tried_code.ipynb

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  "metadata": {
    "colab": {
      "name": "tried_code.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "7if0wIKwtyUB",
        "ga2UVWCTxtUk"
      ],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/dlskawns/cp2/blob/main/tried_code.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Colab 환경에서 보는 것을 권장합니다."
      ],
      "metadata": {
        "id": "K7SKu3Q0s2eR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "# 기본적인 환경설정"
      ],
      "metadata": {
        "id": "p-EpBoVC6YRE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### mecab 및 필요 모듈 설치"
      ],
      "metadata": {
        "id": "dJx1UFyLr1uj"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "E2TfimkqG88s"
      },
      "outputs": [],
      "source": [
        "!pip install transformers"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%bash\n",
        "apt-get update\n",
        "apt-get install g++ openjdk-8-jdk python-dev python3-dev\n",
        "pip3 install JPype1\n",
        "pip3 install konlpy"
      ],
      "metadata": {
        "id": "hjmq74z6JshB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%env JAVA_HOME \"/usr/lib/jvm/java-8-openjdk-amd64\"\n"
      ],
      "metadata": {
        "id": "97hnhll0Jsdi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "%%bash\n",
        "bash <(curl -s https://raw.githubusercontent.com/konlpy/konlpy/master/scripts/mecab.sh)\n",
        "pip3 install /tmp/mecab-python-0.996"
      ],
      "metadata": {
        "id": "lu9WvD09Jsaf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 데이터 불러오기"
      ],
      "metadata": {
        "id": "7if0wIKwtyUB"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gkazlzyi0w92",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5807fa3e-7a68-47d1-d89f-3449faf4c57b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "\n",
        "\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# df = pd.read_excel('/content/drive/MyDrive/policy_metetable(AIB전달용).xlsx')\n",
        "# 엑셀파일 안읽힐때 이거사용쓰\n",
        "df = pd.read_excel('/content/drive/MyDrive/policy_metetable(AIB전달용).xlsx', engine='openpyxl')"
      ],
      "metadata": {
        "id": "0Mt58TuMLPr3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ans = pd.read_csv('//content/drive/MyDrive/웰로/data/wello_admin_답지.csv', error_bad_lines=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZKM9im7N3BS-",
        "outputId": "f365a677-2487-476f-9b2f-a8a332a54449"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "b'Skipping line 2439: expected 24 fields, saw 25\\n'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 태깅 모델"
      ],
      "metadata": {
        "id": "66PpdBRBsUrv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1. 개체명 인식기를 활용한 자체 태그 추출방식\n",
        "\n",
        "태깅을 위한 Text 내 개체명 인식을 진행.\n",
        "\n",
        "<br>  \n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "P0Wr1v230peG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1) Konlpy 패키지의 형태소 분석기 중 Mecab 사용 및 BIO 표현을 통한 개체명 인식기 직접학습.\n",
        "\n",
        "* 선정 이유:  \n",
        "여러 조사에 따라 Mecab이 성능이 준수하면서도 가장 빠른 컨디션의 한국어 형태소 분석기로 사용된다는 점을 감안해 이용했다. 가장 널리 사용되는 BIO 학습방법을 통해 개체명 인식기를 만들고, 이를 통해 태그를 추출할 것은 목표로 두었음.\n",
        "\n",
        "#### 가설 i)  \n",
        "**정책 데이터는 오타, 띄어쓰기 등 어느정도 올바른 문장/단어이므로 성능이 평균 이상만 된다면 속도가 빠른 Mecab을 사용하는 것이 유리할 것이다.**\n",
        " \n",
        "* 방법:  \n",
        " - Mecab의 품사 태깅, 명사 추출을 이용해 각 문맥 데이터의 전처리를 빠르게 진행해 키워드 및 태그를 추출하는 방식.\n",
        "\n",
        "* 이슈 및 특이사항:  \n",
        " - 예상과 달리, 문맥 데이터가 복잡해서 속도보다 Mecab 보다 성능이 좋을 것으로 예상되는 다른 model을 응용할 필요가 있음.\n",
        "\n",
        "<br>\n",
        "\n",
        "#### 가설 ii)  \n",
        "**BIO 표현을 이용해, 특정 키워드에 태그로 반환해 특성(Feature) 별 태그를 추출할 수 있다.**\n",
        "\n",
        " * 방법:  \n",
        "  - Mecab으로 명사를 뽑아낸 뒤, BIO 표현을 이용하여 특정 키워드에 대해 태그(B)를, 그 외에는 모두 (O)를 반환하는 모델을 작성한다.   \n",
        "\n",
        " * 이슈 및 특이사항:  \n",
        "  -  키워드에 따른 태그 추출이 쉬운 특성에만 적용이 가능하다.  \n",
        " ㄴ> 성별(남성/여성/무관), 신청절차(온라인/오프라인/복합) 등 단순한 특성에서만 빈도에 따른 태그 추출의 방법으로 처리 가능하다.\n",
        "\n",
        "  -  키워드 간 복잡한 연관성이 있는 경우, 그 규칙을 찾는 것이 어렵고, 키워드가 무수히 많을 수 있다.\n",
        "\n",
        "<br>  \n",
        "\n",
        "<br>  "
      ],
      "metadata": {
        "id": "UDWTpOIRpj9M"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 코드 구현"
      ],
      "metadata": {
        "id": "gjt5x0fL6zuh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 개체명 인식기 설계 (신청절차 태그추출)\n"
      ],
      "metadata": {
        "id": "U3xNhA7sDnja"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from konlpy.tag import Mecab\n",
        "mecab = Mecab()"
      ],
      "metadata": {
        "id": "bAcF8tKPmgBT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import copy\n",
        "# 개체명 인식을 위한 키워드 별 태그파악 및 문장 분리 작업\n",
        "\n",
        "'''\n",
        "A10-00 : 무관 태그\n",
        "A10-01 : 온라인 태그\n",
        "A10-02 : 오프라인 태그\n",
        "O : 이외의 문자\n",
        "BERT 스페셜 토큰 : [CLS], [SEP]\n",
        "[PAD] : 토큰 길이(샘플 별로 문장의 길이가 다르므로)가 기준에 비해 짧을 경우 채워주는 패딩 토큰\n",
        "오프라인과 온라인을 나타내는 키워드를 제외하고는 모두 O로 표시하여 무시한다.\n",
        "\n",
        "'''\n",
        "\n",
        "df1 = df[:2000]['신청절차'].copy()  # 2000개 샘플만 진행해본다.\n",
        "d = pd.DataFrame()\n",
        "df1 = df1.fillna('아무것도없는샘플')\n",
        "for n, i in enumerate(df1):\n",
        "  print(n)\n",
        "  tok = []        # 문장 토큰을 넣을 곳\n",
        "  \n",
        "  if i != '아무것도없는샘플':\n",
        "    token = mecab.nouns(i)\n",
        "    tok.append([f'Sentence: {n}','[CLS]]','[CLS]'])\n",
        "    for j in token:   # 토큰의 조건 별 태그 추출\n",
        "      if sum(j.count(x) for x in ['방문','우편','서면','팩스','은행','영업점','사무소','내방']) >= 1 :\n",
        "        tok.append([f'Sentence: {n}',j,'A10-02'])\n",
        "      elif sum(j.count(x) for x in ['모바일','ARS','전화','민원24','온라인','이메일','인터넷','홈택스','홈페이지','앱']) >= 1 :\n",
        "        tok.append([f'Sentence: {n}',j,'A10-01'])  \n",
        "      else:\n",
        "        tok.append([f'Sentence: {n}', j,'OO'])\n",
        "  elif i == '아무것도없는샘플':\n",
        "    tok.append([f'Sentence: {n}','[CLS]]','[CLS]'])\n",
        "    tok.append([f'Sentence: {n}', '아무것도없는샘플','A10-00'])\n",
        "\n",
        "  tok.append([f'Sentence: {n}','[SEP]','[SEP]'])\n",
        "  a =  pd.DataFrame(tok)\n",
        "  d = pd.concat([d,a], axis=0)\n",
        "\n",
        "d.columns = ['Sentence #','Word','Tag']"
      ],
      "metadata": {
        "id": "nX4zqfQVbdQI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "d"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "Wv96xshxK8X_",
        "outputId": "031b0689-a441-4972-ca26-f0f5eb1c1102"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sentence #</th>\n",
              "      <th>Word</th>\n",
              "      <th>Tag</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Sentence: 0</td>\n",
              "      <td>[CLS]</td>\n",
              "      <td>[CLS]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Sentence: 0</td>\n",
              "      <td>신청</td>\n",
              "      <td>OO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Sentence: 0</td>\n",
              "      <td>읍</td>\n",
              "      <td>OO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Sentence: 0</td>\n",
              "      <td>동사무소</td>\n",
              "      <td>A10-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Sentence: 0</td>\n",
              "      <td>보장</td>\n",
              "      <td>OO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Sentence: 1999</td>\n",
              "      <td>일자리</td>\n",
              "      <td>OO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Sentence: 1999</td>\n",
              "      <td>담당</td>\n",
              "      <td>OO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Sentence: 1999</td>\n",
              "      <td>부서</td>\n",
              "      <td>OO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Sentence: 1999</td>\n",
              "      <td>신청</td>\n",
              "      <td>OO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Sentence: 1999</td>\n",
              "      <td>[SEP]</td>\n",
              "      <td>[SEP]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>34063 rows × 3 columns</p>\n",
              "</div>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
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              "            '<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",
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              "  "
            ],
            "text/plain": [
              "        Sentence #   Word     Tag\n",
              "0      Sentence: 0  [CLS]   [CLS]\n",
              "1      Sentence: 0     신청      OO\n",
              "2      Sentence: 0      읍      OO\n",
              "3      Sentence: 0   동사무소  A10-02\n",
              "4      Sentence: 0     보장      OO\n",
              "..             ...    ...     ...\n",
              "8   Sentence: 1999    일자리      OO\n",
              "9   Sentence: 1999     담당      OO\n",
              "10  Sentence: 1999     부서      OO\n",
              "11  Sentence: 1999     신청      OO\n",
              "12  Sentence: 1999  [SEP]   [SEP]\n",
              "\n",
              "[34063 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4xEwKJHI00Wx"
      },
      "outputs": [],
      "source": [
