new_train_tutorial2.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/juhee3199/fake-news-detection/blob/main/new_train_tutorial2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eS61baRyUIfS"
      },
      "source": [
        "new_train.csv \r\n",
        "- 출력노드 64\r\n",
        "- dropout 0.5 -> 0.5"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GCd9Z7N1XqKH",
        "outputId": "6c245377-d331-4c6b-cbed-1fabbd6a8487"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uuIGZdJkF3sb"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import re\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_p0YfmVNGmdO"
      },
      "source": [
        "\n",
        "- 0: 진짜뉴스, 1: 가짜 뉴스"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        },
        "id": "lCdYTSooGNAu",
        "outputId": "08654712-b752-40b7-f92f-476f73a17a77"
      },
      "source": [
        "train = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/fake news detection/new_train.csv')\n",
        "train.head()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
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              "   Unnamed: 0       n_id      date  ... ord info  info1_words\n",
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              "\n",
              "[5 rows x 8 columns]"
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        },
        "id": "glM6M8GMaHAO",
        "outputId": "4c5203f7-9174-47f1-8754-02291adaebb4"
      },
      "source": [
        "test = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/fake news detection/news_test.csv')\n",
        "test.head()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>[주목!e스몰캡]코세스, 마이크로LED 시장 개화 최대수혜 기대</td>\n",
              "      <td>마이크로 LED TV 장비 양산 돌입- 전방업체 투자 확대로 본업도 호조연일 '신고가'</td>\n",
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              "      <td>[주목!e스몰캡]코세스, 마이크로LED 시장 개화 최대수혜 기대</td>\n",
              "      <td>[이데일리 김대웅 기자] 반도체 장비 업체 코세스(089890)의 기술력이 마이크로...</td>\n",
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              "      <td>[주목!e스몰캡]코세스, 마이크로LED 시장 개화 최대수혜 기대</td>\n",
              "      <td>최근 대형 업체들과 거래를 맺고 관련 장비들의 양산에 돌입하면서 주가도 연일 신고가...</td>\n",
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              "      <td>20200118</td>\n",
              "      <td>[주목!e스몰캡]코세스, 마이크로LED 시장 개화 최대수혜 기대</td>\n",
              "      <td>TV를 필두로 올해부터 마이크로 LED의 시대가 본격적으로 개화할 것으로 예상되면서...</td>\n",
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              "      <td>NEWS00237</td>\n",
              "      <td>20200118</td>\n",
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              "      <td>코세스는 반도체 장비를 제조, 판매하는 업체로 지난 2006년 11월 코스닥 시장에...</td>\n",
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              "        n_id      date  ... ord           id\n",
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              "\n",
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          "metadata": {
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          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J0hiDVuJYrNn"
      },
      "source": [
        "submission = pd.read_csv(\"/content/drive/MyDrive/Colab Notebooks/fake news detection/sample_submission.csv\")"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a3CokdJXGnNz",
        "outputId": "78d43b06-5cd0-4fb4-e765-9adcf423b762"
      },
      "source": [
        "train.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 118745 entries, 0 to 118744\n",
            "Data columns (total 6 columns):\n",
            " #   Column   Non-Null Count   Dtype \n",
            "---  ------   --------------   ----- \n",
            " 0   n_id     118745 non-null  object\n",
            " 1   date     118745 non-null  int64 \n",
            " 2   title    118745 non-null  object\n",
            " 3   content  118745 non-null  object\n",
            " 4   ord      118745 non-null  int64 \n",
            " 5   info     118745 non-null  int64 \n",
            "dtypes: int64(3), object(3)\n",
            "memory usage: 5.4+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xksmIlXJKX8E",
        "outputId": "6b00d8dd-c3a0-4a7b-fd94-ea9ce0bad576"
      },
      "source": [
        "print(\"진짜 뉴스 개수: {}\".format(len(train.loc[train['info']==0])))\n",
        "print(\"가짜 뉴스 개수: {}\".format(len(train.loc[train['info']==1])))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "진짜 뉴스 개수: 71813\n",
            "가짜 뉴스 개수: 46932\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mJI0PCb_NvzW"
      },
      "source": [
        "**뉴스 헤드라인 확인하기**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IqAafeHANje-",
        "outputId": "a79f022f-9e21-469e-d547-4ed24aad555c"
      },
      "source": [
        "train['content'].head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    [이데일리 MARKETPOINT]15:32 현재 코스닥 기관 678억 순매도\n",
              "1         \"실적기반\" 저가에 매집해야 할 8월 급등유망주 TOP 5 전격공개\n",
              "2             하이스탁론, 선취수수료 없는 월 0.4% 최저금리 상품 출시\n",
              "3              종합 경제정보 미디어 이데일리 - 무단전재 & 재배포 금지\n",
              "4                          전국적인 소비 붐 조성에 기여할 예정\n",
              "Name: content, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "id": "muGaB0KcNhFr",
        "outputId": "375360d4-700c-494d-e7d6-0555e8f1e6c4"
      },
      "source": [
        "# 뉴스 헤드라인 길이\n",
        "\n",
        "train['length'] = train['content'].apply(lambda x: len(x))\n",
        "plt.figure(figsize=(12,5))\n",
        "sns.distplot(train['length'],color='r')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0ed8826dd8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E8QYfpLbOMdI"
      },
      "source": [
        "대부분 뉴스 헤드라인의 길이는 0~250자에 분포되어 있다"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "id": "wAB0RBynKlfg",
        "outputId": "769b22dc-939a-40d5-c494-47d99ff5e63b"
      },
      "source": [
        "plt.figure(figsize=(12,7))\n",
        "fake = train.loc[train['info']==1]\n",
        "real = train.loc[train['info']==0]\n",
        "sns.distplot(fake['length'],label = 'length of fake news')\n",
        "sns.distplot(real['length'],label = 'length of real news')\n",
        "plt.xlim(0,250)\n",
