news_topic.ipynb

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  "metadata": {
    "colab": {
      "name": "news_topic.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMsKwvFw/Kic1BK/v3i45oR",
      "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/threegenie/News_topic_classifying/blob/main/news_topic.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZiYsDOVHzb3z"
      },
      "source": [
        "## **뉴스 토픽 분류 AI 경진대회**\n",
        "한국어 뉴스 헤드라인을 이용하여, 뉴스의 주제를 분류하는 알고리즘 개발\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ma_xNMF2zabs"
      },
      "source": [
        "0 : IT과학 / 1 : 경제 / 2 : 사회 / 3 : 생활문화 / 4 : 세계 / 5 : 스포츠 / 6 : 정치"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AXLNpMDJz7ev"
      },
      "source": [
        "%%capture\n",
        "! pip install konlpy"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zfBsGbHT0Car"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import os, sys\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import urllib.request\n",
        "from collections import Counter\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DV-DMgLN0KDz"
      },
      "source": [
        "# 오류(SettingWithCopyError 발생)\n",
        "pd.set_option('mode.chained_assignment', 'raise') # SettingWithCopyError\n",
        "\n",
        "# 경고(SettingWithCopyWarning 발생, 기본 값입니다)\n",
        "pd.set_option('mode.chained_assignment', 'warn') # SettingWithCopyWarning\n",
        "\n",
        "# 무시\n",
        "pd.set_option('mode.chained_assignment',  None) # <==== 경고를 끈다"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xy4kshmB0KoT"
      },
      "source": [
        "df = pd.read_csv('train_data.csv')\n",
        "test = pd.read_csv('test_data.csv')"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "SG1CBhd20Yus",
        "outputId": "8ae91461-caa8-4047-bd18-a9133640c905"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>index</th>\n",
              "      <th>title</th>\n",
              "      <th>topic_idx</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>인천→핀란드 항공기 결항…휴가철 여행객 분통</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>실리콘밸리 넘어서겠다…구글 15조원 들여 美전역 거점화</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>이란 외무 긴장완화 해결책은 미국이 경제전쟁 멈추는 것</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>NYT 클린턴 측근韓기업 특수관계 조명…공과 사 맞물려종합</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>시진핑 트럼프에 중미 무역협상 조속 타결 희망</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   index                             title  topic_idx\n",
              "0      0          인천→핀란드 항공기 결항…휴가철 여행객 분통          4\n",
              "1      1    실리콘밸리 넘어서겠다…구글 15조원 들여 美전역 거점화          4\n",
              "2      2    이란 외무 긴장완화 해결책은 미국이 경제전쟁 멈추는 것          4\n",
              "3      3  NYT 클린턴 측근韓기업 특수관계 조명…공과 사 맞물려종합          4\n",
              "4      4         시진핑 트럼프에 중미 무역협상 조속 타결 희망          4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "Yj0OqDts0Z0P",
        "outputId": "64f5d845-0132-4383-97b0-b53d4ae0eba1"
      },
      "source": [
        "test.head()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>index</th>\n",
              "      <th>title</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>45654</td>\n",
              "      <td>유튜브 내달 2일까지 크리에이터 지원 공간 운영</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>45655</td>\n",
              "      <td>어버이날 맑다가 흐려져…남부지방 옅은 황사</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>45656</td>\n",
              "      <td>내년부터 국가RD 평가 때 논문건수는 반영 않는다</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>45657</td>\n",
              "      <td>김명자 신임 과총 회장 원로와 젊은 과학자 지혜 모을 것</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>45658</td>\n",
              "      <td>회색인간 작가 김동식 양심고백 등 새 소설집 2권 출간</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   index                            title\n",
              "0  45654       유튜브 내달 2일까지 크리에이터 지원 공간 운영\n",
              "1  45655          어버이날 맑다가 흐려져…남부지방 옅은 황사\n",
              "2  45656      내년부터 국가RD 평가 때 논문건수는 반영 않는다\n",
              "3  45657  김명자 신임 과총 회장 원로와 젊은 과학자 지혜 모을 것\n",
              "4  45658   회색인간 작가 김동식 양심고백 등 새 소설집 2권 출간"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OrwW7p120rD5"
      },
      "source": [
        "#### 데이터 정제 - 훈련, 테스트셋에 모두 적용 -> 정규표현식 사용하여 한글 제외 모두 제거"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "B795FHbr0piq",
        "outputId": "7e696cfe-844d-4aa3-92af-4482f2ed3d6d"
      },
      "source": [
        "df['title'] = df['title'].str.replace(\"[^ㄱ-ㅎㅏ-ㅣ가-힣 ]\",\"\")\n",
        "df['title'].replace('', np.nan, inplace=True)\n",
        "df.head()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>index</th>\n",
              "      <th>title</th>\n",