        "# 정리된 데이터 프레임 내에서 문장 별로 단어와 태그를 정리해준다.\n",
        "class SentenceGetter(object):\n",
        "    def __init__(self, data):\n",
        "        self.n_sent = 1\n",
        "        self.data = data\n",
        "        self.empty = False\n",
        "        agg_func = lambda s: [(w, t) for w,  t in zip(s[\"Word\"].values.tolist(),\n",
        "                                                          #  s[\"POS\"].values.tolist(),\n",
        "                                                           s[\"Tag\"].values.tolist())]\n",
        "        self.grouped = self.data.groupby(\"Sentence #\").apply(agg_func)\n",
        "        self.sentences = [s for s in self.grouped]\n",
        "    \n",
        "    def get_next(self):\n",
        "        try:\n",
        "            s = self.grouped[\"Sentence: {}\".format(self.n_sent)]\n",
        "            self.n_sent += 1\n",
        "            return s\n",
        "        except:\n",
        "            return None"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9t3wcM6k00Wy"
      },
      "outputs": [],
      "source": [
        "getter = SentenceGetter(d)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "i81H9HGQ00W0"
      },
      "outputs": [],
      "source": [
        "sentences = [[word[0] for word in sentence] for sentence in getter.sentences] # 문장의 토큰 리스트\n",
        "labels = [[s[1] for s in sent] for sent in getter.sentences]                  # 문장의 라벨(태그) 리스트"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tag_values = list(set(d[\"Tag\"].values))         \n",
        "tag_values.append(\"[PAD]\")\n",
        "tag2idx = {t: i for i, t in enumerate(tag_values)}\n",
        "index_to_ner = {i:j for j, i in tag2idx.items()}"
      ],
      "metadata": {
        "id": "_4gIgq9Ycw_Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 태그의 종류\n",
        "tag_values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g2q_z_JlYfkp",
        "outputId": "658e98e5-051b-4644-98bb-3891454816b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['OO', '[SEP]', 'A10-02', 'A10-00', 'A10-01', '[CLS]', '[PAD]']"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Bert 토크나이저로 학습진행"
      ],
      "metadata": {
        "id": "YVfirgegbYml"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import BertModel, BertTokenizer\n",
        "\n",
        "tokenizer = BertTokenizer.from_pretrained('bert-base-multilingual-cased')"
      ],
      "metadata": {
        "id": "o4pHxeGXOWoV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# word piece tokenizing 하면서 label이 단어를 잃지 않도록 처리하는 함수\n",
        "def tokenize_and_preserve_labels(sentence, text_labels):\n",
        "    tokenized_sentence = []\n",
        "    labels = []\n",
        "\n",
        "    for word, label in zip(sentence, text_labels):\n",
        "        # 단어를 토큰화 하고, 떨어진 서브워드 개수를 센다.\n",
        "        tokenized_word = tokenizer.tokenize(str(word))\n",
        "        n_subwords = len(tokenized_word)\n",
        "\n",
        "        # Add the tokenized word to the final tokenized word list\n",
        "        # 최종 토크나이즈드 워드 \n",
        "        tokenized_sentence.extend(tokenized_word)\n",
        "\n",
        "        # Add the same label to the new list of labels `n_subwords` times\n",
        "        labels.extend([label] * n_subwords)\n",
        "\n",
        "    return tokenized_sentence, labels"
      ],
      "metadata": {
        "id": "sCOWgbzZd5Fr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_texts_and_labels = list()\n",
        "# Bert 토크나이저로 각각의 words에 따른 labels을 인식시켜준다\n",
        "for sent, labs in zip(sentences, labels):\n",
        "  tokenized_texts_and_labels.append(tokenize_and_preserve_labels(sent, labs))"
      ],
      "metadata": {
        "id": "EsNOobB1d5DJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "tokenized_texts = [token_label_pair[0] for token_label_pair in tokenized_texts_and_labels]\n",
        "labels = [token_label_pair[1] for token_label_pair in tokenized_texts_and_labels]\n",
        "print(tokenized_texts[1])\n",
        "print(labels[1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zxprCHfSQLaL",
        "outputId": "0179b43f-c5b7-40d7-fef1-9285a36a14b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['[CLS]', ']', '개', '##벌', '신', '##청', '경우', '모', '##바', '##일', '국', '##세', '##청', '홈', '택', '##스', '앱', '인', '##터', '##넷', '홈', '택', '##스', '서', '##비스', '서', '##면', '신', '##청', '가', '##능', '개', '##별', '신', '##청', '경우', '인', '##터', '##넷', '홈', '택', '##스', '서', '##면', '신', '##청', '가', '##능', '[SEP]']\n",
            "['[CLS]', '[CLS]', 'OO', 'OO', 'OO', 'OO', 'OO', 'A10-01', 'A10-01', 'A10-01', 'OO', 'OO', 'OO', 'OO', 'OO', 'OO', 'A10-01', 'A10-01', 'A10-01', 'A10-01', 'OO', 'OO', 'OO', 'OO', 'OO', 'A10-02', 'A10-02', 'OO', 'OO', 'OO', 'OO', 'OO', 'OO', 'OO', 'OO', 'OO', 'A10-01', 'A10-01', 'A10-01', 'OO', 'OO', 'OO', 'A10-02', 'A10-02', 'OO', 'OO', 'OO', 'OO', '[SEP]']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 토큰 최대길이 설정\n",
        "MAX_LEN = 40"
      ],
      "metadata": {
        "id": "RsqQhJzhVFLE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 토큰을 Bert 토크나이저의 사전에 맞는 인덱스로 변환하고, 특정길이(40)에 맞춰 짧을 경우 0으로 채운다.\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "input_ids = pad_sequences([tokenizer.convert_tokens_to_ids(txt) for txt in tokenized_texts],\n",
        "                          maxlen=MAX_LEN, dtype=\"long\", value=0.0,\n",
        "                          truncating=\"post\", padding=\"post\")"
      ],
      "metadata": {
        "id": "RN2Iq47VQLX6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 라벨로 패딩처리 진행, 길이가 짧을 경우 [PAD]로 채워준다.\n",
        "tags = pad_sequences([[tag2idx.get(l) for l in lab] for lab in labels],\n",
        "                     maxlen=MAX_LEN, value=tag2idx[\"[PAD]\"], padding=\"post\",\n",
        "                     dtype=\"long\", truncating=\"post\")"
      ],
      "metadata": {
        "id": "-UlWhdz5QLT8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 패딩처리를 확인하기 위한 마스킹\n",
        "import numpy as np\n",
        "attention_masks = np.array([[int(i != tokenizer.convert_tokens_to_ids(\"[PAD]\")) for i in ii] for ii in input_ids])"
      ],
      "metadata": {
        "id": "4rcw8veh7t3v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 학습, 검증 셋 분류\n",
        "from sklearn.model_selection import train_test_split\n",
        "tr_inputs, val_inputs, tr_tags, val_tags = train_test_split(input_ids, tags,\n",
        "                                                            random_state=2018, test_size=0.1)\n",
        "\n",
        "tr_masks, val_masks, _, _ = train_test_split(attention_masks, input_ids,\n",
        "                                             random_state=2018, test_size=0.1)"
      ],
      "metadata": {
        "id": "C4-I5td46mWc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TFBertModel\n",
        "import tensorflow as tf\n",
        "\n",
        "\n",
        "SEQ_LEN = MAX_LEN\n",
        "\n",
        "# Huggingface의 Bert 모델 중 bert-base-mulitilingual로 한국어 지원 진행\n",
        "model1 = TFBertModel.from_pretrained(\"bert-base-multilingual-cased\", from_pt=True, num_labels=len(tag2idx), output_attentions = False,\n",
        "  output_hidden_states = False)\n",
        "\n",
        "token_inputs = tf.keras.layers.Input((SEQ_LEN,), dtype=tf.int32, name='input_word_ids') # 토큰 인풋\n",
        "mask_inputs = tf.keras.layers.Input((SEQ_LEN,), dtype=tf.int32, name='input_masks') # 마스크 인풋\n",
        "\n",
        "bert_outputs = model1([token_inputs, mask_inputs])\n",
        "bert_outputs = bert_outputs[0] # shape : (Batch_size, max_len, 30(개체의 총 개수))\n",
        "nr = tf.keras.layers.Dense(len(tag2idx), activation='softmax')(bert_outputs) # shape : (Batch_size, max_len)\n",
        "\n",
        "nr_model1 = tf.keras.Model([token_inputs, mask_inputs], nr)\n",
        "\n",
        "nr_model1.compile(optimizer=tf.keras.optimizers.Adam(lr=0.00002), loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),\n",
        "    metrics=['sparse_categorical_accuracy'])\n",
        "nr_model1.summary()"
      ],
      "metadata": {
        "id": "hX2iaukz6Wvc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 714,
          "referenced_widgets": [
            "7dce4a1240a34e5f88e346fb206aa74c",
            "32cee5535ec94154aab43e38ec1fe0dd",
            "daf82be31fb94026a8439e3ac6fa3f80",
            "4b4bdd88c84843a4988e7ce64c77eb80",
            "b314a5d55d604ed3990bf9a8a44d6746",
            "1c62e42d837f41a5b19eec5bb9a419e3",
            "f4d950f24f0a4cd9b604a1deddafbf8d",
            "b4021ff9ca4e409f99e979e432624c49",
            "6a08e310ea09400fa9e55f126cd09d1b",
            "7546f2a2738443b3baca38e21e00a170",
            "d0e8ff7dc35346609411534de9da03a3"
          ]
        },
        "outputId": "0df57c1e-b896-43b5-d920-34df3c9079cf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7dce4a1240a34e5f88e346fb206aa74c",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/681M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the PyTorch model were not used when initializing the TF 2.0 model TFBertModel: ['cls.predictions.transform.dense.bias', 'cls.predictions.bias', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias']\n",