        "plt.legend()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f0ed4a3afd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7qaGwxHDOrMG"
      },
      "source": [
        "진짜, 가짜 뉴스 텍스트의 길이 분포도를 보면 가짜 뉴스는 0-50자 사이에 치우쳐있는 반면, 진짜뉴스는 0-150에 골고루 분포되어있다\n",
        "\n",
        "가짜 뉴스는 짧은 스팸성 내용이들어있다고 추측할 수 있다"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vW-oEFWlPcgL",
        "outputId": "81b15ca4-de6d-4086-8411-75b0d208da17"
      },
      "source": [
        "fake['length'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    46932.000000\n",
              "mean        34.470702\n",
              "std         20.383651\n",
              "min          2.000000\n",
              "25%         27.000000\n",
              "50%         34.000000\n",
              "75%         39.000000\n",
              "max       1022.000000\n",
              "Name: length, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q8z1kcKpObSI",
        "outputId": "405e6175-18a2-448a-f0f7-088a7835afda"
      },
      "source": [
        "real['length'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    71813.000000\n",
              "mean        62.342891\n",
              "std         43.596060\n",
              "min          2.000000\n",
              "25%         34.000000\n",
              "50%         55.000000\n",
              "75%         82.000000\n",
              "max       3469.000000\n",
              "Name: length, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3rLozwFNPSKc"
      },
      "source": [
        "# ord 의 가짜진짜 뉴스 분포 정도\n",
        "# 가짜 뉴스에 대한 wordcloud 에 따라 해당 top 단어 들이 있는 컬럼을 fake뉴스로 예측."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jNa7IZuIQiFZ"
      },
      "source": [
        "AutoML \n",
        "https://dacon.io/competitions/official/235658/codeshare/1843?page=1&dtype=recent&ptype=pub\n",
        "PB 0.94"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wY__WIPsS9Wp"
      },
      "source": [
        "자연어 처리 과정\n",
        "1. Preprocessing\n",
        ": 불용어 제거(Stopwords removing), 형태소 분석(Stemming), 표제어 추출(Lemmatization)\n",
        "\n",
        "\n",
        "2. Vectorization\n",
        ": One-hot Encoding, Count vectorization, Tfidf, Padding\n",
        "\n",
        "\n",
        "3. Embedding\n",
        ": Word2vec, Doc2vec, Glove, Fasttext\n",
        "\n",
        "\n",
        "4. Modeling\n",
        ": GRU, LSTM, Attention\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ju6V4StfTyBw"
      },
      "source": [
        "위의 과정을 이번 신문기사 분류 문제에 적용해보면 다음과 같습니다.\n",
        "\n",
        "1. 우선 데이터를 불러온 후 기사의 각 문장별로 Konlpy의 함수 중 하나를 활용하여 형태소 분석을 진행합니다.\n",
        "2. 그 후 필요없는 단어들을 불용어로 지정하고 제외합니다.\n",
        "3. 해당 결과를 sklearn 패키지의 함수 중 하나를 이용하여 정수 인코딩으로 변환해주는 Vectorization을 진행합니다.\n",
        "4. 각 문장별로 벡터화된 문장들은 서로 다른 길이를 갖게 됩니다. 모델에 입력하기 위해 모든 문장 벡터들은 일정한 길이를 가져야 하므로 길이가 긴 문장을 자르고 길이가 짧은 문장은 0으로 채워넣습니다.(Padding)\n",
        "5. 앞서 처리된 문장 벡터들을 특정 차원으로 임베딩 시킵니다. 딥러닝 모델을 이용할 시 자동으로 임베딩을 진행해주는 층을 추가하면 간단하게 진행이 됩니다. 성능을 높이기 위해서 pre-trained embedding인 word2vec, glove 등을 이용합니다.\n",
        "6. 훈련을 시키고 테스트를 진행합니다."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bi4h9vOna5gs"
      },
      "source": [
        "### 1. Preprocessing -  형태소 분석, 불용어 제거"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1XlYJT1qrpBe",
        "outputId": "9b8e0ece-8b51-4e44-ab1c-f11841f83c03"
      },
      "source": [
        "# konlpy Mecab 사용하기\n",
        "\n",
        "!set -x \\\n",
        "&& pip install konlpy \\\n",
        "&& curl -s https://raw.githubusercontent.com/konlpy/konlpy/master/scripts/mecab.sh | bash -x"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "+ pip install konlpy\n",
            "Collecting konlpy\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/85/0e/f385566fec837c0b83f216b2da65db9997b35dd675e107752005b7d392b1/konlpy-0.5.2-py2.py3-none-any.whl (19.4MB)\n",
            "\u001b[K     |████████████████████████████████| 19.4MB 1.3MB/s \n",
            "\u001b[?25hCollecting beautifulsoup4==4.6.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/9e/d4/10f46e5cfac773e22707237bfcd51bbffeaf0a576b0a847ec7ab15bd7ace/beautifulsoup4-4.6.0-py3-none-any.whl (86kB)\n",
            "\u001b[K     |████████████████████████████████| 92kB 9.1MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.6 in /usr/local/lib/python3.6/dist-packages (from konlpy) (1.18.5)\n",
            "Requirement already satisfied: lxml>=4.1.0 in /usr/local/lib/python3.6/dist-packages (from konlpy) (4.2.6)\n",
            "Collecting JPype1>=0.7.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/b7/21/9e2c0dbf9df856e6392a1aec1d18006c60b175aa4e31d351e8278a8a63c0/JPype1-1.2.0-cp36-cp36m-manylinux2010_x86_64.whl (453kB)\n",
            "\u001b[K     |████████████████████████████████| 460kB 44.6MB/s \n",
            "\u001b[?25hCollecting colorama\n",
            "  Downloading https://files.pythonhosted.org/packages/44/98/5b86278fbbf250d239ae0ecb724f8572af1c91f4a11edf4d36a206189440/colorama-0.4.4-py2.py3-none-any.whl\n",
            "Collecting tweepy>=3.7.0\n",
            "  Downloading https://files.pythonhosted.org/packages/bb/7c/99d51f80f3b77b107ebae2634108717362c059a41384a1810d13e2429a81/tweepy-3.9.0-py2.py3-none-any.whl\n",
            "Requirement already satisfied: typing-extensions; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from JPype1>=0.7.0->konlpy) (3.7.4.3)\n",
            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tweepy>=3.7.0->konlpy) (1.15.0)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tweepy>=3.7.0->konlpy) (1.3.0)\n",
            "Requirement already satisfied: requests[socks]>=2.11.1 in /usr/local/lib/python3.6/dist-packages (from tweepy>=3.7.0->konlpy) (2.23.0)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from requests-oauthlib>=0.7.0->tweepy>=3.7.0->konlpy) (3.1.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests[socks]>=2.11.1->tweepy>=3.7.0->konlpy) (2020.12.5)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests[socks]>=2.11.1->tweepy>=3.7.0->konlpy) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests[socks]>=2.11.1->tweepy>=3.7.0->konlpy) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests[socks]>=2.11.1->tweepy>=3.7.0->konlpy) (1.24.3)\n",