              "      <th>topic_idx</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>인천핀란드 항공기 결항휴가철 여행객 분통</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>실리콘밸리 넘어서겠다구글 조원 들여 전역 거점화</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>이란 외무 긴장완화 해결책은 미국이 경제전쟁 멈추는 것</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>클린턴 측근기업 특수관계 조명공과 사 맞물려종합</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>시진핑 트럼프에 중미 무역협상 조속 타결 희망</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   index                           title  topic_idx\n",
              "0      0          인천핀란드 항공기 결항휴가철 여행객 분통          4\n",
              "1      1      실리콘밸리 넘어서겠다구글 조원 들여 전역 거점화          4\n",
              "2      2  이란 외무 긴장완화 해결책은 미국이 경제전쟁 멈추는 것          4\n",
              "3      3      클린턴 측근기업 특수관계 조명공과 사 맞물려종합          4\n",
              "4      4       시진핑 트럼프에 중미 무역협상 조속 타결 희망          4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "ISt8CToo2bir",
        "outputId": "c655f374-409a-4862-fd51-1e77d137c040"
      },
      "source": [
        "test['title'] = test['title'].str.replace(\"[^ㄱ-ㅎㅏ-ㅣ가-힣 ]\",\"\")\n",
        "test['title'].replace('', np.nan, inplace=True)\n",
        "test.head()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>index</th>\n",
              "      <th>title</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>45654</td>\n",
              "      <td>유튜브 내달 일까지 크리에이터 지원 공간 운영</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>45655</td>\n",
              "      <td>어버이날 맑다가 흐려져남부지방 옅은 황사</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>45656</td>\n",
              "      <td>내년부터 국가 평가 때 논문건수는 반영 않는다</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>45657</td>\n",
              "      <td>김명자 신임 과총 회장 원로와 젊은 과학자 지혜 모을 것</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>45658</td>\n",
              "      <td>회색인간 작가 김동식 양심고백 등 새 소설집 권 출간</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   index                            title\n",
              "0  45654        유튜브 내달 일까지 크리에이터 지원 공간 운영\n",
              "1  45655           어버이날 맑다가 흐려져남부지방 옅은 황사\n",
              "2  45656        내년부터 국가 평가 때 논문건수는 반영 않는다\n",
              "3  45657  김명자 신임 과총 회장 원로와 젊은 과학자 지혜 모을 것\n",
              "4  45658    회색인간 작가 김동식 양심고백 등 새 소설집 권 출간"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CwOdA8B92iYH"
      },
      "source": [
        "#### 토큰화 - Okt 사용"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T83nxo672jeU"
      },
      "source": [
        "from konlpy.tag import Okt\n",
        "okt = Okt()"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qz9m1P8X2lzK"
      },
      "source": [
        "stopwords = ['요', '도', '는', '다', '의', '가', '이', '은', '한', '에', '하', '고', '을', '를', '인', '듯', '과', '와', '네', '들', '듯', '지', '임', '게']"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GX5_3H0N2nWP"
      },
      "source": [
        "df['tokenized'] = df['title'].apply(okt.morphs)\n",
        "df['tokenized'] = df['tokenized'].apply(lambda x: [item for item in x if item not in stopwords])"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ispwxvch2ue3"
      },
      "source": [
        "test['tokenized'] = test['title'].apply(okt.morphs)\n",
        "test['tokenized'] = test['tokenized'].apply(lambda x: [item for item in x if item not in stopwords])"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WPzcEyPo3AIU"
      },
      "source": [
        "#### 훈련, 테스트셋 분리"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_ZnRf9Ja3GMa",
        "outputId": "924479cc-44b7-4a69-ac55-6108662dfe3d"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "train = df['tokenized']\n",
        "target = df['topic_idx']\n",
        "\n",
        "X_train, X_val, y_train, y_val = train_test_split(train, target, test_size = 0.1, random_state = 11)\n",
        "\n",
        "X_train.shape, X_val.shape, y_train.shape, y_val.shape"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((41088,), (4566,), (41088,), (4566,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kr8tEHLu5o16"
      },
      "source": [
        "X_test = test['tokenized']"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lBiA0fl736Ye"
      },
      "source": [
        "tokenizer = Tokenizer()\n",
        "tokenizer.fit_on_texts(X_train)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BGq5zHqT4Fdx",
        "outputId": "b62d5b2a-fe4d-4a71-c309-d8e5efb5dffc"
      },
      "source": [
        "threshold = 2\n",
        "total_cnt = len(tokenizer.word_index) \n",
        "rare_cnt = 0 \n",
        "total_freq = 0 \n",
        "rare_freq = 0 \n",
        "\n",
        "for key, value in tokenizer.word_counts.items():\n",
        "    total_freq = total_freq + value\n",
        "\n",
        "    if(value < threshold):\n",
        "        rare_cnt = rare_cnt + 1\n",
        "        rare_freq = rare_freq + value\n",
        "\n",
        "print('단어 집합(vocabulary)의 크기 :',total_cnt)\n",
        "print('등장 빈도가 %s번 이하인 희귀 단어의 수: %s'%(threshold - 1, rare_cnt))\n",
        "print(\"단어 집합에서 희귀 단어의 비율:\", (rare_cnt / total_cnt)*100)\n",