            "- This IS expected if you are initializing TFBertModel from a PyTorch model trained on another task or with another architecture (e.g. initializing a TFBertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing TFBertModel from a PyTorch model that you expect to be exactly identical (e.g. initializing a TFBertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "All the weights of TFBertModel were initialized from the PyTorch model.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFBertModel for predictions without further training.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_word_ids (InputLayer)    [(None, 40)]         0           []                               \n",
            "                                                                                                  \n",
            " input_masks (InputLayer)       [(None, 40)]         0           []                               \n",
            "                                                                                                  \n",
            " tf_bert_model (TFBertModel)    TFBaseModelOutputWi  177853440   ['input_word_ids[0][0]',         \n",
            "                                thPoolingAndCrossAt               'input_masks[0][0]']            \n",
            "                                tentions(last_hidde                                               \n",
            "                                n_state=(None, 40,                                                \n",
            "                                768),                                                             \n",
            "                                 pooler_output=(Non                                               \n",
            "                                e, 768),                                                          \n",
            "                                 past_key_values=No                                               \n",
            "                                ne, hidden_states=N                                               \n",
            "                                one, attentions=Non                                               \n",
            "                                e, cross_attentions                                               \n",
            "                                =None)                                                            \n",
            "                                                                                                  \n",
            " dense (Dense)                  (None, 40, 7)        5383        ['tf_bert_model[0][0]']          \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 177,858,823\n",
            "Trainable params: 177,858,823\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/adam.py:105: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  super(Adam, self).__init__(name, **kwargs)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "nr_model1.fit([tr_inputs, tr_masks], tr_tags, validation_data=([val_inputs, val_masks], val_tags), batch_size = 64, epochs=3, shuffle=False)"
      ],
      "metadata": {
        "id": "p3hB-kPz6Wve",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d700fece-9bae-40c9-9d3e-1cd8e2b856a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/3\n",
            "WARNING:tensorflow:Gradients do not exist for variables ['tf_bert_model/bert/pooler/dense/kernel:0', 'tf_bert_model/bert/pooler/dense/bias:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss`argument?\n",
            "WARNING:tensorflow:Gradients do not exist for variables ['tf_bert_model/bert/pooler/dense/kernel:0', 'tf_bert_model/bert/pooler/dense/bias:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss`argument?\n",
            "29/29 [==============================] - 32s 590ms/step - loss: 0.4796 - sparse_categorical_accuracy: 0.8569 - val_loss: 0.0575 - val_sparse_categorical_accuracy: 0.9836\n",
            "Epoch 2/3\n",
            "29/29 [==============================] - 14s 500ms/step - loss: 0.0366 - sparse_categorical_accuracy: 0.9900 - val_loss: 0.0108 - val_sparse_categorical_accuracy: 0.9976\n",
            "Epoch 3/3\n",
            "29/29 [==============================] - 15s 504ms/step - loss: 0.0091 - sparse_categorical_accuracy: 0.9981 - val_loss: 0.0038 - val_sparse_categorical_accuracy: 0.9986\n",
            "CPU times: user 46.5 s, sys: 4.9 s, total: 51.4 s\n",
            "Wall time: 1min 36s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f83901b9910>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 문장을 넣으면 태그를 뽑아내는 개체명 인식기 함수 작성 (신청절차 태그)\n",
        "def apply_tags(test_sentence):\n",
        "  \n",
        "  tokenized_sentence = np.array([tokenizer.encode(test_sentence, max_length=MAX_LEN, truncation=True, padding='max_length')])\n",
        "  tokenized_mask = np.array([[int(x!=1) for x in tokenized_sentence[0].tolist()]])\n",
        "  ans = nr_model1.predict([tokenized_sentence, tokenized_mask])\n",
        "  ans = np.argmax(ans, axis=2)\n",
        "\n",
        "  tokens = tokenizer.convert_ids_to_tokens(tokenized_sentence[0])\n",
        "  new_tokens, new_labels = [], []\n",
        "  for token, label_idx in zip(tokens, ans[0]):\n",
        "    if (token=='[CLS]'):\n",
        "      pass\n",
        "    elif (token=='[SEP]'):\n",
        "      pass\n",
        "    elif (token=='[PAD]'):\n",
        "      pass\n",
        "    elif (token != '[CLS]' or token != '[SEP]'):\n",
        "      new_tokens.append(token)\n",
        "      new_labels.append(index_to_ner[label_idx])\n",
        "  \n",
        "  return new_labels"
      ],
      "metadata": {
        "id": "laJ3dkfCpbEi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 개체명 인식기 모델 사용해보기\n",
        "print(apply_tags('인터넷 또는 직접 방문해서 신청 가능'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "09e6c7f4-158b-499c-8c70-089957f15259",
        "id": "fKMQCx9u32Om"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['A10-01', 'A10-01', 'A10-01', 'OO', 'OO', 'A10-02', 'A10-02', 'OO', 'OO', 'OO', 'OO', 'OO']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 모델링 및 태그 추출 결과:  \n",
        "\n",
        "* 모델의 성능은 1에 가까워 키워드 또는 태그 필터링 역할로 사용이 가능하다.\n",
        "* 신청절차와 같은 단순히 온라인 또는 오프라인, 두 가지가 모두 있으면 복합으로 표현할 수 있는 feature에 사용이 유용하다.  \n",
        "\n",
        "#### 문제점\n",
        "\n",
        "  - a) 불가, 제외 등의 키워드가 있는 경우에도 해당 키워드가 존재하므로 잘못된 태그를 추출할 수있다. (ex: 방문 접수만 가능, 온라인 접수는 불가)  \n",
        "\n",
        "  - b) 키워드 간 복잡한 연관성을 파악하지 못해 태그 내 관계가 단순한 경우가 아니면 이러한 개체명 인식기만으로 태그 추출이 어렵다.\n",
        "\n",
        "#### 개선방안  \n",
        "  - a) 불가, 제외 등의 상쇄 키워드에 대한 태그를 추가 학습하고, 결과값에서 태그 근처에 상쇄 태그가 있는 경우, 이를 제거하여 처리한다.  \n",
        "\n",
        "  - b) 태그 또는 키워드를 변환하여 추가적인 신경망 모델을 사용해 복잡한 관계를 파악하도록 한다.\n",
        "\n",
        "<br>\n",
        "\n",
        "<br>"
      ],
      "metadata": {
        "id": "czAcjTQDuD3b"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "\n",
        "<br>\n",
        "\n",
        "<br>\n",
        "\n",
        "### 2) KakaoBrain 개발 자연어처리 라이브러리 Pororo를 이용 \n",
        "\n",
        "* 선정 이유:  \n",
        "kakaobrain 사에서 올해 2월에 선보인 라이브러리로 자체적인 개체명인식 기능을 가지고 있어, 사용할 수 있는 특성에서는 최대한 활용하기 위함\n",
        "\n",
        "#### 가설)  \n",
        "**개체명 인식관련 모듈을 가진 라이브러리 pororo를 이용해 문맥 내 개체명을 필터링 해 태깅 정보를 가져올 수 있을 것이다.**\n",
        "\n",
        "* 방법:  \n",
        "  - pororo 라이브러리 내 자체 개체명 인식 기능을 활용해 지역(loacation), 성별(gender), 연령대(child/adult) 등을 파악한 뒤, 필터링을 거쳐 태그 추출  \n",
        "ex) 지역(loca) 등의 기능을 정책 및 유저 데이터셋의 '시/도' 또는 '시군구' 파악에 사용\n",
        "\n",
        "* 이슈 및 특이사항:  \n",
        "  - 지역명이 포함된 기관명을 분해하여 지역명으로 인식함.\n",
        "  - 정책이라는 특수한 도메인에 사용되는 말뭉치가 아닌, 일상 대화에서 사용되는 말뭉치임.\n",
        "\n",
        "<br>  \n",
        "\n",
        "<br>  \n"
      ],
      "metadata": {
        "id": "rWkBlJlmpf4b"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 코드 구현"
      ],
      "metadata": {
        "id": "-3F_ZzvVjfM_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 개체명 인식기 시연\n",
        "\n",
        "pororo는 직접 학습하는 것이 아닌 패키지에 들어있는 개체명 인식 모듈을 사용"
      ],
      "metadata": {
        "id": "5R2Q6UeajiBy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from pororo import Pororo\n",
        "\n",
        "ner = Pororo(task=\"ner\", lang=\"kor\")\n"
      ],
      "metadata": {
        "id": "iM19NDM6kSnN",
        "outputId": "6a696f1a-1877-496e-aa3b-e4702da66fa6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df['지원대상'][20000])\n",
        "print(ner(df['지원대상'][20000]))"
      ],
      "metadata": {
        "id": "3n6DcPUdlHCl",
        "outputId": "a640b78f-2509-4d72-9d52-ef514d3a33db",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "○ 도내 시설채소 재배 농업인 및 농업법인\n",
            "\n",
            "기업인||중소기업/소상공인||농축수산인\n",
            "[('○', 'O'), (' ', 'O'), ('도내', 'O'), (' ', 'O'), ('시설채소', 'O'), (' ', 'O'), ('재배', 'O'), (' ', 'O'), ('농업인', 'CIVILIZATION'), (' ', 'O'), ('및', 'O'), (' ', 'O'), ('농업법인', 'O'), (' ', 'O'), ('기업인', 'OCCUPATION'), ('||중소기업', 'CIVILIZATION'), ('/', 'O'), ('소상공인', 'CIVILIZATION'), ('||', 'O'), ('농축수산인', 'CIVILIZATION')]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "DfLvrW5TjDcc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 태그 추출 결과:  \n",
        "\n",
        "  - 일반적인 단어들에 대해 어느정도 인식을 하지만, 본래 목표하던 시/도, 시군구에 대해서도 구분이 명확히 되지않아 사용이 어려움  \n",
        "\n",
        "  ex) 상위 태그인 ‘강원도’와 하위태그인 ‘홍천군’ 모두 LOCATION, 하위태그인 ‘동해시’는 CITY로 나오는 상황\n",
        "    \n",
        "    [('강원도', 'LOCATION'), ('강원', 'LOCATION'), ('동해시', 'CITY'),\n",
        "     ('홍천군', 'LOCATION'), ('평창군', 'LOCATION'), ('양구군', 'LOCATION'), ('양양군', 'LOCATION')]\n",