            "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6; extra == \"socks\" in /usr/local/lib/python3.6/dist-packages (from requests[socks]>=2.11.1->tweepy>=3.7.0->konlpy) (1.7.1)\n",
            "Installing collected packages: beautifulsoup4, JPype1, colorama, tweepy, konlpy\n",
            "  Found existing installation: beautifulsoup4 4.6.3\n",
            "    Uninstalling beautifulsoup4-4.6.3:\n",
            "      Successfully uninstalled beautifulsoup4-4.6.3\n",
            "  Found existing installation: tweepy 3.6.0\n",
            "    Uninstalling tweepy-3.6.0:\n",
            "      Successfully uninstalled tweepy-3.6.0\n",
            "Successfully installed JPype1-1.2.0 beautifulsoup4-4.6.0 colorama-0.4.4 konlpy-0.5.2 tweepy-3.9.0\n",
            "+ bash -x\n",
            "+ curl -s https://raw.githubusercontent.com/konlpy/konlpy/master/scripts/mecab.sh\n",
            "+ mecab_dicdir=/usr/local/lib/mecab/dic/mecab-ko-dic\n",
            "+ set -e\n",
            "++ uname\n",
            "+ os=Linux\n",
            "+ [[ ! Linux == \\L\\i\\n\\u\\x ]]\n",
            "+ hash sudo\n",
            "+ sudo=sudo\n",
            "+ python=python3\n",
            "+ hash pyenv\n",
            "+ at_user_site=\n",
            "++ check_python_site_location_is_writable\n",
            "++ python3 -\n",
            "+ [[ 1 == \\0 ]]\n",
            "+ hash automake\n",
            "+ echo 'Installing automake (A dependency for mecab-ko)'\n",
            "Installing automake (A dependency for mecab-ko)\n",
            "+ install_automake\n",
            "+ '[' Linux == Linux ']'\n",
            "++ grep -Ei 'debian|buntu|mint' /etc/lsb-release /etc/os-release\n",
            "+ '[' '/etc/lsb-release:DISTRIB_ID=Ubuntu\n",
            "/etc/lsb-release:DISTRIB_DESCRIPTION=\"Ubuntu 18.04.5 LTS\"\n",
            "/etc/os-release:NAME=\"Ubuntu\"\n",
            "/etc/os-release:ID=ubuntu\n",
            "/etc/os-release:ID_LIKE=debian\n",
            "/etc/os-release:PRETTY_NAME=\"Ubuntu 18.04.5 LTS\"\n",
            "/etc/os-release:HOME_URL=\"https://www.ubuntu.com/\"\n",
            "/etc/os-release:SUPPORT_URL=\"https://help.ubuntu.com/\"\n",
            "/etc/os-release:BUG_REPORT_URL=\"https://bugs.launchpad.net/ubuntu/\"\n",
            "/etc/os-release:PRIVACY_POLICY_URL=\"https://www.ubuntu.com/legal/terms-and-policies/privacy-policy\"\n",
            "/etc/os-release:UBUNTU_CODENAME=bionic' ']'\n",
            "+ sudo apt-get update\n",
            "Get:1 http://security.ubuntu.com/ubuntu bionic-security InRelease [88.7 kB]\n",
            "Ign:2 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  InRelease\n",
            "Get:3 https://cloud.r-project.org/bin/linux/ubuntu bionic-cran40/ InRelease [3,626 B]\n",
            "Ign:4 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64  InRelease\n",
            "Get:5 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic InRelease [15.9 kB]\n",
            "Hit:6 http://archive.ubuntu.com/ubuntu bionic InRelease\n",
            "Get:7 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  Release [697 B]\n",
            "Hit:8 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64  Release\n",
            "Get:9 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  Release.gpg [836 B]\n",
            "Get:10 http://archive.ubuntu.com/ubuntu bionic-updates InRelease [88.7 kB]\n",
            "Get:11 http://security.ubuntu.com/ubuntu bionic-security/restricted amd64 Packages [237 kB]\n",
            "Get:12 http://security.ubuntu.com/ubuntu bionic-security/multiverse amd64 Packages [15.3 kB]\n",
            "Get:13 http://security.ubuntu.com/ubuntu bionic-security/main amd64 Packages [1,816 kB]\n",
            "Get:14 https://cloud.r-project.org/bin/linux/ubuntu bionic-cran40/ Packages [40.7 kB]\n",
            "Get:15 http://ppa.launchpad.net/graphics-drivers/ppa/ubuntu bionic InRelease [21.3 kB]\n",
            "Get:16 http://security.ubuntu.com/ubuntu bionic-security/universe amd64 Packages [1,372 kB]\n",
            "Get:17 http://archive.ubuntu.com/ubuntu bionic-backports InRelease [74.6 kB]\n",
            "Ign:19 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  Packages\n",
            "Get:19 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  Packages [506 kB]\n",
            "Get:20 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic/main Sources [1,699 kB]\n",
            "Get:21 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 Packages [2,136 kB]\n",
            "Get:22 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 Packages [2,244 kB]\n",
            "Get:23 http://archive.ubuntu.com/ubuntu bionic-updates/multiverse amd64 Packages [53.8 kB]\n",
            "Get:24 http://archive.ubuntu.com/ubuntu bionic-updates/restricted amd64 Packages [266 kB]\n",
            "Get:25 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic/main amd64 Packages [870 kB]\n",
            "Get:26 http://ppa.launchpad.net/graphics-drivers/ppa/ubuntu bionic/main amd64 Packages [46.5 kB]\n",
            "Fetched 11.6 MB in 3s (4,301 kB/s)\n",
            "Reading package lists... Done\n",
            "+ sudo apt-get install -y automake\n",
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "The following additional packages will be installed:\n",
            "  autoconf autotools-dev libsigsegv2 m4\n",
            "Suggested packages:\n",
            "  autoconf-archive gnu-standards autoconf-doc libtool gettext m4-doc\n",
            "The following NEW packages will be installed:\n",
            "  autoconf automake autotools-dev libsigsegv2 m4\n",
            "0 upgraded, 5 newly installed, 0 to remove and 56 not upgraded.\n",
            "Need to get 1,082 kB of archives.\n",
            "After this operation, 3,994 kB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu bionic/main amd64 libsigsegv2 amd64 2.12-1 [14.7 kB]\n",
            "Get:2 http://archive.ubuntu.com/ubuntu bionic/main amd64 m4 amd64 1.4.18-1 [197 kB]\n",
            "Get:3 http://archive.ubuntu.com/ubuntu bionic/main amd64 autoconf all 2.69-11 [322 kB]\n",
            "Get:4 http://archive.ubuntu.com/ubuntu bionic/main amd64 autotools-dev all 20180224.1 [39.6 kB]\n",
