ri=0.1, alpha_rs=0.1, p_rd=0.1, num_aug=9):\n",
        "\tsentence = get_only_hangul(sentence)\n",
        "\twords = sentence.split(' ')\n",
        "\twords = [word for word in words if word is not \"\"]\n",
        "\tnum_words = len(words)\n",
        "\taugmented_sentences = []\n",
        "\tnum_new_per_technique = int(num_aug/4) + 1\n",
        "\tn_sr = max(1, int(alpha_sr*num_words))\n",
        "\tn_ri = max(1, int(alpha_ri*num_words))\n",
        "\tn_rs = max(1, int(alpha_rs*num_words))\n",
        "\t# sr\n",
        "\tfor _ in range(num_new_per_technique):\n",
        "\t\ta_words = synonym_replacement(words, n_sr)\n",
        "\t\taugmented_sentences.append(' '.join(a_words))\n",
        "\t# ri\n",
        "\tfor _ in range(num_new_per_technique):\n",
        "\t\ta_words = random_insertion(words, n_ri)\n",
        "\t\taugmented_sentences.append(' '.join(a_words))\n",
        "\t# rs\n",
        "\tfor _ in range(num_new_per_technique):\n",
        "\t\ta_words = random_swap(words, n_rs)\n",
        "\t\taugmented_sentences.append(\" \".join(a_words))\n",
        "\t# rd\n",
        "\tfor _ in range(num_new_per_technique):\n",
        "\t\ta_words = random_deletion(words, p_rd)\n",
        "\t\taugmented_sentences.append(\" \".join(a_words))\n",
        "\taugmented_sentences = [get_only_hangul(sentence) for sentence in augmented_sentences]\n",
        "\trandom.shuffle(augmented_sentences)\n",
        "\tif num_aug >= 1:\n",
        "\t\taugmented_sentences = augmented_sentences[:num_aug]\n",
        "\telse:\n",
        "\t\tkeep_prob = num_aug / len(augmented_sentences)\n",
        "\t\taugmented_sentences = [s for s in augmented_sentences if random.uniform(0, 1) < keep_prob]\n",
        "\taugmented_sentences.append(sentence)\n",
        "\treturn augmented_sentences"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "dzTWYLUwea6K"
      },
      "outputs": [],
      "source": [
        "temp_result=[]\n",
        "n=0\n",
        "for sentence in df1['text']:\n",
        "  result_list=EDA(sentence, alpha_sr=0.1, alpha_ri=0.1, alpha_rs=0.1, p_rd=0.1, num_aug=5)\n",
        "  id_list=['p'+str(df1['AI_id'][n]), 'p'+str(df1['AI_id'][n]), 'p'+str(df1['AI_id'][n]), 'p'+str(df1['AI_id'][n]),'p'+str(df1['AI_id'][n]),'p'+str(df1['AI_id'][n])]\n",
        "  digit_1_list=[df1['digit_1'][n], df1['digit_1'][n], df1['digit_1'][n], df1['digit_1'][n], df1['digit_1'][n], df1['digit_1'][n]]\n",
        "  digit_2_list=[df1['digit_2'][n], df1['digit_2'][n], df1['digit_2'][n], df1['digit_2'][n], df1['digit_2'][n], df1['digit_2'][n]]\n",
        "  digit_3_list=[df1['digit_3'][n], df1['digit_3'][n], df1['digit_3'][n], df1['digit_3'][n], df1['digit_3'][n], df1['digit_3'][n]]\n",
        "  data_to_insert={'AI_id' : id_list, 'digit_1' : digit_1_list, 'digit_2' : digit_2_list, 'digit_3': digit_3_list, 'text' : result_list}\n",
        "  df1_to_insert = pd.DataFrame(data_to_insert)\n",
        "  df1= df1.append(df1_to_insert, ignore_index=True)\n",
        "  n=n+1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I90LypFcePR4"
      },
      "source": [
        "기존 데이터 합치기"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.concat([df, df1])"
      ],
      "metadata": {
        "id": "JvWl7U5S2EQE"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "RVEc2ID62e8R",
        "outputId": "83002eb0-74e7-4eb2-d89b-745a71e25f09"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             AI_id digit_1  digit_2  digit_3  \\\n",
              "0       id_0001765       F       42      425   \n",
              "1       id_0001962       F       42      425   \n",
              "2       id_0002818       F       42      425   \n",
              "3       id_0003478       F       42      425   \n",
              "4       id_0003751       F       42      425   \n",
              "...            ...     ...      ...      ...   \n",
              "38411  pid_0999993       F       41      412   \n",
              "38412  pid_0999993       F       41      412   \n",
              "38413  pid_0999993       F       41      412   \n",
              "38414  pid_0999993       F       41      412   \n",
              "38415  pid_0999993       F       41      412   \n",
              "\n",
              "                                               text  \n",
              "0      사업장에서 고객의 의뢰를 받아 전문건설하도급 (시설물 유지관리 공사, 상하수도)  \n",
              "1                   고객의 사업장에서 고객 요청에 따라 시설물 유지관리 공사  \n",
              "2                         건설현장에서 고객의 의뢰를 받아 토목시설 건설  \n",
              "3                            현장에서 고객의 요청으로 시설물 유지공사  \n",
              "4                         사업체에서 시설을 갖추고 난방보일러 유지 보수  \n",
              "...                                             ...  \n",
              "38411                    고객의 조경건설공사 고객 요구의 따라 사업장에서  \n",
              "38412                    고객의 사업장에서 고객 요구의 따라 조경건설공사  \n",
              "38413                    고객의 사업장에서 고객 요구의 따라 조경건설공사  \n",
              "38414                    고객의 사업장에서 고객 요구의 따라 조경건설공사  \n",
              "38415                    고객의 사업장에서 고객 요구의 따라 조경건설공사  \n",
              "\n",
              "[38416 rows x 5 columns]"
            ],