        "    \n",
        "\n",
        "\n",
        "#### 문제점:  \n",
        "\n",
        "  - 지역(시/도) 또는 지역(시군구)의 태깅 관련해서라도 사용을 하고자 했으나, 이마저도 상당수의 키워드가 개체명이 통일되지 않아 fine tunning에 시간이 많이 소모됨.\n",
        "  - 데이터셋의 도메인에 특화된 개체명인식이 되지 않아 다른 태그들에 있어서도 큰 효과가 없음\n",
        "\n",
        "<br>\n",
        "\n"
      ],
      "metadata": {
        "id": "s4HLJ1mEjRbT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "\n",
        "<br>\n",
        "\n",
        "### 3) KoalaNLP 패키지의 API를 이용.\n",
        "\n",
        "* 선정 이유:  \n",
        "    - pororo와 같이 개체명 인식 기능을 활용할 수 있는 패키지이며, API 사용권한을 받아 개체명 사전 등록이 가능할 것으로 판단되어, 이를 최대한 활용해 직접 개체명 학습하는 것을 줄이기 위함.\n",
        "\n",
        "#### 가설) \n",
        "**패키지 내 사전 등록을 통해 개체명 필터링 후 태깅 정보를 가져올 수 있을 것이다.**\n",
        "\n",
        "* 방법: \n",
        "  - 2) pororo와 동일하게 자체 개체명 인식 기능을 활용, 사용자 사전 등록을 통해 개체명 추가를 시도해보고 개체명 필터링을 하여 태그 추출을 진행\n",
        "\n",
        "* 이슈 및 특이사항:  \n",
        "  - pororo와 마찬가지로 지역명이 포함된 기관명을 분해하여 지역명으로 인식함.\n",
        "  - 정책이라는 특수한 도메인에 사용되는 말뭉치가 아닌, 일상 대화에서 사용되는 말뭉치임.\n",
        "\n",
        "<br>  \n"
      ],
      "metadata": {
        "id": "S9D70qWSppTi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 태그 추출 결과:  \n",
        "\n",
        "  - pororo 패키지 사용시와 같은 성능을 보임\n",
        "\n",
        "\n",
        "#### 문제점:  \n",
        "\n",
        "  - pororo 패키지 사용시와 같은 문제를 보임 (ex: 시/도(LOCA)와 기관(ORGANIZATION)의 구분을 못하는 등)\n",
        "\n",
        "\n",
        "<br>"
      ],
      "metadata": {
        "id": "jaYvahBHL0Rt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "\n",
        "<br>\n",
        "\n",
        "## 2. 직접 학습시킨 개체명 인식기 + 신경망 모델학습 통한 문맥에 따른 태그 추출\n",
        "\n",
        "Text 내 개체명 인식을 통해 태그 추출 후 딥러닝 모델 적용을 통해 태그 추출기능 강화"
      ],
      "metadata": {
        "id": "ga2UVWCTxtUk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1) 개체명 인식기 output + Bert를 통한 추출\n",
        "\n",
        "* 선정 이유:  \n",
        "    - 개체명 인식을 통해 나온 output 값은 O와 태그(ex:A10-01)값으로 이루어져 있는데, Bert 모델의 양방향 인코더로 문맥(순서)를 잘 파악할 수 있고, 위치 임베딩을 통해 문맥 내 태그의 위치를 고려한 최종 태그 예측을 학습하기 위함.\n",
        "\n",
        "#### 가설) \n",
        "**개체명 인식의 결과값에 대해 Bert 모델로 학습시킬 경우, 문맥적, 위치적 특징을 잘 학습할 수 있을 것이다.**\n",
        "\n",
        "* 방법:  \n",
        "  - 개체명 인식기를 이용해 데이터 추출(ex: [O,O,O,'A10-01',O])  \n",
        "  - For문 및 if문 등 자체 필터링을 거쳐 샘플 별 최대한 자체 label값(태그) 생성  \n",
        "  - Bert 모델 작성 및 학습진행\n",
        "\n",
        "* 이슈 및 특이사항:  \n",
        "  - 학습이 전혀 이루어지지 않는 정도의 성능을 보임 (val_accuracy: 0.010 이하)    \n",
        "    ㄴ> input 시 태그값이 '[UNK]'으로 처리되는 것을 방지하기 위해 0과 기타 숫자로 변환할 때, BertTokenizer에서 갖고있는 사전의 값이 적용되어 학습이 잘 안된 것으로 사료됨.  \n",
        "\n",
        "    ㄴ> 위의 이유에 더해, '문맥'을 파악하기에 적은 단어 종류(O와 태그) 및 길이로 인해 학습이 안됨.\n",
        "\n",
        "<br>  "
      ],
      "metadata": {
        "id": "jX8XlueOM54e"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 코드 구현"
      ],
      "metadata": {
        "id": "5RBAFGOz7A-e"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 개체명 인식기 결과값을 수치화"
      ],
      "metadata": {
        "id": "0cak6M627Der"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def apply_tags1(test_sentence):\n",
        "  \n",
        "  # 문장 토크나이징 진행 및 어레이화\n",
        "  tokenized_sentence = np.array([tokenizer.encode(test_sentence, max_length=MAX_LEN, truncation=True, padding='max_length')])\n",
        "\n",
        "  # \n",
        "  tokenized_mask = np.array([[int(x!=1) for x in tokenized_sentence[0].tolist()]])\n",
        "  ans = nr_model1.predict([tokenized_sentence, tokenized_mask])\n",
        "  ans = np.argmax(ans, axis=2)\n",
        "\n",
        "  tokens = tokenizer.convert_ids_to_tokens(tokenized_sentence[0])\n",
        "  new_tokens, new_labels = [], []\n",
        "  for token, label_idx in zip(tokens, ans[0]):\n",
        "    if (token=='[CLS]'):\n",
        "      new_tokens.append(token)\n",
        "      new_labels.append(label_idx)\n",
        "    elif (token=='[SEP]'):\n",
        "      new_tokens.append(token)\n",
        "      new_labels.append(label_idx)\n",
        "    elif (token=='[PAD]'):\n",
        "      new_tokens.append(token)\n",
        "      new_labels.append(label_idx)\n",
        "    elif (token != '[CLS]' or token != '[SEP]'):\n",
        "      new_tokens.append(token)\n",
        "      new_labels.append(label_idx)\n",
        "  \n",
        "  return new_labels"
      ],
      "metadata": {
        "id": "O4yMGNfnXA7b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 반환된 index가 의미하는 태그 파악하기\n",
        "print(index_to_ner)\n",
        "\n",
        "# 수치화된 개체명 인식기 사용해보기\n",
        "print('개체명인식기 수치화 결과값:',apply_tags1('인터넷 또는 직접 방문해서 신청 가능'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "55a763f2-f100-4fd9-dbfb-9e249fae433f",
        "id": "oEWb9ZWAXA7d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{0: 'OO', 1: '[SEP]', 2: 'A10-02', 3: 'A10-00', 4: 'A10-01', 5: '[CLS]', 6: '[PAD]'}\n",
            "개체명인식기 수치화 결과값: [0, 4, 4, 4, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(data)\n",
        "print(np.array(x_label))"
      ],
      "metadata": {
        "id": "WXbo4M7xSM7L",
        "outputId": "20cb8791-ab26-40dd-f948-a8a40cbe8aef",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 1]\n",
            " [0 0 0 ... 0 0 1]\n",
            " ...\n",
            " [0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]\n",
            " [0 2 2 ... 0 0 0]]\n",
            "[2 4 2 ... 2 2 2]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 학습, 검증 셋 분리\n",
        "train,  val = train_test_split(data, test_size = 0.2, random_state = 2)\n",
        "val,  test = train_test_split(val, test_size = 0.2, random_state = 2)\n",
        "\n",
        "train_l,  val_l = train_test_split(x_label, test_size = 0.2, random_state = 2)\n",
        "val_l,  test_l = train_test_split(val_l, test_size = 0.2, random_state = 2)"
      ],
      "metadata": {
        "id": "IjAqWfJ0E8SG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train = np.array(train, dtype='float32')\n",
        "val = np.array(val, dtype='float32')\n",
        "train_l = np.array(train_l, dtype='float32')\n",
        "val_l = np.array(val_l, dtype='float32')"
      ],
      "metadata": {
        "id": "MWWZgndqVnpH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Bert 학습 모델 설계 및 학습 진행"
      ],
      "metadata": {
        "id": "AztI2likaAyR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TFBertModel, BertConfig\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.layers import Dropout\n",
        "\n",
        "SEQ_LEN = MAX_LEN\n",
        "config = BertConfig.from_pretrained('bert-base-multilingual-uncased')\n",
        "config.output_hidden_states = False\n",
        "\n",
        "model = TFBertModel.from_pretrained(\"bert-base-multilingual-cased\", from_pt=True, num_labels=3, output_attentions = False,\n",
        "  output_hidden_states = False)\n",
        "\n",
        "\n",
        "bert = model.layers[0] \n",
        "input_ids = tf.keras.Input(shape = (40, ), name = 'input_ids', dtype = 'int32')  \n",
        "inputs = {'input':input_ids}        # 여러 인풋을 이용시 \n",
        "bert_model = bert(input_ids)[1]       \n",
        "dropout = Dropout(config.hidden_dropout_prob, name = 'pooled_output') \n",
        "pooled_output = dropout(bert_model, training=False) \n",
        "outputs = tf.keras.layers.Dense(3, activation = 'softmax', kernel_initializer = tf.keras.initializers.TruncatedNormal(stddev=config.initializer_range), name = 'output')(pooled_output)\n",
        "model_final = tf.keras.Model(inputs = inputs, outputs = outputs, name = 'Bert_Classifier')\n",
        "model_final.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bf62b17b-d448-4ee6-b76c-0955b5942c17",
        "id": "NNf5xZxAW69F"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the PyTorch model were not used when initializing the TF 2.0 model TFBertModel: ['cls.predictions.transform.dense.bias', 'cls.predictions.bias', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias']\n",
            "- This IS expected if you are initializing TFBertModel from a PyTorch model trained on another task or with another architecture (e.g. initializing a TFBertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing TFBertModel from a PyTorch model that you expect to be exactly identical (e.g. initializing a TFBertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "All the weights of TFBertModel were initialized from the PyTorch model.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFBertModel for predictions without further training.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"Bert_Classifier\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_ids (InputLayer)      [(None, 40)]              0         \n",
            "                                                                 \n",
            " bert (TFBertMainLayer)      TFBaseModelOutputWithPoo  177853440 \n",
            "                             lingAndCrossAttentions(l            \n",
            "                             ast_hidden_state=(None,             \n",
            "                             40, 768),                           \n",
            "                              pooler_output=(None, 76            \n",
            "                             8),                                 \n",
            "                              past_key_values=None, h            \n",