            "Get:5 http://archive.ubuntu.com/ubuntu bionic/main amd64 automake all 1:1.15.1-3ubuntu2 [509 kB]\n",
            "Fetched 1,082 kB in 1s (1,297 kB/s)\n",
            "debconf: unable to initialize frontend: Dialog\n",
            "debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 76, <> line 5.)\n",
            "debconf: falling back to frontend: Readline\n",
            "debconf: unable to initialize frontend: Readline\n",
            "debconf: (This frontend requires a controlling tty.)\n",
            "debconf: falling back to frontend: Teletype\n",
            "dpkg-preconfigure: unable to re-open stdin: \n",
            "Selecting previously unselected package libsigsegv2:amd64.\n",
            "(Reading database ... 144865 files and directories currently installed.)\n",
            "Preparing to unpack .../libsigsegv2_2.12-1_amd64.deb ...\n",
            "Unpacking libsigsegv2:amd64 (2.12-1) ...\n",
            "Selecting previously unselected package m4.\n",
            "Preparing to unpack .../archives/m4_1.4.18-1_amd64.deb ...\n",
            "Unpacking m4 (1.4.18-1) ...\n",
            "Selecting previously unselected package autoconf.\n",
            "Preparing to unpack .../autoconf_2.69-11_all.deb ...\n",
            "Unpacking autoconf (2.69-11) ...\n",
            "Selecting previously unselected package autotools-dev.\n",
            "Preparing to unpack .../autotools-dev_20180224.1_all.deb ...\n",
            "Unpacking autotools-dev (20180224.1) ...\n",
            "Selecting previously unselected package automake.\n",
            "Preparing to unpack .../automake_1%3a1.15.1-3ubuntu2_all.deb ...\n",
            "Unpacking automake (1:1.15.1-3ubuntu2) ...\n",
            "Setting up libsigsegv2:amd64 (2.12-1) ...\n",
            "Setting up m4 (1.4.18-1) ...\n",
            "Setting up autotools-dev (20180224.1) ...\n",
            "Setting up autoconf (2.69-11) ...\n",
            "Setting up automake (1:1.15.1-3ubuntu2) ...\n",
            "update-alternatives: using /usr/bin/automake-1.15 to provide /usr/bin/automake (automake) in auto mode\n",
            "Processing triggers for libc-bin (2.27-3ubuntu1.2) ...\n",
            "/sbin/ldconfig.real: /usr/local/lib/python3.6/dist-packages/ideep4py/lib/libmkldnn.so.0 is not a symbolic link\n",
            "\n",
            "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n",
            "+ hash mecab\n",
            "+ echo 'Install mecab-ko-dic'\n",
            "Install mecab-ko-dic\n",
            "+ install_mecab_ko\n",
            "+ cd /tmp\n",
            "+ curl -LO https://bitbucket.org/eunjeon/mecab-ko/downloads/mecab-0.996-ko-0.9.2.tar.gz\n",
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\n",
            "100 1381k  100 1381k    0     0  3959k      0 --:--:-- --:--:-- --:--:-- 3959k\n",
            "+ tar zxfv mecab-0.996-ko-0.9.2.tar.gz\n",
            "mecab-0.996-ko-0.9.2/\n",
            "mecab-0.996-ko-0.9.2/example/\n",
            "mecab-0.996-ko-0.9.2/example/example.cpp\n",
            "mecab-0.996-ko-0.9.2/example/example_lattice.cpp\n",
            "mecab-0.996-ko-0.9.2/example/example_lattice.c\n",
            "mecab-0.996-ko-0.9.2/example/example.c\n",
            "mecab-0.996-ko-0.9.2/example/thread_test.cpp\n",
            "mecab-0.996-ko-0.9.2/mecab-config.in\n",
            "mecab-0.996-ko-0.9.2/man/\n",
            "mecab-0.996-ko-0.9.2/man/Makefile.am\n",
            "mecab-0.996-ko-0.9.2/man/mecab.1\n",
            "mecab-0.996-ko-0.9.2/man/Makefile.in\n",
            "mecab-0.996-ko-0.9.2/mecab.iss.in\n",
            "mecab-0.996-ko-0.9.2/config.guess\n",
            "mecab-0.996-ko-0.9.2/README\n",
            "mecab-0.996-ko-0.9.2/COPYING\n",
            "mecab-0.996-ko-0.9.2/CHANGES.md\n",
            "mecab-0.996-ko-0.9.2/README.md\n",
            "mecab-0.996-ko-0.9.2/INSTALL\n",
            "mecab-0.996-ko-0.9.2/config.sub\n",
            "mecab-0.996-ko-0.9.2/configure.in\n",
            "mecab-0.996-ko-0.9.2/swig/\n",
            "mecab-0.996-ko-0.9.2/swig/Makefile\n",
            "mecab-0.996-ko-0.9.2/swig/version.h.in\n",
            "mecab-0.996-ko-0.9.2/swig/version.h\n",
            "mecab-0.996-ko-0.9.2/swig/MeCab.i\n",
            "mecab-0.996-ko-0.9.2/aclocal.m4\n",
            "mecab-0.996-ko-0.9.2/LGPL\n",
            "mecab-0.996-ko-0.9.2/Makefile.am\n",
            "mecab-0.996-ko-0.9.2/configure\n",
            "mecab-0.996-ko-0.9.2/tests/\n",
            "mecab-0.996-ko-0.9.2/tests/autolink/\n",
            "mecab-0.996-ko-0.9.2/tests/autolink/unk.def\n",
            "mecab-0.996-ko-0.9.2/tests/autolink/dicrc\n",
            "mecab-0.996-ko-0.9.2/tests/autolink/dic.csv\n",
            "mecab-0.996-ko-0.9.2/tests/autolink/test\n",
            "mecab-0.996-ko-0.9.2/tests/autolink/char.def\n",
            "mecab-0.996-ko-0.9.2/tests/autolink/matrix.def\n",
            "mecab-0.996-ko-0.9.2/tests/autolink/test.gld\n",
            "mecab-0.996-ko-0.9.2/tests/t9/\n",
            "mecab-0.996-ko-0.9.2/tests/t9/unk.def\n",
            "mecab-0.996-ko-0.9.2/tests/t9/ipadic.pl\n",
            "mecab-0.996-ko-0.9.2/tests/t9/dicrc\n",
            "mecab-0.996-ko-0.9.2/tests/t9/dic.csv\n",
            "mecab-0.996-ko-0.9.2/tests/t9/test\n",
            "mecab-0.996-ko-0.9.2/tests/t9/char.def\n",
            "mecab-0.996-ko-0.9.2/tests/t9/matrix.def\n",
            "mecab-0.996-ko-0.9.2/tests/t9/mkdic.pl\n",
            "mecab-0.996-ko-0.9.2/tests/t9/test.gld\n",
            "mecab-0.996-ko-0.9.2/tests/cost-train/\n",
            "mecab-0.996-ko-0.9.2/tests/cost-train/ipa.train\n",
            "mecab-0.996-ko-0.9.2/tests/cost-train/ipa.test\n",
            "mecab-0.996-ko-0.9.2/tests/cost-train/seed/\n",
            "mecab-0.996-ko-0.9.2/tests/cost-train/seed/rewrite.def\n",
            "mecab-0.996-ko-0.9.2/tests/cost-train/seed/feature.def\n",
            "mecab-0.996-ko-0.9.2/tests/cost-train/seed/unk.def\n",
            "mecab-0.996-ko-0.9.2/tests/cost-train/seed/dicrc\n",
            "mecab-0.996-ko-0.9.2/tests/cost-train/seed/dic.csv\n",
            "mecab-0.996-ko-0.9.2/tests/cost-train/seed/char.def\n",
            "mecab-0.996-ko-0.9.2/tests/cost-train/seed/matrix.def\n",