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              "  <div id=\"df-10682836-daba-414d-8636-f5af3b5f84be\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AI_id</th>\n",
              "      <th>digit_1</th>\n",
              "      <th>digit_2</th>\n",
              "      <th>digit_3</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>id_0001765</td>\n",
              "      <td>F</td>\n",
              "      <td>42</td>\n",
              "      <td>425</td>\n",
              "      <td>사업장에서 고객의 의뢰를 받아 전문건설하도급 (시설물 유지관리 공사, 상하수도)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>id_0001962</td>\n",
              "      <td>F</td>\n",
              "      <td>42</td>\n",
              "      <td>425</td>\n",
              "      <td>고객의 사업장에서 고객 요청에 따라 시설물 유지관리 공사</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>id_0002818</td>\n",
              "      <td>F</td>\n",
              "      <td>42</td>\n",
              "      <td>425</td>\n",
              "      <td>건설현장에서 고객의 의뢰를 받아 토목시설 건설</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>id_0003478</td>\n",
              "      <td>F</td>\n",
              "      <td>42</td>\n",
              "      <td>425</td>\n",
              "      <td>현장에서 고객의 요청으로 시설물 유지공사</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>id_0003751</td>\n",
              "      <td>F</td>\n",
              "      <td>42</td>\n",
              "      <td>425</td>\n",
              "      <td>사업체에서 시설을 갖추고 난방보일러 유지 보수</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>38411</th>\n",
              "      <td>pid_0999993</td>\n",
              "      <td>F</td>\n",
              "      <td>41</td>\n",
              "      <td>412</td>\n",
              "      <td>고객의 조경건설공사 고객 요구의 따라 사업장에서</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38412</th>\n",
              "      <td>pid_0999993</td>\n",
              "      <td>F</td>\n",
              "      <td>41</td>\n",
              "      <td>412</td>\n",
              "      <td>고객의 사업장에서 고객 요구의 따라 조경건설공사</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38413</th>\n",
              "      <td>pid_0999993</td>\n",
              "      <td>F</td>\n",
              "      <td>41</td>\n",
              "      <td>412</td>\n",
              "      <td>고객의 사업장에서 고객 요구의 따라 조경건설공사</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38414</th>\n",
              "      <td>pid_0999993</td>\n",
              "      <td>F</td>\n",
              "      <td>41</td>\n",
              "      <td>412</td>\n",
              "      <td>고객의 사업장에서 고객 요구의 따라 조경건설공사</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38415</th>\n",
              "      <td>pid_0999993</td>\n",
              "      <td>F</td>\n",
              "      <td>41</td>\n",
              "      <td>412</td>\n",
              "      <td>고객의 사업장에서 고객 요구의 따라 조경건설공사</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>38416 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-10682836-daba-414d-8636-f5af3b5f84be')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-10682836-daba-414d-8636-f5af3b5f84be button.colab-df-convert');\n",
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              "                                                     [key], {});\n",
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            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['digit_3'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bQNqAurP2QW9",
        "outputId": "dabec82f-8f7e-4637-a300-91bf72689e8d"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "412    26904\n",
              "426    12528\n",
              "424    11152\n",
              "422     5744\n",
              "421     4724\n",
              "425     4472\n",
              "423     4360\n",
              "411     3582\n",
              "Name: digit_3, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "3MzQRuCp6gnE"
      },
      "outputs": [],
      "source": [
        "df.loc[(df['digit_3'] == 411), 'digit_3'] = 0  \n",
        "df.loc[(df['digit_3'] == 412), 'digit_3'] = 1  \n",
        "df.loc[(df['digit_3'] == 421), 'digit_3'] = 2  \n",
        "df.loc[(df['digit_3'] == 422), 'digit_3'] = 3  \n",
        "df.loc[(df['digit_3'] == 423), 'digit_3'] = 4  \n",
        "df.loc[(df['digit_3'] == 424), 'digit_3'] = 5  \n",
        "df.loc[(df['digit_3'] == 425), 'digit_3'] = 6  \n",
        "df.loc[(df['digit_3'] == 426), 'digit_3'] = 7  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "xdv6VPGk_dIX"
      },
      "outputs": [],
      "source": [
        "df.to_csv('df_F_inc.csv', index=False, encoding='utf-8-sig')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DvplvVddk7kT"
      },
      "outputs": [],
      "source": [
        ""
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "background_execution": "on",
      "collapsed_sections": [],
      "machine_shape": "hm",
      "name": "연습파일_220320",
      "provenance": [],
      "include_colab_link": true
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    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
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  "nbformat": 4,
  "nbformat_minor": 0
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