            "                             idden_states=None, atten            \n",
            "                             tions=None, cross_attent            \n",
            "                             ions=None)                          \n",
            "                                                                 \n",
            " pooled_output (Dropout)     (None, 768)               0         \n",
            "                                                                 \n",
            " output (Dense)              (None, 3)                 2307      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 177,855,747\n",
            "Trainable params: 177,855,747\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_final.compile(optimizer=tf.keras.optimizers.Adam(lr=0.00002, decay = 0.00001), loss='sparse_categorical_crossentropy',\n",
        "    metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "7aEOxn_uDo2o"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "early = tf.keras.callbacks.EarlyStopping(monitor = 'val_loss', patience=3, restore_best_weights= True)\n",
        "\n",
        "\n",
        "model_final.fit(train,train_l, validation_data = (val,val_l), batch_size = 64, epochs=3, callbacks=[early])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7fSiTECODx45",
        "outputId": "a9a7e0d3-3601-493f-d774-7451f314c872"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/3\n",
            "22/22 [==============================] - 28s 635ms/step - loss: nan - accuracy: 0.0000e+00 - val_loss: nan - val_accuracy: 0.0000e+00\n",
            "Epoch 2/3\n",
            "22/22 [==============================] - 10s 475ms/step - loss: nan - accuracy: 0.0000e+00 - val_loss: nan - val_accuracy: 0.0000e+00\n",
            "Epoch 3/3\n",
            "22/22 [==============================] - 11s 490ms/step - loss: nan - accuracy: 0.0000e+00 - val_loss: nan - val_accuracy: 0.0000e+00\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f80a8e0ca10>"
            ]
          },
          "metadata": {},
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 모델링 및 태그 추출 결과:  \n",
        "\n",
        "* 모델의 학습이 아예 되지 않는다.\n",
        "\n",
        "\n",
        "#### 문제점\n",
        "\n",
        "  - 모델의 val_loss가 확인이 되지 않고, val_accuracy가 0에 가까운 숫자로 확인이 된다. 기울기 소실이 될만한 활성함수가 없는 상황이기에 데이터 상의 문제로 파악됨  \n",
        "  ㄴ> 예상되는 원인:\n",
        "    - 데이터의 종류가 0, 4, 2로 매우 적음\n",
        "    - 데이터 샘플의 종류가 적은 와중에 길이도 짧음\n",
        "    - 데이터를 수치화 했는데, 버트 토크나이저 자체의 사전에 등록된 다른 단어로 인지 되어 잘못된 문맥 파악이 됐을 수 있음\n",
        "\n",
        "#### 개선방안  \n",
        "  - Bert 모델을 이용하고자 할 경우, 수치화된 데이터가 아닌 한글이 기재된 데이터에서 중요 키워드 부분만 동일한 어떤 태그관련 단어로 변경해 빈도수(중요도)를 높이며 문맥과 중요 키워드를 캐치하여 태그를 추출하도록 할 수 있을 것이다.\n",
        "\n",
        "<br>\n",
        "\n",
        "<br>"
      ],
      "metadata": {
        "id": "a6HoOP5WQ_oI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "\n",
        "<br>\n",
        "\n",
        "### 2) 개체명 인식기 output + Convolution layer(CNN)를 통한 추출\n",
        "\n",
        "* 선정 이유:  \n",
        "    - Conv layer의 filter를 통해 개체명 인식 결과값 내 태그와 그 외 요소의 특징을 추출하고, 이에 따라 label을 예측하는 모델을 학습하기 위함.\n",
        "\n",
        "#### 가설) \n",
        "**개체명 인식의 결과값에 대해 CNN 모델로 학습시킬 경우, 필터를 통한 위치별 가중치를 이용해 데이터의 특징을 잘 학습해 키워드 분류를 진행할 수 있을 것이다.**\n",
        "\n",
        "* 방법:  \n",
        "  - 개체명 인식기를 이용해 데이터 추출(ex: [O,O,O,'A10-01',O])  \n",
        "  - For문 및 if문 등 자체 필터링을 거쳐 샘플 별 최대한 자체 label값(태그) 생성 \n",
        "  - CNN 모델 작성 및 학습진행  \n",
        "\n",
        "* 이슈 및 특이사항:   \n",
        "  - 마찬가지로 학습이 제대로 이뤄지지 않음  \n",
        "    ㄴ> Filter를 사용하기에 데이터의 길이가 너무 짧고, 데이터 종류가 너무 적은 것도 원인으로 추정됨.\n",
        "\n",
        "<br>  \n"
      ],
      "metadata": {
        "id": "O4Jqsnz4pv4a"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 코드 구현"
      ],
      "metadata": {
        "id": "DuPlFTjLa3gE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### CNN (Conv1D) 모델 작성 및 학습 진행"
      ],
      "metadata": {
        "id": "mG05m_FbbEkS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Conv1D, MaxPooling1D, Flatten, Dense\n",
        "\n",
        "inputs = tf.keras.Input(shape = (40 ,1))\n",
        "x =  tf.keras.layers.Conv1D(filters=5, kernel_size=2, activation = 'relu')(inputs)\n",
        "x = tf.keras.layers.MaxPooling1D(2)(x)\n",
        "x = tf.keras.layers.Flatten()(x)\n",
        "x = tf.keras.layers.Dense(16, activation = 'relu')(x)\n",
        "outputs = tf.keras.layers.Dense(3, activation = 'softmax')(x)\n",
        "model_cnn = tf.keras.Model(inputs = inputs, outputs =outputs)\n"
      ],
      "metadata": {
        "id": "L34a9gjhP-IA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_cnn.compile(optimizer=tf.keras.optimizers.Adam(lr=0.00002), loss='sparse_categorical_crossentropy',\n",
        "    metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "nvVkMXAsaVsn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_cnn.fit(train, train_l, epochs=10, validation_data = (val,val_l))"
      ],
      "metadata": {
        "id": "x0Pdd5dLaZxY",
        "outputId": "0725b9b9-ff4a-4f80-acc2-2545ccefbf2c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "44/44 [==============================] - 1s 7ms/step - loss: nan - accuracy: 0.0000e+00 - val_loss: nan - val_accuracy: 0.0000e+00\n",
            "Epoch 2/10\n",
            "44/44 [==============================] - 0s 3ms/step - loss: nan - accuracy: 0.0000e+00 - val_loss: nan - val_accuracy: 0.0000e+00\n",
            "Epoch 3/10\n",
            "44/44 [==============================] - 0s 4ms/step - loss: nan - accuracy: 0.0000e+00 - val_loss: nan - val_accuracy: 0.0000e+00\n",
            "Epoch 4/10\n",
            "44/44 [==============================] - 0s 3ms/step - loss: nan - accuracy: 0.0000e+00 - val_loss: nan - val_accuracy: 0.0000e+00\n",
            "Epoch 5/10\n",
            "44/44 [==============================] - 0s 4ms/step - loss: nan - accuracy: 0.0000e+00 - val_loss: nan - val_accuracy: 0.0000e+00\n",
            "Epoch 6/10\n",
            "44/44 [==============================] - 0s 4ms/step - loss: nan - accuracy: 0.0000e+00 - val_loss: nan - val_accuracy: 0.0000e+00\n",
            "Epoch 7/10\n",
            "44/44 [==============================] - 0s 4ms/step - loss: nan - accuracy: 0.0000e+00 - val_loss: nan - val_accuracy: 0.0000e+00\n",
            "Epoch 8/10\n",
            "44/44 [==============================] - 0s 3ms/step - loss: nan - accuracy: 0.0000e+00 - val_loss: nan - val_accuracy: 0.0000e+00\n",
            "Epoch 9/10\n",
            "44/44 [==============================] - 0s 4ms/step - loss: nan - accuracy: 0.0000e+00 - val_loss: nan - val_accuracy: 0.0000e+00\n",
            "Epoch 10/10\n",
            "44/44 [==============================] - 0s 4ms/step - loss: nan - accuracy: 0.0000e+00 - val_loss: nan - val_accuracy: 0.0000e+00\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f809c0f21d0>"
            ]
          },
          "metadata": {},
          "execution_count": 146
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 모델링 및 태그 추출 결과:  \n",
        "\n",
        "* 기존 버트 모델의 학습 방법과 같이 아예 되지 않는다.\n",
        "\n",
        "\n",
        "#### 문제점\n",
        "\n",
        "  - 모델의 val_loss가 확인이 되지 않고, val_accuracy가 0에 가까운 숫자로 확인이 된다. 기울기 소실이 될만한 활성함수가 없는 상황이기에 데이터 상의 문제로 파악됨  \n",
        "  ㄴ> 예상되는 원인:\n",
        "    - 데이터의 종류가 0, 4, 2로 매우 적음\n",
        "    - 데이터 샘플의 종류가 적은 와중에 길이도 짧음\n",
        "    - 위 두가지 이유로 인해 filter를 통한 특성 추출 자체가 제대로 이뤄지지 않음.\n",
        "\n",
        "#### 개선방안  \n",
        "  - conv layer를 활용할 경우, 자체 학습시킨 개체명인식기를 통해 태그 추출 시, 태그용 키워드만 변형하고 나머지는 그냥 둠으로써 filter가 각 데이터들의 특징을 추출할 수 있도록 한다.\n",
        "\n",
        "<br>"
      ],
      "metadata": {
        "id": "1CqIgXcTbSck"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "\n",
        "<br>\n",
        "\n",
        "### 3) 개체명 인식기 output 수정 + Bert를 통한 추출 `=> 추후 진행할 방향`\n",
        "\n",
        "* 선정 이유:  \n",
        "    - 개체명 인식 결과값에서 태그 추출을 위한 키워드만 특정 문자로 변환하고, 나머지 요소는 그대로 두어 키워드의 빈도수를 높이며, 문맥과 해당 키워드의 위치를 이용해 키워드를 예측하는 모델을 학습하기 위함.\n",
        "\n",
        "#### 가설) \n",
        "**개체명 인식기의 결과값을 문맥 데이터 내 키워드들만 특정 문자로 변환하도록 하면, 데이터의 단조로움 및 길이 문제를 해결하여 문맥정보 및 위치정보를 활용해 언어모델에서의 학습이 잘 될 것이다.**\n",
        "\n",
        "* 방법:  \n",
        "  - 개체명 인식기를 이용해 키워드 데이터를 변형 추출 (신청절차 특성: 방문 -> 오프라인)  \n",
        "ex: ['사무실 방문 신청']> [사무실 오프라인 오프라인 신청)  \n",
        "  - For문 및 if문 등 자체 필터링을 거쳐 샘플 별 최대한 자체 label값(태그) 생성   \n",
        "  - Bert 모델 작성 및 학습진행  \n",
        "\n",
        "* 이슈 및 특이사항:   \n",
        "  - 약 80~85%의 성능을 보임.  \n",
        "    ㄴ> 키워드 추출을 정교하게 한 뒤 개체명인식기 + Bert 이용 추출하면 충분히 90% 이상 성능을 복잡한 특성 내에서도 할 수 있을 것으로 예상됨.\n",
        "\n",
        "<br>  \n"
      ],
      "metadata": {
        "id": "5Na97p6Mpxdu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 코드 구현"
      ],
      "metadata": {
        "id": "ynKes_Pi2loF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 모델을 이용해 토큰을 그대로 살리고, 키워드 토큰만을 한글태그로 변형하는 함수\n",
        "\n",
        "def apply_tags2(test_sentence):  \n",
        "\n",
        "  tokenized_sentence = np.array([tokenizer.encode(test_sentence, max_length=MAX_LEN, truncation=True, padding='max_length')])\n",
        "  tokenized_mask = np.array([[int(x!=1) for x in tokenized_sentence[0].tolist()]])\n",
        "  ans = nr_model1.predict([tokenized_sentence, tokenized_mask])\n",
        "  ans = np.argmax(ans, axis=2)\n",