            "mecab-0.996-ko-0.9.2/tests/run-eval.sh\n",
            "mecab-0.996-ko-0.9.2/tests/run-cost-train.sh\n",
            "mecab-0.996-ko-0.9.2/tests/Makefile.am\n",
            "mecab-0.996-ko-0.9.2/tests/katakana/\n",
            "mecab-0.996-ko-0.9.2/tests/katakana/unk.def\n",
            "mecab-0.996-ko-0.9.2/tests/katakana/dicrc\n",
            "mecab-0.996-ko-0.9.2/tests/katakana/dic.csv\n",
            "mecab-0.996-ko-0.9.2/tests/katakana/test\n",
            "mecab-0.996-ko-0.9.2/tests/katakana/char.def\n",
            "mecab-0.996-ko-0.9.2/tests/katakana/matrix.def\n",
            "mecab-0.996-ko-0.9.2/tests/katakana/test.gld\n",
            "mecab-0.996-ko-0.9.2/tests/eval/\n",
            "mecab-0.996-ko-0.9.2/tests/eval/answer\n",
            "mecab-0.996-ko-0.9.2/tests/eval/system\n",
            "mecab-0.996-ko-0.9.2/tests/eval/test.gld\n",
            "mecab-0.996-ko-0.9.2/tests/shiin/\n",
            "mecab-0.996-ko-0.9.2/tests/shiin/unk.def\n",
            "mecab-0.996-ko-0.9.2/tests/shiin/dicrc\n",
            "mecab-0.996-ko-0.9.2/tests/shiin/dic.csv\n",
            "mecab-0.996-ko-0.9.2/tests/shiin/test\n",
            "mecab-0.996-ko-0.9.2/tests/shiin/char.def\n",
            "mecab-0.996-ko-0.9.2/tests/shiin/matrix.def\n",
            "mecab-0.996-ko-0.9.2/tests/shiin/mkdic.pl\n",
            "mecab-0.996-ko-0.9.2/tests/shiin/test.gld\n",
            "mecab-0.996-ko-0.9.2/tests/latin/\n",
            "mecab-0.996-ko-0.9.2/tests/latin/unk.def\n",
            "mecab-0.996-ko-0.9.2/tests/latin/dicrc\n",
            "mecab-0.996-ko-0.9.2/tests/latin/dic.csv\n",
            "mecab-0.996-ko-0.9.2/tests/latin/test\n",
            "mecab-0.996-ko-0.9.2/tests/latin/char.def\n",
            "mecab-0.996-ko-0.9.2/tests/latin/matrix.def\n",
            "mecab-0.996-ko-0.9.2/tests/latin/test.gld\n",
            "mecab-0.996-ko-0.9.2/tests/chartype/\n",
            "mecab-0.996-ko-0.9.2/tests/chartype/unk.def\n",
            "mecab-0.996-ko-0.9.2/tests/chartype/dicrc\n",
            "mecab-0.996-ko-0.9.2/tests/chartype/dic.csv\n",
            "mecab-0.996-ko-0.9.2/tests/chartype/test\n",
            "mecab-0.996-ko-0.9.2/tests/chartype/char.def\n",
            "mecab-0.996-ko-0.9.2/tests/chartype/matrix.def\n",
            "mecab-0.996-ko-0.9.2/tests/chartype/test.gld\n",
            "mecab-0.996-ko-0.9.2/tests/run-dics.sh\n",
            "mecab-0.996-ko-0.9.2/tests/ngram/\n",
            "mecab-0.996-ko-0.9.2/tests/ngram/unk.def\n",
            "mecab-0.996-ko-0.9.2/tests/ngram/dicrc\n",
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            "mecab-0.996-ko-0.9.2/src/scoped_ptr.h\n",
            "mecab-0.996-ko-0.9.2/Makefile.in\n",
            "+ cd mecab-0.996-ko-0.9.2\n",
            "+ ./configure\n",
            "checking for a BSD-compatible install... /usr/bin/install -c\n",
            "checking whether build environment is sane... yes\n",
            "checking for a thread-safe mkdir -p... /bin/mkdir -p\n",
            "checking for gawk... no\n",
            "checking for mawk... mawk\n",
            "checking whether make sets $(MAKE)... yes\n",
            "checking for gcc... gcc\n",
            "checking whether the C compiler works... yes\n",
            "checking for C compiler default output file name... a.out\n",
            "checking for suffix of executables... \n",
            "checking whether we are cross compiling... no\n",
            "checking for suffix of object files... o\n",
            "checking whether we are using the GNU C compiler... yes\n",
            "checking whether gcc accepts -g... yes\n",
            "checking for gcc option to accept ISO C89... none needed\n",
            "checking for style of include used by make... GNU\n",
            "checking dependency style of gcc... none\n",
            "checking for g++... g++\n",
            "checking whether we are using the GNU C++ compiler... yes\n",
            "checking whether g++ accepts -g... yes\n",
            "checking dependency style of g++... none\n",
            "checking how to run the C preprocessor... gcc -E\n",
            "checking for grep that handles long lines and -e... /bin/grep\n",
            "checking for egrep... /bin/grep -E\n",
            "checking whether gcc needs -traditional... no\n",
            "checking whether make sets $(MAKE)... (cached) yes\n",
            "checking build system type... x86_64-unknown-linux-gnu\n",
            "checking host system type... x86_64-unknown-linux-gnu\n",
            "checking how to print strings... printf\n",
            "checking for a sed that does not truncate output... /bin/sed\n",
            "checking for fgrep... /bin/grep -F\n",
            "checking for ld used by gcc... /usr/bin/ld\n",
            "checking if the linker (/usr/bin/ld) is GNU ld... yes\n",
            "checking for BSD- or MS-compatible name lister (nm)... /usr/bin/nm -B\n",
            "checking the name lister (/usr/bin/nm -B) interface... BSD nm\n",
            "checking whether ln -s works... yes\n",
            "checking the maximum length of command line arguments... 1572864\n",
            "checking whether the shell understands some XSI constructs... yes\n",
            "checking whether the shell understands \"+=\"... yes\n",
            "checking how to convert x86_64-unknown-linux-gnu file names to x86_64-unknown-linux-gnu format... func_convert_file_noop\n",
            "checking how to convert x86_64-unknown-linux-gnu file names to toolchain format... func_convert_file_noop\n",
            "checking for /usr/bin/ld option to reload object files... -r\n",
            "checking for objdump... objdump\n",
            "checking how to recognize dependent libraries... pass_all\n",
            "checking for dlltool... dlltool\n",
            "checking how to associate runtime and link libraries... printf %s\\n\n",
            "checking for ar... ar\n",
            "checking for archiver @FILE support... @\n",
            "checking for strip... strip\n",
            "checking for ranlib... ranlib\n",
            "checking command to parse /usr/bin/nm -B output from gcc object... ok\n",
            "checking for sysroot... no\n",