        "print(\"전체 등장 빈도에서 희귀 단어 등장 빈도 비율:\", (rare_freq / total_freq)*100)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "단어 집합(vocabulary)의 크기 : 31196\n",
            "등장 빈도가 1번 이하인 희귀 단어의 수: 13164\n",
            "단어 집합에서 희귀 단어의 비율: 42.19771765610976\n",
            "전체 등장 빈도에서 희귀 단어 등장 빈도 비율: 3.629625953314474\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3F3XM_1z4IC_",
        "outputId": "b69bdd6a-8335-4328-d63f-b676a645c70c"
      },
      "source": [
        "vocab_size = total_cnt - rare_cnt + 2\n",
        "print('단어 집합의 크기 :',vocab_size)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "단어 집합의 크기 : 18034\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d31qWkw74J1w"
      },
      "source": [
        "tokenizer = Tokenizer(vocab_size, oov_token = 'OOV') \n",
        "tokenizer.fit_on_texts(X_train)\n",
        "X_train = tokenizer.texts_to_sequences(X_train)\n",
        "X_val= tokenizer.texts_to_sequences(X_val)\n",
        "X_test= tokenizer.texts_to_sequences(X_test)"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        },
        "id": "HRMg_Si44ROM",
        "outputId": "422e1ace-32c9-4555-970b-33a86f48f5b8"
      },
      "source": [
        "print('기사의 최대 길이 :',max(len(l) for l in X_train))\n",
        "print('기사의 평균 길이 :',sum(map(len, X_train))/len(X_train))\n",
        "plt.hist([len(s) for s in X_train], bins=50)\n",
        "plt.xlabel('length of samples')\n",
        "plt.ylabel('number of samples')\n",
        "plt.show()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "기사의 최대 길이 : 17\n",
            "기사의 평균 길이 : 8.826956775700934\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jPshLxfl4VUp"
      },
      "source": [
        "max_len = 80 #Padding 80으로\n",
        "X_train = pad_sequences(X_train, maxlen = max_len)\n",
        "X_val = pad_sequences(X_val, maxlen = max_len)\n",
        "X_test = pad_sequences(X_test, maxlen = max_len)"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kPI7FK3A4bOn"
      },
      "source": [
        "#### LSTM 모델을 이용한 기사 분석"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SWjt5oR54dr_"
      },
      "source": [
        "from tensorflow.keras.layers import Embedding, Dense, GRU\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.models import load_model\n",
        "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
        "from tensorflow.keras.layers import LSTM, Bidirectional\n",
        "\n",
        "model_lstm = Sequential()\n",
        "model_lstm.add(Embedding(vocab_size, 100))\n",
        "model_lstm.add(LSTM(100)) #Bidirectional drop\n",
        "model_lstm.add(Dense(1, activation='relu')) #activation = sigmoid -> relu"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RY6IOhK34mAj",
        "outputId": "528210fa-0ca7-463d-c6a0-f64c9a7238f2"
      },
      "source": [
        "model_lstm.summary()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding (Embedding)        (None, None, 100)         1803400   \n",
            "_________________________________________________________________\n",
            "lstm (LSTM)                  (None, 100)               80400     \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 1)                 101       \n",
            "=================================================================\n",
            "Total params: 1,883,901\n",
            "Trainable params: 1,883,901\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4bu21fpz4nBE"
      },
      "source": [
        "es_lstm = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=4)\n",
        "mc_lstm = ModelCheckpoint('best_model.h5', monitor='val_acc', mode='max', verbose=1, save_best_only=True)"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IyW9Deoj4ozc",
        "outputId": "8c239f8c-dd8e-4d2f-8c0b-aff93fedd88e"
      },
      "source": [
        "model_lstm.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['acc'])\n",
        "history = model_lstm.fit(X_train, y_train, epochs=15, callbacks=[es_lstm, mc_lstm], batch_size=60, validation_split=0.2)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/15\n",
            "548/548 [==============================] - 58s 100ms/step - loss: -32.7097 - acc: 0.1354 - val_loss: -32.8050 - val_acc: 0.1377\n",
            "\n",
            "Epoch 00001: val_acc improved from -inf to 0.13775, saving model to best_model.h5\n",
            "Epoch 2/15\n",
            "548/548 [==============================] - 54s 99ms/step - loss: -33.0444 - acc: 0.1356 - val_loss: -32.8050 - val_acc: 0.1377\n",
            "\n",
            "Epoch 00002: val_acc did not improve from 0.13775\n",
            "Epoch 3/15\n",
            "548/548 [==============================] - 54s 99ms/step - loss: -33.0444 - acc: 0.1356 - val_loss: -32.8050 - val_acc: 0.1377\n",
            "\n",
            "Epoch 00003: val_acc did not improve from 0.13775\n",
            "Epoch 4/15\n",
            "548/548 [==============================] - 54s 98ms/step - loss: -33.0444 - acc: 0.1356 - val_loss: -32.8050 - val_acc: 0.1377\n",