        "\n",
        "  tokens = tokenizer.convert_ids_to_tokens(tokenized_sentence[0])\n",
        "  new_tokens, new_labels = [], []\n",
        "  for token, label_idx in zip(tokens, ans[0]):\n",
        "    new_labels.append(label_idx)\n",
        "    # if (token != '[CLS]' or token != '[SEP]' or ['PAD']):\n",
        "    if label_idx == tag2idx['A10-01']:\n",
        "      new_tokens.extend(tokenizer.tokenize(str('온라인')))\n",
        "    elif label_idx == tag2idx['A10-02']:\n",
        "      new_tokens.extend(tokenizer.tokenize(str('오프라인')))\n",
        "    elif label_idx == tag2idx['A10-00']:\n",
        "      new_tokens.extend(tokenizer.tokenize(str('복합')))\n",
        "    elif token == '[PAD]':\n",
        "      pass\n",
        "    else:\n",
        "      new_tokens.append(token)\n",
        "  \n",
        "  return new_tokens"
      ],
      "metadata": {
        "id": "1akChPfOdlkK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 변경된 개체명 인식기 시연\n",
        "print(df2['신청절차'][4])\n",
        "print(apply_tags2(df2['신청절차'][4]))"
      ],
      "metadata": {
        "id": "eObnPkh93zsm",
        "outputId": "e8151497-99fd-47d8-927a-985f8d525f11",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "해당 지자체(시군구청)에 방문하여 신청\n",
            "['[CLS]', '해당', '지', '##자', '##체', '(', '시', '##군', '##구', '##청', ')', '에', '오', '##프', '##라', '##인', '오', '##프', '##라', '##인', '##하여', '신', '##청', '[SEP]']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "early = tf.keras.callbacks.EarlyStopping(monitor = 'val_loss',patience = 3)\n",
        "\n",
        "model_final.fit(tr_inputs2, tr_tags2, validation_data = (val_inputs2, val_tags2), epochs = 10, batch_size = 32, callbacks = [early])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WAS8icO_sWui",
        "outputId": "bdeac727-3fe0-4f8c-9d3b-9f8bce69228b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "125/125 [==============================] - 47s 380ms/step - loss: 0.4644 - accuracy: 0.8602 - val_loss: 0.4161 - val_accuracy: 0.8730\n",
            "Epoch 2/10\n",
            "125/125 [==============================] - 45s 360ms/step - loss: 0.3764 - accuracy: 0.8885 - val_loss: 0.3256 - val_accuracy: 0.9110\n",
            "Epoch 3/10\n",
            "125/125 [==============================] - 45s 360ms/step - loss: 0.3087 - accuracy: 0.9118 - val_loss: 0.3339 - val_accuracy: 0.8890\n",
            "Epoch 4/10\n",
            "125/125 [==============================] - 45s 358ms/step - loss: 0.2665 - accuracy: 0.9240 - val_loss: 0.3090 - val_accuracy: 0.9080\n",
            "Epoch 5/10\n",
            "125/125 [==============================] - 44s 356ms/step - loss: 0.2195 - accuracy: 0.9438 - val_loss: 0.2770 - val_accuracy: 0.9190\n",
            "Epoch 6/10\n",
            "125/125 [==============================] - 45s 357ms/step - loss: 0.2081 - accuracy: 0.9460 - val_loss: 0.2666 - val_accuracy: 0.9330\n",
            "Epoch 7/10\n",
            "125/125 [==============================] - 45s 356ms/step - loss: 0.1945 - accuracy: 0.9480 - val_loss: 0.2662 - val_accuracy: 0.9220\n",
            "Epoch 8/10\n",
            "125/125 [==============================] - 44s 356ms/step - loss: 0.1894 - accuracy: 0.9500 - val_loss: 0.2778 - val_accuracy: 0.9230\n",
            "Epoch 9/10\n",
            "125/125 [==============================] - 45s 356ms/step - loss: 0.1679 - accuracy: 0.9585 - val_loss: 0.2923 - val_accuracy: 0.9260\n",
            "Epoch 10/10\n",
            "125/125 [==============================] - 44s 356ms/step - loss: 0.1570 - accuracy: 0.9605 - val_loss: 0.3040 - val_accuracy: 0.9140\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fd94fe18b50>"
            ]
          },
          "metadata": {},
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 모델링 및 태그 추출 결과:  `해당 결과를 바탕으로 2차 모델링 진행 예정`\n",
        " - 태그화 및 수치화된 결과값(ex: O,O,O,A10-10,O,O)을 그대로 신경망 모델을 넣었을때보다 성능이 월등히 좋음  \n",
        " ㄴ> 예상되는 원인:  \n",
        "  - Bert tokenizer가 사전 학습해놓은 words를 사용해 문맥 학습을 용이하게 함.\n",
        "  - 특정 키워드들이 모두 희소한 word가 아니라 동일하게 '오프라인', '온라인'으로 통일되어 중요키워드로 작용하고, 그 위치를 잘 파악하게 되어 전체 문맥에 따른 태그 추출이 용이\n",
        "\n",
        "#### 문제점:  \n",
        "  - 여전히 단순한 태그 추출이기 때문에 추가적으로 복잡한 태그 추출시 다양한 문제를 직면할 것으로 예상\n",
        "\n",
        "#### 개선점:\n",
        "  - 정교한 태깅이 되어야 정확한 태그 추출을 할 수 있기 때문에, 필터링에서 좀 더 다양한 패턴을 분석한다.\n",
        "  - 개체명 인식기의 결과값에서 키워드 변환시 글자 수 만큼 반환되는 것을 추후 보완해서 word 1개당 1개의 태그 키워드로 변환토록 할 예정  \n",
        "  ex) \n",
        "    - 기존: 방문 접수 가능 -> 오프라인 오프라인 접수 가능  \n",
        "    - 추후: 방문 접수 가능 -> 오프라인 접수 가능\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "ThAo7qu-6Mph"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "\n",
        "<br>\n",
        "\n",
        "## 3. 키워드 변환 후 KoBERT - pytorch\n",
        "자세한 코드는  tagging_model을 참고\n",
        "\n",
        "- **데이터**\n",
        "  - policy_label = label이 있는 df. \n",
        "  ```\n",
        "  policy_label.columns\n",
        "  ['정책ID', '정책서비스ID', '서비스명', '소관기관', '소관기관유형.1', '생애주기', '정책신청절차', '선정기준',\n",
        "        '정책지원유형', '서비스목적', '지원내용', '지원대상', '대상연령시작', '대상연령끝', '시도', '시군구',\n",
        "        '중위소득', '관심상황특성', '대상특성', '대상특성상세', 'MB_15', '소관기관유형', '지원유형', '지원유형상세',\n",
        "        '신청절차', '성별', '결혼', '학력', '가구원', '자녀', '자녀상세', '직장', '직장상세']\n",
        "  ```\n",
        "\n",
        "  - policy = 정책별 공고문 text를 feature로 나눈 df.\n",
        "  ```\n",
        "  policy.columns\n",
        "  ['정책ID', '정책서비스ID', '서비스명', '소관기관', '소관기관유형.1', '생애주기', '신청절차', '선정기준',\n",
        "       '지원유형', '서비스목적', '지원내용', '지원대상']\n",
        "  ```\n",
        "\n",
        "\n",
        "- **방법**\n",
        "  1. 추출하고자 하는 태깅과 관련된 키워드를 태깅단어로 바꿈 ( tagging_model.ipynb > 데이터 준비 모델 > def pytorch_data() 참고 )\n",
        "    ```\n",
        "    예시1) '임산부를 위한 정책입니다.' => '여성을 위한 정책입니다.'\n",
        "    예시2) '군입대를 앞둔 사람을 위한 정책입니다.' => 남성를 앞둔 사람을 위한 정책입니다.'\n",
        "    ```\n",
        "    \n",
        "    정확도를 높일 수 있을 것이라 예상.\n",
        "  </br>\n",
        "  \n",
        "  2. SKTBrain에서 발표한 BERT기반의 KoBERT를 이용하여 학습.\n",
        "\n",
        "    - KoBERT 선정 이유</br>\n",
        "    1) 정책 텍스트의 길이가 길고, 복잡하기 때문에 양방향 학습이 가능해야하며, 데이터가 크기 때문에 병렬처리가 가능한 모델을 선택.</br>\n",
        "    2) 한국어 학습이 진행된 모델이므로 기존 BERT의 사전학습된 bert-base-multilingual-cased 보다 성능이 좋을 것으로 예상.</br>\n",
        "\n",
        "- **학습 결과**</br>\n",
        "학습은 되는 것 같아 보이나, 한계가 존재함.\n",
        "\n",
        "    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)"
      ],
      "metadata": {
        "id": "3IDdhQTOK5Tp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- **한계점 및 보완사항**\n",
        "\n",
        "1. 신청절차, 성별, 자녀, 정책지원유형 등은 라벨에 중복되는 값이 없음. 그러나 그 외의 특성들은 라벨에 중복되는 값들이 있는데, 라벨값이 하나일수도, 열 개일수도 있음.\n",
        "```\n",
        "policy_label['신청절차'] = ['무관', '무관', '여성', '남성'...] \n",
        "policy_label['대상특성'] = ['해당사항없음', '장애인, 다문화가족', '임신부', '독거노인, 기관/시설, 장애인', '해당사항없음'...]\n",
        "```\n",
        "\n",
        "  이 부분에 대해 학습 시, loss를 데이터 갯수에 맞게 그때그때 구해주려고 해보았으나, 결국 최종적으로 test에서 n개의 아웃풋 결과를 설정해줘야함.\n",
        "  ```\n",
        "  out = model(token_ids, valid_length, segment_ids)\n",
        "  label = label[0]\n",
        "  \n",
        "  if ',' in label:          \n",
        "      labels = list(map(int, label.split(',')))\n",
        "      loss = 0\n",
        "      for i in range(len(labels)):\n",
        "        label = labels[i]\n",
        "        # label = tuple(label, )\n",
        "        label = torch.from_numpy(np.array([np.int32(label)])).long().to(device)\n",
        "        loss += loss_fn(out, label)\n",
        "        loss /= len(labels)\n",
        "  else:\n",
        "    label = torch.from_numpy(np.array([np.int32(label)])).long().to(device)\n",
        "    loss = loss_fn(out, label)\n",
        "  loss.backward()\n",
        "  ```\n",
        "\n",
        "</br>\n",
        "\n",
        "2. 학습에 사용된 라벨값은 기존에 for문, if문을 이용해서 키워드를 기준으로 태깅을 진행한 값이다.\n",
        "\n",
        "  즉, 100%의 정확도를 가지는 라벨값이 아니라, 피처마다 최소 40% 최대 98% 사이의 정확도를 가지는 라벨값이기 때문에 애초에 높은 모델 성능을 기대하기 어려움.\n",
        "\n",
        "3. Input의 label을 One-Hot 형태로 변환하지 않았음.\n",
        "4. label값이 여러개인 feature의 경우 어떻게 해야할지 모르겠어서 태깅모델 A을 이용함.</br>\n",
        "    프로젝트를 회고하며 알게된 multi label classification 방법을 공부한다면 해결할 수 있을 것으로 보임. [참고자료](https://m.blog.naver.com/PostView.naver?isHttpsRedirect=true&blogId=chrhdhkd&logNo=221469784081)"
      ],
      "metadata": {
        "id": "nUCIhlEabpLx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "eRyg9PM5NQRh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "\n",
        "<br>\n",
        "\n",
        "<br>\n",
        "\n",
        "\n",
        "# 추천 모델\n"
      ],
      "metadata": {
        "id": "pEvZjfK8sWEB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1. 전체 데이터 이용 추천모델\n",
        "\n",
        "주어진 전체 유저데이터셋과 정책 데이터셋을 이용해 딥러닝 모델 진행하는 방법"
      ],
      "metadata": {
        "id": "7ycsI1KywZx4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1) Wide & Deep  `=> 추후 진행할 방향`\n",
        "\n",
        "* 선정 이유:  \n",
        "  - 도메인 특성상 추천될 아이템의 조건에 유저의 조건이 부합해야 유저 경험을 강화할 수 있는데, Wide & Deep의 wide part에서 memorization을 이용해 반드시 조건을 부합하면서도 deep part의 generalization을 통해 freshness를 높이고자 선정\n",
        "\n",
        "#### 가설)  \n",
        "**Wide part에서 유저의 정보와 정책의 정보가 일치(조건에 부합)하도록 유지하면서 Deep part를 통해 조건 내 다양한 정책을 추천할 수 있을 것이다.**\n",
        "\n",
        "\n",