            "./configure: line 7378: /usr/bin/file: No such file or directory\n",
            "checking for mt... no\n",
            "checking if : is a manifest tool... no\n",
            "checking for ANSI C header files... yes\n",
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            "checking if gcc supports -fno-rtti -fno-exceptions... no\n",
            "checking for gcc option to produce PIC... -fPIC -DPIC\n",
            "checking if gcc PIC flag -fPIC -DPIC works... yes\n",
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            "checking whether -lc should be explicitly linked in... no\n",
            "checking dynamic linker characteristics... GNU/Linux ld.so\n",
            "checking how to hardcode library paths into programs... immediate\n",
            "checking whether stripping libraries is possible... yes\n",
            "checking if libtool supports shared libraries... yes\n",
            "checking whether to build shared libraries... yes\n",
            "checking whether to build static libraries... yes\n",
            "checking how to run the C++ preprocessor... g++ -E\n",
            "checking for ld used by g++... /usr/bin/ld\n",
            "checking if the linker (/usr/bin/ld) is GNU ld... yes\n",
            "checking whether the g++ linker (/usr/bin/ld) supports shared libraries... yes\n",
            "checking for g++ option to produce PIC... -fPIC -DPIC\n",
            "checking if g++ PIC flag -fPIC -DPIC works... yes\n",
            "checking if g++ static flag -static works... yes\n",
            "checking if g++ supports -c -o file.o... yes\n",
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            "checking whether the g++ linker (/usr/bin/ld) supports shared libraries... yes\n",
            "checking dynamic linker characteristics... (cached) GNU/Linux ld.so\n",
            "checking how to hardcode library paths into programs... immediate\n",
            "checking for library containing strerror... none required\n",
            "checking whether byte ordering is bigendian... no\n",
            "checking for ld used by GCC... /usr/bin/ld\n",
            "checking if the linker (/usr/bin/ld) is GNU ld... yes\n",
            "checking for shared library run path origin... done\n",
            "checking for iconv... yes\n",
            "checking for working iconv... yes\n",
            "checking for iconv declaration... \n",
            "         extern size_t iconv (iconv_t cd, char * *inbuf, size_t *inbytesleft, char * *outbuf, size_t *outbytesleft);\n",
            "checking for ANSI C header files... (cached) yes\n",
            "checking for an ANSI C-conforming const... yes\n",
            "checking whether byte ordering is bigendian... (cached) no\n",
            "checking for string.h... (cached) yes\n",
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            "checking pthread.h presence... yes\n",
            "checking for pthread.h... yes\n",
            "checking for off_t... yes\n",
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            "checking size of char... 1\n",
            "checking size of short... 2\n",
            "checking size of int... 4\n",
            "checking size of long... 8\n",
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            "checking for main in -lstdc++... yes\n",
            "checking for pthread_create in -lpthread... yes\n",
            "checking for pthread_join in -lpthread... yes\n",
            "checking for getenv... yes\n",
            "checking for opendir... yes\n",
            "checking whether make is GNU Make... yes\n",
            "checking if g++ supports stl <vector> (required)... yes\n",
            "checking if g++ supports stl <list> (required)... yes\n",
            "checking if g++ supports stl <map> (required)... yes\n",
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            "checking if g++ supports stl <queue> (required)... yes\n",
            "checking if g++ supports stl <functional> (required)... yes\n",
            "checking if g++ supports stl <algorithm> (required)... yes\n",
            "checking if g++ supports stl <string> (required)... yes\n",
            "checking if g++ supports stl <iostream> (required)... yes\n",
            "checking if g++ supports stl <sstream> (required)... yes\n",
            "checking if g++ supports stl <fstream> (required)... yes\n",
            "checking if g++ supports template <class T> (required)... yes\n",
            "checking if g++ supports const_cast<> (required)... yes\n",
            "checking if g++ supports static_cast<> (required)... yes\n",
            "checking if g++ supports reinterpret_cast<> (required)... yes\n",
            "checking if g++ supports namespaces (required) ... yes\n",
            "checking if g++ supports __thread (optional)... yes\n",
            "checking if g++ supports template <class T> (required)... yes\n",
            "checking if g++ supports GCC native atomic operations (optional)... yes\n",
            "checking if g++ supports OSX native atomic operations (optional)... no\n",
            "checking if g++ environment provides all required features... yes\n",
            "configure: creating ./config.status\n",
            "config.status: creating Makefile\n",
            "config.status: creating src/Makefile\n",
            "config.status: creating src/Makefile.msvc\n",
            "config.status: creating man/Makefile\n",
            "config.status: creating doc/Makefile\n",
            "config.status: creating tests/Makefile\n",
            "config.status: creating swig/version.h\n",
            "config.status: creating mecab.iss\n",
            "config.status: creating mecab-config\n",
            "config.status: creating mecabrc\n",
            "config.status: creating config.h\n",
            "config.status: executing depfiles commands\n",
            "config.status: executing libtool commands\n",
            "config.status: executing default commands\n",
            "+ make\n",
            "make  all-recursive\n",
            "make[1]: Entering directory '/tmp/mecab-0.996-ko-0.9.2'\n",
            "Making all in src\n",
            "make[2]: Entering directory '/tmp/mecab-0.996-ko-0.9.2/src'\n",