            "\n",
            "Epoch 00004: val_acc did not improve from 0.13775\n",
            "Epoch 5/15\n",
            "548/548 [==============================] - 54s 98ms/step - loss: -33.0444 - acc: 0.1356 - val_loss: -32.8050 - val_acc: 0.1377\n",
            "\n",
            "Epoch 00005: val_acc did not improve from 0.13775\n",
            "Epoch 00005: early stopping\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N9jq4JK_4zMY"
      },
      "source": [
        "y_pred = model_lstm.predict(X_val)"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TFhR_vRx8gKi"
      },
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import classification_report"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iEI45OYQ8hnw",
        "outputId": "f74a0a0b-29c8-4c65-ec96-aafd63f8b3ee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        }
      },
      "source": [
        "accuracy_score(y_val, y_pred)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-28-71f1f410495d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0maccuracy_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_val\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py\u001b[0m in \u001b[0;36maccuracy_score\u001b[0;34m(y_true, y_pred, normalize, sample_weight)\u001b[0m\n\u001b[1;32m    183\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    184\u001b[0m     \u001b[0;31m# Compute accuracy for each possible representation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 185\u001b[0;31m     \u001b[0my_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    186\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    187\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0my_type\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'multilabel'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py\u001b[0m in \u001b[0;36m_check_targets\u001b[0;34m(y_true, y_pred)\u001b[0m\n\u001b[1;32m     88\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_type\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     89\u001b[0m         raise ValueError(\"Classification metrics can't handle a mix of {0} \"\n\u001b[0;32m---> 90\u001b[0;31m                          \"and {1} targets\".format(type_true, type_pred))\n\u001b[0m\u001b[1;32m     91\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     92\u001b[0m     \u001b[0;31m# We can't have more than one value on y_type => The set is no more needed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Classification metrics can't handle a mix of multiclass and continuous targets"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vT9aPgc-8D66",
        "outputId": "ee1d5cce-92a9-4dfb-91fd-e34909476cb3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        }
      },
      "source": [
        "print(classification_report(y_val, y_pred))"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-29-e573157cdd23>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclassification_report\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_val\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py\u001b[0m in \u001b[0;36mclassification_report\u001b[0;34m(y_true, y_pred, labels, target_names, sample_weight, digits, output_dict, zero_division)\u001b[0m\n\u001b[1;32m   1969\u001b[0m     \"\"\"\n\u001b[1;32m   1970\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1971\u001b[0;31m     \u001b[0my_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1972\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1973\u001b[0m     \u001b[0mlabels_given\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py\u001b[0m in \u001b[0;36m_check_targets\u001b[0;34m(y_true, y_pred)\u001b[0m\n\u001b[1;32m     88\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_type\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     89\u001b[0m         raise ValueError(\"Classification metrics can't handle a mix of {0} \"\n\u001b[0;32m---> 90\u001b[0;31m                          \"and {1} targets\".format(type_true, type_pred))\n\u001b[0m\u001b[1;32m     91\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     92\u001b[0m     \u001b[0;31m# We can't have more than one value on y_type => The set is no more needed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Classification metrics can't handle a mix of multiclass and continuous targets"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TbZNFhQa7QF-"
      },
      "source": [
        "#### 제출 파일 "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gFLAiboX7Rpz"
      },
      "source": [
        "submission = pd.read_csv('sample_submission.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0t_H1w1J8MMs"
      },
      "source": [
        "pred = model_lstm.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Rj5oyTW8PEk"
      },
      "source": [
        "submission['topic_idx'] = pred\n",
        "submission.head()"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}