        "* **방법**:  \n",
        "  a) label: 유저가 선호하는 정책(중복가능 11개)과 유저 태그가 정책데이터 특성과의 일치여부에 따라 1,0으로 부여\n",
        "    - 유저와 정책 데이터셋의 '선호정책'을 One Hot Encoding 하여 정책 데이터가 유저 선호정책(중복 선택 중) 한개라도 포함될 경우\n",
        "    - 유저와 정책 데이터셋의 중요태그(대상특성, 직장, 가구원 등)가 모두 부합할 경우\n",
        "    - 위 두가지 조건(관심정책 & 중요태그) 일치 여부에 따라 라벨 1 또는 0으로 부여  \n",
        "\n",
        "  b) train \n",
        "    - wide part(w/ cross product transformation) & deep part input 진행 및 학습  \n",
        "\n",
        "  c) recommendation\n",
        "    - 특정 유저의 특성과 전체 정책의 특성 조합에 대한 label을 예측\n",
        "    - 가장 높은 점수의 정책 5개 ~10개 추천 진행 \n",
        "\n",
        "* 이슈 및 특이사항:  \n",
        "    - 궁극적으로 전체 고객 * 정책을 모두 매칭해야하므로 학습량이 너무 많아 전체 학습을 할 수 없음  (유저 약 20000개, 정책 약 88000개 > 유저 * 정책 = 8억8천만 샘플) \n",
        "    \n",
        "      ㄴ> 이를 해결하기 위해 대표 샘플링, 클러스터링 샘플링 진행\n",
        "\n"
      ],
      "metadata": {
        "id": "RQgkonHMRNEc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![image.png](data:image/png;base64,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)\n",
        "\n",
        "<br>\n"
      ],
      "metadata": {
        "id": "u18QO--myph2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**적은 특성과 100명의 유저샘플, 1000개의 정책샘플(random)로 학습진행**\n",
        "\n",
        "```python\n",
        "\n",
        "\"\"\"\n",
        "_x: 유저 데이터 특성\n",
        "_y: 정책 데이터 특성\n",
        "두 가지가 모두 1이 되는 경우를 기억하기 위한 crossed_cols 설정\n",
        "\"\"\"\n",
        "# wide part 특성\n",
        "wide_cols = ['성별_x', '자녀_x', 'mb_10+대상특성', 'mb_11+관심상황특성','대상특성', '대상특성상세',\n",
        "       '소관기관유형', '지원유형', '지원유형상세', '신청절차', '성별_y','자녀_y']\n",
        "\n",
        "# wide part의 cross layer 설정\n",
        "crossed_cols = (['성별_x', '성별_y'], ['자녀_x', '자녀_y'], ['mb_10+대상특성','대상특성'])\n",
        "\n",
        "# deep part 특성\n",
        "embedding_cols = ['성별_x', '자녀_x', 'mb_10+대상특성', 'mb_11+관심상황특성','대상특성', '대상특성상세',\n",
        "                  '소관기관유형', '지원유형', '지원유형상세', '신청절차', '성별_y','자녀_y']\n",
        "cont_cols = ['나이','대상연령시작','대상연령끝']\n",
        "```\n",
        "\n",
        "\n",
        "```\n",
        "Epoch 1/10\n",
        "625/625 [==============================] - 8s 7ms/step - loss: 1.1948 - accuracy: 0.6978\n",
        "Epoch 2/10\n",
        "625/625 [==============================] - 3s 6ms/step - loss: 0.5343 - accuracy: 0.7396\n",
        "Epoch 3/10\n",
        "625/625 [==============================] - 3s 5ms/step - loss: 0.5205 - accuracy: 0.7451\n",
        "Epoch 4/10\n",
        "625/625 [==============================] - 3s 5ms/step - loss: 0.5140 - accuracy: 0.7463\n",
        "Epoch 5/10\n",
        "625/625 [==============================] - 3s 6ms/step - loss: 0.5103 - accuracy: 0.7465\n",
        "Epoch 6/10\n",
        "625/625 [==============================] - 3s 5ms/step - loss: 0.5081 - accuracy: 0.7459\n",
        "Epoch 7/10\n",
        "625/625 [==============================] - 3s 5ms/step - loss: 0.5068 - accuracy: 0.7468\n",
        "Epoch 8/10\n",
        "625/625 [==============================] - 3s 5ms/step - loss: 0.5058 - accuracy: 0.7475\n",
        "Epoch 9/10\n",
        "625/625 [==============================] - 3s 5ms/step - loss: 0.5049 - accuracy: 0.7473\n",
        "Epoch 10/10\n",
        "625/625 [==============================] - 3s 5ms/step - loss: 0.5044 - accuracy: 0.7475\n",
        "```\n",
        "```\n",
        "# 실제 label 분포\n",
        "0    13977\n",
        "1     6023\n",
        "Name: label, dtype: int64\n",
        "```\n",
        "\n",
        "```\n",
        "# 예측 결과\n",
        "              precision    recall  f1-score   support\n",
        "\n",
        "       False       0.93      0.76      0.84     17132\n",
        "        True       0.32      0.67      0.43      2868\n",
        "\n",
        "    accuracy                           0.75     20000\n",
        "   macro avg       0.63      0.72      0.64     20000\n",
        "weighted avg       0.84      0.75      0.78     20000\n",
        "\n",
        "```\n",
        "**적은 샘플로 진행해본 결과**  \n",
        "전체 accuracy는 0.74이지만, True의 f1-score가 매우 낮게 나온다.  \n",
        "결론적으로 모델 예측 자체는 True label 예측이 실제보다 매우 적은 편.  \n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "<br>\n",
        "\n",
        "**필요 특성을 추가하고, 14000명의 유저샘플, 1000개의 정책샘플(클러스터링 후 군집 별 표본추출)로 학습진행**\n",
        "\n",
        "```python\n",
        "\n",
        "# wide part 특성\n",
        "wide_cols = [ '성별_x', '학력_x', '직장_x', '결혼_x',\n",
        "              ' 농축수산인', '해당사항없음_x', '시도_x', '한부모가정/조손가정_x', '국가유공자_x', '북한이탈주민_x',\n",
        "              '질병/부상/질환자_x', '장애인_x', '다문화가족_x', '다자녀가정_x', '문화생활 지원_x', '주택-부동산 지원_x', '관심정책없음', '근로자 지원_x',\n",
        "              '의료 지원_x', '보육지원(만0~7세)', '개인금융지원_x', '교육지원(만8~19세)', '성인교육지원_x',\n",
        "              '기업금융지원_x', '취업 지원_x', '창업 지원_x', '시도_y',\n",
        "              '지원유형', '학력_y', '성별_y', '결혼_y', '직장_y','질병/부상/질환자_y', '국가유공자_y',\n",
        "              '한부모가정/조손가정_y', '다자녀가정_y', '해당사항없음_y', '다문화가족_y', '북한이탈주민_y', '농축수산인',\n",
        "              '장애인_y', '개인금융지원_y', '문화생활 지원_y', '취업 지원_y', '창업 지원_y', '교육지원(8~19세)',\n",
        "              '의료 지원_y', '보육지원(0~7세)', '주택-부동산 지원_y', '성인교육지원_y', '기업금융지원_y',\n",
        "              '근로자 지원_y']\n",
        "\n",
        "# deep part 특성\n",
        "crossed_cols = (['성별_x', '성별_y'], ['결혼_x', '결혼_y'])\n",
        "\n",
        "embedding_cols = ['시도_x', '학력_x', '직장_x', '결혼_x',\n",
        "              ' 농축수산인', '해당사항없음_x', '한부모가정/조손가정_x', '국가유공자_x', '북한이탈주민_x','질병/부상/질환자_x', '장애인_x', '다문화가족_x', '다자녀가정_x',\n",
        "              '문화생활 지원_x', '주택-부동산 지원_x', '관심정책없음', '근로자 지원_x','의료 지원_x', '보육지원(만0~7세)', '개인금융지원_x', '교육지원(만8~19세)', '성인교육지원_x','기업금융지원_x', '취업 지원_x', '창업 지원_x',\n",
        "              '시도_y', '소관기관유형', '지원유형', '학력_y', '성별_y', '결혼_y', '자녀', '직장_y',\n",
        "              '농축수산인', '해당사항없음_y','한부모가정/조손가정_y', '국가유공자_y', '북한이탈주민_y', '질병/부상/질환자_y', '장애인_y', '다문화가족_y', '다자녀가정_y', \n",
        "              '개인금융지원_y', '문화생활 지원_y', '취업 지원_y', '창업 지원_y', '교육지원(8~19세)','의료 지원_y', '보육지원(0~7세)', '주택-부동산 지원_y', '성인교육지원_y', '기업금융지원_y','근로자 지원_y']\n",
        "cont_cols = ['나이','max_income_x','min_income_x','대상연령시작','대상연령끝','max_income_y', 'min_income_y'] \n",
        "```\n",
        "```\n",
        "Epoch 1/10\n",
        "18750/18750 [==============================] - 184s 10ms/step - loss: 0.4793 - accuracy: 0.8713 - val_loss: 0.4127 - val_accuracy: 0.8731\n",
        "Epoch 2/10\n",
        "18750/18750 [==============================] - 182s 10ms/step - loss: 0.3896 - accuracy: 0.8730 - val_loss: 0.3740 - val_accuracy: 0.8736\n",
        "Epoch 3/10\n",
        "18750/18750 [==============================] - 183s 10ms/step - loss: 0.3641 - accuracy: 0.8730 - val_loss: 0.3583 - val_accuracy: 0.8727\n",
        "Epoch 4/10\n",
        "18750/18750 [==============================] - 182s 10ms/step - loss: 0.3516 - accuracy: 0.8731 - val_loss: 0.3478 - val_accuracy: 0.8741\n",
        "Epoch 5/10\n",
        "18750/18750 [==============================] - 184s 10ms/step - loss: 0.3441 - accuracy: 0.8733 - val_loss: 0.3422 - val_accuracy: 0.8732\n",
        "Epoch 6/10\n",
        "18750/18750 [==============================] - 183s 10ms/step - loss: 0.3391 - accuracy: 0.8734 - val_loss: 0.3372 - val_accuracy: 0.8718\n",
        "Epoch 7/10\n",
        "18750/18750 [==============================] - 186s 10ms/step - loss: 0.3356 - accuracy: 0.8732 - val_loss: 0.3348 - val_accuracy: 0.8745\n",
        "Epoch 8/10\n",
        "18750/18750 [==============================] - 188s 10ms/step - loss: 0.3329 - accuracy: 0.8734 - val_loss: 0.3321 - val_accuracy: 0.8723\n",
        "Epoch 9/10\n",
        "18750/18750 [==============================] - 189s 10ms/step - loss: 0.3308 - accuracy: 0.8734 - val_loss: 0.3307 - val_accuracy: 0.8719\n",
        "Epoch 10/10\n",
        "18750/18750 [==============================] - 193s 10ms/step - loss: 0.3291 - accuracy: 0.8734 - val_loss: 0.3289 - val_accuracy: 0.8740\n",
        "18750/18750 [==============================] - 184s 10ms/step - loss: 0.3289 - \n",
        "\n",
        "```\n",
        "```\n",
        "# 실제 라벨 분포\n",
        "1    643834\n",
        "0    642650\n",
        "Name: label, dtype: int64\n",
        "```\n",
        "```\n",
        "# accuracy 측정 결과\n",
        "\n",
        "accuracy: 0.8740\n",
        "wide and deep model accuracy: 0.873960018157959\n",
        "              precision    recall  f1-score   support\n",
        "\n",
        "           0       0.89      0.88      0.89    342435\n",
        "           1       0.85      0.86      0.85    257565\n",
        "\n",
        "    accuracy                           0.87    600000\n",
        "   macro avg       0.87      0.87      0.87    600000\n",
        "weighted avg       0.87      0.87      0.87    600000\n",
        "```\n",
        "\n",
        "* 평가결과:  \n",
        "  * 데이터셋 양과 feature 수를 증가시킨 결과, 테스트 데이터셋 기준 f1 score 85%까지의 큰 폭으로 상승함.\n",
        "  * 기타 파라미터 조정 및 feature 조율을 통해 90% 이상 진행 가능할 것으로 보임\n",
        "\n",
        "```\n",
        "# model predict 및 임의의 유저 추천 진행 결과\n",
        "  \n",
        "\n",
        "유저 테이블 100번 유저 정보\n",
        "user1.iloc[100]\n",
        "\n",
        "num                       4969\n",
        "성별                          여성\n",
        "나이                          54\n",
        "시도                       서울특별시\n",