            "/bin/bash ../libtool --tag=CXX   --mode=compile g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC=\"\\\"/usr/local/etc/mecabrc\\\"\"    -O3 -Wall  -c -o viterbi.lo viterbi.cpp\n",
            "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c viterbi.cpp  -fPIC -DPIC -o .libs/viterbi.o\n",
            "In file included from \u001b[01m\u001b[Kviterbi.cpp:14:0\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[Kparam.h:30:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K'\u001b[01m\u001b[KTarget {anonymous}::lexical_cast(Source) [with Target = std::__cxx11::basic_string<char>; Source = std::__cxx11::basic_string<char>]\u001b[m\u001b[K' defined but not used [\u001b[01;35m\u001b[K-Wunused-function\u001b[m\u001b[K]\n",
            " std::string \u001b[01;35m\u001b[Klexical_cast<std::string, std::string>\u001b[m\u001b[K(std::string arg) {\n",
            "             \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c viterbi.cpp -o viterbi.o >/dev/null 2>&1\n",
            "/bin/bash ../libtool --tag=CXX   --mode=compile g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC=\"\\\"/usr/local/etc/mecabrc\\\"\"    -O3 -Wall  -c -o tagger.lo tagger.cpp\n",
            "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c tagger.cpp  -fPIC -DPIC -o .libs/tagger.o\n",
            "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c tagger.cpp -o tagger.o >/dev/null 2>&1\n",
            "/bin/bash ../libtool --tag=CXX   --mode=compile g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC=\"\\\"/usr/local/etc/mecabrc\\\"\"    -O3 -Wall  -c -o utils.lo utils.cpp\n",
            "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c utils.cpp  -fPIC -DPIC -o .libs/utils.o\n",
            "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c utils.cpp -o utils.o >/dev/null 2>&1\n",
            "/bin/bash ../libtool --tag=CXX   --mode=compile g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC=\"\\\"/usr/local/etc/mecabrc\\\"\"    -O3 -Wall  -c -o eval.lo eval.cpp\n",
            "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c eval.cpp  -fPIC -DPIC -o .libs/eval.o\n",
            "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c eval.cpp -o eval.o >/dev/null 2>&1\n",
            "/bin/bash ../libtool --tag=CXX   --mode=compile g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC=\"\\\"/usr/local/etc/mecabrc\\\"\"    -O3 -Wall  -c -o iconv_utils.lo iconv_utils.cpp\n",
            "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c iconv_utils.cpp  -fPIC -DPIC -o .libs/iconv_utils.o\n",
            "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c iconv_utils.cpp -o iconv_utils.o >/dev/null 2>&1\n",
            "/bin/bash ../libtool --tag=CXX   --mode=compile g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC=\"\\\"/usr/local/etc/mecabrc\\\"\"    -O3 -Wall  -c -o dictionary_rewriter.lo dictionary_rewriter.cpp\n",
            "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c dictionary_rewriter.cpp  -fPIC -DPIC -o .libs/dictionary_rewriter.o\n",
            "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c dictionary_rewriter.cpp -o dictionary_rewriter.o >/dev/null 2>&1\n",
            "/bin/bash ../libtool --tag=CXX   --mode=compile g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC=\"\\\"/usr/local/etc/mecabrc\\\"\"    -O3 -Wall  -c -o dictionary_generator.lo dictionary_generator.cpp\n",
            "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c dictionary_generator.cpp  -fPIC -DPIC -o .libs/dictionary_generator.o\n",
            "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c dictionary_generator.cpp -o dictionary_generator.o >/dev/null 2>&1\n",
            "/bin/bash ../libtool --tag=CXX   --mode=compile g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC=\"\\\"/usr/local/etc/mecabrc\\\"\"    -O3 -Wall  -c -o dictionary_compiler.lo dictionary_compiler.cpp\n",
            "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c dictionary_compiler.cpp  -fPIC -DPIC -o .libs/dictionary_compiler.o\n",
            "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c dictionary_compiler.cpp -o dictionary_compiler.o >/dev/null 2>&1\n",
            "/bin/bash ../libtool --tag=CXX   --mode=compile g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC=\"\\\"/usr/local/etc/mecabrc\\\"\"    -O3 -Wall  -c -o context_id.lo context_id.cpp\n",
            "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c context_id.cpp  -fPIC -DPIC -o .libs/context_id.o\n",
            "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c context_id.cpp -o context_id.o >/dev/null 2>&1\n",
            "/bin/bash ../libtool --tag=CXX   --mode=compile g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC=\"\\\"/usr/local/etc/mecabrc\\\"\"    -O3 -Wall  -c -o connector.lo connector.cpp\n",
            "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c connector.cpp  -fPIC -DPIC -o .libs/connector.o\n",
            "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c connector.cpp -o connector.o >/dev/null 2>&1\n",
            "/bin/bash ../libtool --tag=CXX   --mode=compile g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC=\"\\\"/usr/local/etc/mecabrc\\\"\"    -O3 -Wall  -c -o nbest_generator.lo nbest_generator.cpp\n",
            "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c nbest_generator.cpp  -fPIC -DPIC -o .libs/nbest_generator.o\n",
            "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c nbest_generator.cpp -o nbest_generator.o >/dev/null 2>&1\n",
            "/bin/bash ../libtool --tag=CXX   --mode=compile g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC=\"\\\"/usr/local/etc/mecabrc\\\"\"    -O3 -Wall  -c -o writer.lo writer.cpp\n",
            "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c writer.cpp  -fPIC -DPIC -o .libs/writer.o\n",
            "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c writer.cpp -o writer.o >/dev/null 2>&1\n",
            "/bin/bash ../libtool --tag=CXX   --mode=compile g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC=\"\\\"/usr/local/etc/mecabrc\\\"\"    -O3 -Wall  -c -o string_buffer.lo string_buffer.cpp\n",