        "시군구                        강서구\n",
        "학력                      고등학교졸업\n",
        "직장                   소상공인,중소기업\n",
        "가구원             무주택 세대주,주택 세대주\n",
        "결혼                          기혼\n",
        "자녀                          없음\n",
        "자녀상세                    해당사항없음\n",
        "자녀수                         no\n",
        "mb_10                     저소득층\n",
        "mb_10+대상특성                저소득층\n",
        "mb_11                     해당없음\n",
        "mb_11+관심상황특성              None\n",
        "mb_12           중위소득 60~80% 사이\n",
        "mb_13                       no\n",
        "mb_14                       no\n",
        "mb_15                주택-부동산 지원\n",
        "Name: 100, dtype: object\n",
        "--------------------------------------------\n",
        "100번 유저 관련 추천 정책\n",
        "run.Recommendation(user1.iloc[100])\n",
        "\n",
        "\n",
        "정책ID\t서비스명\tprob\n",
        "0\t51650\t긴급복지 지원\t0.979127\n",
        "1\t22676\t긴급복지 지원\t0.942127\n",
        "2\t70552\t긴급복지 지원\t0.942127\n",
        "3\t45770\t긴급복지 지원\t0.942127\n",
        "4\t44396\t긴급복지 지원\t0.942127\n",
        "5\t47027\t긴급 복지지원 제도\t0.939212\n",
        "6\t46117\t저소득 가정 청소년 보호 (위문 격려금)\t0.929142\n",
        "7\t24786\t가정위탁보호 지원\t0.924070\n",
        "8\t53930\t소년소녀가정 및 가정위탁가정 아동 수련회비 또는 수학여행비 지원\t0.909780\n",
        "9\t46253\t소년소녀가정 및 가정위탁보호아동지원\t0.909780\n",
        "```\n",
        "* 추천 모듈 관련 평가\n",
        "  * 평균 추천 예상 시간: 23초로 매우 느린 수준\n",
        "  * 유저 조건에 부합하지 않는 정책이 다수\n",
        "    * 유저는 자녀-없음 이지만, 아동-청소년 지원정책이 추천되거나  \n",
        "\n",
        "<br>\n",
        "\n",
        "\n",
        "한계점 및 보완사항\n",
        "1. 학습을 위한 데이터 전처리 과정에서 메모리 부족 문제 발생\n",
        "  *  원인: 너무 많은 샘플 및 특성의 수  \n",
        "    * 보완방법: High cardinality의 특성 중 중요도가 덜한 특성을 제외(ex: 시도, 시군구)  및 샘플 수 줄이는 방법\n",
        "\n",
        "\n",
        "2. 학습을 통한 성능 향상이 90% 이상 진행되지 않음. \n",
        "  * 원인: 특성 간 교차를 진행하는 cross product를 '결혼','성별'과 같이 쉽고 복잡하지 않은 특성들로만 사용,\n",
        "    * 보완방법: 직장, 대상특성과 같은 다소 중요한 정책 조건으로 사용되는 특성을 cross product 진행한다. \n",
        "\n",
        "3. 추천진행 관련 모듈 설계 한계  \n",
        "  1) 추천 시간이 평균 23초로 매우 오래걸림\n",
        "    * 원인: 유저데이터를 입력받고, 추천을 위한 model predict 인풋값으로의 전처리 작업에서 기존 정책 8만8천개의 데이터들과의 합을 만드는 과정에서 시간이 다소 소요됨.\n",
        "      * 해결방안: 유저 관심정책이 있는 경우, 관심정책을 Query로 삼아 Retrieval 모듈을 통해 우선적으로 관심정책에 해당하는 정책을 선별 (관심정책을 선택하지 않은 유저의 경우, 똑같이 해당사항없음의 정책 중에서 선별)\n",
        "\n",
        "  2) 추천 진행 결과, 거의 추천 대상과 정책이 잘 맞지 않음.\n",
        "    * 원인1: 알고리즘 자체에서 제가 실수해서 서비스명이나 정책ID가 잘못기재 되는 등의 휴먼 에러 가능성\n",
        "    * 원인2: 무의미한 정책 데이터셋(88000개 샘플)의 양이 너무 많음.\n",
        "      * 해결방안: 1번과 같이 Query를 통해 먼저 샘플 수를 걸러내는 방법, 또는 Matrix 모델을 바탕으로 1차적으로 정책을 걸러낸 뒤, 와이드앤딥 모델로 최종 높은 score의 정책을 추천하는 방법\n",
        "\n",
        "  3) 기존 훈련 진행시 모든 중복태그를 one-hot 인코딩 하지 않는 경우, training은 진행되지만, 추천을 위한 전처리 과정에서 정확하게 feature 생성이 매우 번거로움\n",
        "\n",
        "    * 모든 샘플로부터 경우의 수를 파악해서 feature로 생성하여 학습 데이터셋과 예측 데이터셋의 feature를 동일하게 생성 가능하지만, 시간효율이 떨어짐\n",
        "      * 해결방안: 필요시 해당 중요한 특성들에 한해서는 one-hot encoding을 통해 get_dummies를 피해준다. -> 단, 공수가 더욱 많이 들어가며, 시간 효율이 떨어질 수도 있다.\n",
        "\n",
        "예시: 직장, 지원유형 feature의 get_dummies\n",
        "\n",
        "```\n",
        "# training data columns \n",
        "\n",
        "직장\n",
        "['직장_x_구직자/실업자', '직장_x_구직자/실업자,근로자/직장인', '직장_x_구직자/실업자,무관', '직장_x_근로자/직장인,무관', '직장_x_근로자/직장인,예비창업자']\n",
        "\n",
        "지원유형\n",
        "['지원유형_이용권,의료지원,봉사/기부,기타', '지원유형_이용권,의료지원,상담/법률지원', '지원유형_이용권,의료지원,상담/법률지원,기타']\n",
        "\n",
        "위와 같이 중복태그를 모두 살린 뒤 그대로 get_dummies하여 학습했을 경우,  \n",
        "model이 인지하고 있는 특성 이름(중복태그의 경우의 수)가 없으면 에러 발생\n",
        "\n",
        "ex) 위 '직장', '지원유형'과 같은 feature에 대해 model이 학습하고, user의 직장 태그가 구직자/실업자,예비창업자 인 경우.\n",
        "\n",
        "```\n",
        "\n",
        "  \n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "bGs__pjxXAFP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "\n",
        "<br>\n",
        "\n",
        "## 2. 데이터 선별 + 추천 진행 모델\n",
        "\n",
        "일차적으로 유사도기반 모델로 선별하여 진행하는 모델"
      ],
      "metadata": {
        "id": "VIEkwV7AlcBB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### (Baseline) 유사도 Matrix 모델:  \n",
        "유저 데이터셋과 정책 데이터셋의 공통된 feature 요소에 대해 행렬곱을 취해 만들어진 최종 유저/정책 matrix에서 해당 유저에게 가장 높은 score를 띄는 정책을 선별하는 Baseline 모델\n",
        "\n"
      ],
      "metadata": {
        "id": "dkohixTsk4Kt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        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      ],
      "metadata": {
        "id": "09w-rlCkyiLG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "\n",
        "<br>\n",
        "\n",
        "### 1) 유사도 모델(Baseline model) + NCF(Neural Collaborative Filtering)\n",
        "\n",
        "* 선정 이유:  \n",
        "  a) 유사도 Matrix 모델 선정:  \n",
        "    - 정책 데이터가 너무 많으므로 basic한 유사도 모델을 통해 고객이 관심을 가질만한 정책을 일차적으로 선별하기 위함.  \n",
        "\n",
        "  b) NCF 모델 선정:  \n",
        "    - 일차적으로 선별된 수량에 대해 GMF part에서 유저id, 정책id의 gerenalized MF를 이용해 유저-정책 간의 연관성을 선형적(단순한 관계)으로 파악하도록 하고, MLP part에서 좀 더 복잡한 관계를 읽어내어 유저-정책 간의 고도화된 관계를 파악해 추천하는 모델을 이용하기 위함. \n",
        "\n",
        "#### 가설)  \n",
        "**각각의 feature에 대한 특징을 담고있는 수치를 통해 Matrix를 만들고, 이를 바탕으로 NCF 모델을 학습하면, 유저-정책의 선형적, 비선형적 관계를 파악해 추천하는 모델이 될 수 있다.**\n",
        "\n",
        "* 방법:  \n",
        "  - GMF part에서 유저데이터와 정책데이터의 feature를 각각 1차원으로 압축해 matrix 상에 유저가 관심이 있는 정책일 경우 label 1을 부여, 아닐 경우 0을 부여한다.\n",
        "  - MLP part에서 유저 feature와 정책 feature를 각각 임베딩 layer로 넣어 concat한 뒤 feed forward를 진행한다. 라벨은 GMF와 같은 원리로 부여해서 학습 진행\n",
        "  - Metric을 hit ratio로 두어 top K를 뽑아 추천을 진행한다. \n",
        "   \n",
        "* 이슈 및 특이사항:  \n",
        "  - GMF 상의 Input값에 기존 NCF 논문 상에서 유저ID와 정책ID만으로 matrix를 만들어 요소 자체가 1차원으로 구성되어 있지만, 본 프로젝트의 데이터를 통해 다양한 feature를 의미가 있는 1차원 데이터로 압축하는 방법을 찾지 못함.  \n",
        "  ㄴ> 기존 GMF의 Input값에 대한 이해도가 부족했던 것으로 확인. 의도대로 진행한다면 NCF와는 다른 모델이 된다.\n",
        "  - 기존 NCF 모델의 GMF를 이용해 모델링을 진행할 경우, 특성이 매우 많아 MLP(DNN) part는 임베딩 레이어 자체가 아주 많아지며, 무겁고 복잡한 모델이 될 수 있음\n",
        "  - 위와 같이 모델링 진행할 경우, 다양한 관계를 파악하긴 좋지만, 유저에게 조건이 부합하지 않는 정책이 추천될 수 있다.\n"
      ],
      "metadata": {
        "id": "GEV--LUDnkBd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "0z8f3b36yTN9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "<br>\n",
        "\n",
        "---\n",
        "\n",
        "<br>"
      ],
      "metadata": {
        "id": "8PVhcvKExODx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "### 2) 유사도 모델(Baseline model) + Feature 요소 벡터화 및 요소 별 대표 샘플 추출 + 딥러닝 모델(딥러닝 진행못함)\n",
        "\n",
        "* 선정 이유:  \n",
        "  - 유사도 모델 이후에도 정책 데이터 수가 너무 많기 때문에 이차적으로 샘플 데이터를 선별하기 위해 정책의 feature 요소들을 모두 벡터화 한 뒤, 해당 벡터화 된 요소들 중 대표가 될 수있는 데이터를 뽑아 대표군들로만 모델을 학습시키기 위함\n",
        "\n",
        "#### 가설)  \n",
        "**Feature 요소를 수치화 했을 때, 대표가 될 수 있는 샘플을 선정할 수 있다.**\n",
        "\n",
        "* 방법:  \n",
        "  - 정책 데이터의 각 feature를 dense vector화를 진행\n",
        "  - 수치화된 데이터를 n개의 계층으로 구분하고, n개의 계층 중 k개를 선발.\n",
        "  - 해당 대표 데이터로 추천 신경망 모델 학습을 진행.\n",
        "\n",
        "* 이슈 및 특이사항:  \n",
        "  - 데이터를 수치화 했을때, 가까운 수치를 가진 유저끼리 비슷한 성향을 가진 유저라고 단정지을 수 있는 근거가 없다."
      ],
      "metadata": {
        "id": "ABUG-glEMjGL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "ddRWbPwbCVA-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "<br>\n",
        "\n",
        "---\n",
        "\n",
        "<br>"
      ],
      "metadata": {
        "id": "CF9EH7hZA87R"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "### 3) 유사도 모델(Baseline model) + 클러스터링을 통한 군집화 후 샘플 추출 + 딥러닝 모델(딥러닝 진행못함)\n",
        "\n",
        "* 선정 이유:  \n",
        "  - 유사도 모델 이후에도 정책 데이터 수가 너무 많기 때문에 이차적으로 샘플 데이터를 선별하기 위해 정책 데이터를 군집화 한 뒤, 해당 군집 내에서의 샘플 n개를 추출해서 학습으로 사용하여 학습 데이터 수를 줄이기 위함.\n",
        "\n",
        "#### 가설)  \n",
        "데이터의 군집화를 진행한 뒤 샘플링을 한다면, 대표 군집 내의 데이터를 일정 수량 추출해 전체 학습해야 할 정책 데이터 수를 줄일 수 있을 것이다.\n",
        "\n",
        "* 방법:  \n",
        "  - 정책 데이터의 각 numerical / categorical feature를 구분한다.\n",
        "  - k-prototypes 클러스터링을 진행하여 numerical, categorical data 모두에 대한 군집화를 진행\n",
        "  - 군집을 대표군으로 보고 군집별 n개씩의 샘플을 추출해 추천시스템 신경망 모델의 인풋으로 이용한다.\n",
        "  -(optional) Umap 모듈 임베딩을 이용해 2차원 시각화 진행 군집 확인\n",
        "\n",
        "* 이슈 및 특이사항:  \n",
        "  - numerical & categorical 특성이 모두 존재해 k-prototypes로 군집화를 진행\n",
        "  - 적은 feature로 군집화 진행시, 시각화에서도 직관적으로 납득되는 군집을 형성\n",
        "  -  feature 수가 많아질 경우, 직관적 납득이 어려움  \n",
        "  ㄴ> 예상 원인: \n",
        "  특성의 차원이 너무 높기 때문에 이를 2차원으로 축소했을 때의 군집화는 실제 보이는 샘플의 위치와 다를 수 있다. (umap시각화의 한계)"
      ],
      "metadata": {
        "id": "MRrovgQtTT6K"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 적은 수의 Feature Clustering 시각화\n",
        "Features : '대상연령시작','대상연령끝','성별','신청절차'\n",
        "\n",
        "2차원 시각화 시, 군집 별로 다른 class가 섞이지 않고 잘 나뉜것을 확인할 수 있음\n"
      ],
      "metadata": {
        "id": "Yrum16Z1GU4X"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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      ],
      "metadata": {
        "id": "TUQSYFPLGiwQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 많은 수의 Feature Clustering\n",
        "\n",
        "Features: 전체 features\n",
        "\n",
        "feature가 많아지면서 각 feature들 간의 복잡한 관계가 생겨서인지, 시각적으로 모여있는 샘플들 안에 서로다른 class가 섞여있는 것을 볼 수 있음."
      ],
      "metadata": {
        "id": "V29N51CnJopX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "szHG2ivbGkJE"
      }
    }
  ]
}