            "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c string_buffer.cpp  -fPIC -DPIC -o .libs/string_buffer.o\n",
            "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c string_buffer.cpp -o string_buffer.o >/dev/null 2>&1\n",
            "/bin/bash ../libtool --tag=CXX   --mode=compile g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC=\"\\\"/usr/local/etc/mecabrc\\\"\"    -O3 -Wall  -c -o param.lo param.cpp\n",
            "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c param.cpp  -fPIC -DPIC -o .libs/param.o\n",
            "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c param.cpp -o param.o >/dev/null 2>&1\n",
            "/bin/bash ../libtool --tag=CXX   --mode=compile g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC=\"\\\"/usr/local/etc/mecabrc\\\"\"    -O3 -Wall  -c -o tokenizer.lo tokenizer.cpp\n",
            "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c tokenizer.cpp  -fPIC -DPIC -o .libs/tokenizer.o\n",
            "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c tokenizer.cpp -o tokenizer.o >/dev/null 2>&1\n",
            "/bin/bash ../libtool --tag=CXX   --mode=compile g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC=\"\\\"/usr/local/etc/mecabrc\\\"\"    -O3 -Wall  -c -o char_property.lo char_property.cpp\n",
            "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c char_property.cpp  -fPIC -DPIC -o .libs/char_property.o\n",
            "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c char_property.cpp -o char_property.o >/dev/null 2>&1\n",
            "/bin/bash ../libtool --tag=CXX   --mode=compile g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC=\"\\\"/usr/local/etc/mecabrc\\\"\"    -O3 -Wall  -c -o dictionary.lo dictionary.cpp\n",
            "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c dictionary.cpp  -fPIC -DPIC -o .libs/dictionary.o\n",
            "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c dictionary.cpp -o dictionary.o >/dev/null 2>&1\n",
            "/bin/bash ../libtool --tag=CXX   --mode=compile g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC=\"\\\"/usr/local/etc/mecabrc\\\"\"    -O3 -Wall  -c -o feature_index.lo feature_index.cpp\n",
            "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c feature_index.cpp  -fPIC -DPIC -o .libs/feature_index.o\n",
            "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c feature_index.cpp -o feature_index.o >/dev/null 2>&1\n",
            "/bin/bash ../libtool --tag=CXX   --mode=compile g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC=\"\\\"/usr/local/etc/mecabrc\\\"\"    -O3 -Wall  -c -o lbfgs.lo lbfgs.cpp\n",
            "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c lbfgs.cpp  -fPIC -DPIC -o .libs/lbfgs.o\n",
            "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c lbfgs.cpp -o lbfgs.o >/dev/null 2>&1\n",
            "/bin/bash ../libtool --tag=CXX   --mode=compile g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC=\"\\\"/usr/local/etc/mecabrc\\\"\"    -O3 -Wall  -c -o learner_tagger.lo learner_tagger.cpp\n",
            "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c learner_tagger.cpp  -fPIC -DPIC -o .libs/learner_tagger.o\n",
            "\u001b[01m\u001b[Klearner_tagger.cpp:25:7:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K'\u001b[01m\u001b[Kchar* MeCab::{anonymous}::mystrdup(const string&)\u001b[m\u001b[K' defined but not used [\u001b[01;35m\u001b[K-Wunused-function\u001b[m\u001b[K]\n",
            " char *\u001b[01;35m\u001b[Kmystrdup\u001b[m\u001b[K(const std::string &str) {\n",
            "       \u001b[01;35m\u001b[K^~~~~~~~\u001b[m\u001b[K\n",
            "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c learner_tagger.cpp -o learner_tagger.o >/dev/null 2>&1\n",
            "/bin/bash ../libtool --tag=CXX   --mode=compile g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC=\"\\\"/usr/local/etc/mecabrc\\\"\"    -O3 -Wall  -c -o learner.lo learner.cpp\n",
            "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c learner.cpp  -fPIC -DPIC -o .libs/learner.o\n",
            "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c learner.cpp -o learner.o >/dev/null 2>&1\n",
            "/bin/bash ../libtool --tag=CXX   --mode=compile g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC=\"\\\"/usr/local/etc/mecabrc\\\"\"    -O3 -Wall  -c -o libmecab.lo libmecab.cpp\n",
            "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c libmecab.cpp  -fPIC -DPIC -o .libs/libmecab.o\n",
            "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c libmecab.cpp -o libmecab.o >/dev/null 2>&1\n",
            "/bin/bash ../libtool --tag=CXX   --mode=link g++  -O3 -Wall  -no-undefined -version-info 2:0:0  -o libmecab.la -rpath /usr/local/lib viterbi.lo tagger.lo utils.lo eval.lo iconv_utils.lo dictionary_rewriter.lo dictionary_generator.lo dictionary_compiler.lo context_id.lo connector.lo nbest_generator.lo writer.lo string_buffer.lo param.lo tokenizer.lo char_property.lo dictionary.lo feature_index.lo lbfgs.lo learner_tagger.lo learner.lo libmecab.lo  -lpthread -lpthread  -lstdc++ \n",
            "libtool: link: g++  -fPIC -DPIC -shared -nostdlib /usr/lib/gcc/x86_64-linux-gnu/7/../../../x86_64-linux-gnu/crti.o /usr/lib/gcc/x86_64-linux-gnu/7/crtbeginS.o  .libs/viterbi.o .libs/tagger.o .libs/utils.o .libs/eval.o .libs/iconv_utils.o .libs/dictionary_rewriter.o .libs/dictionary_generator.o .libs/dictionary_compiler.o .libs/context_id.o .libs/connector.o .libs/nbest_generator.o .libs/writer.o .libs/string_buffer.o .libs/param.o .libs/tokenizer.o .libs/char_property.o .libs/dictionary.o .libs/feature_index.o .libs/lbfgs.o .libs/learner_tagger.o .libs/learner.o .libs/libmecab.o   -lpthread -L/usr/lib/gcc/x86_64-linux-gnu/7 -L/usr/lib/gcc/x86_64-linux-gnu/7/../../../x86_64-linux-gnu -L/usr/lib/gcc/x86_64-linux-gnu/7/../../../../lib -L/lib/x86_64-linux-gnu -L/lib/../lib -L/usr/lib/x86_64-linux-gnu -L/usr/lib/../lib -L/usr/local/cuda/lib64/stubs -L/usr/lib/gcc/x86_64-linux-gnu/7/../../.. -lstdc++ -lm -lc -lgcc_s /usr/lib/gcc/x86_64-linux-gnu/7/crtendS.o /usr/lib/gcc/x86_64-linux-gnu/7/../../../x86_64-linux-gnu/crtn.o  -O3   -Wl,-soname -Wl,libmecab.so.2 -o .libs/libmecab.so.2.0.0\n",
            "libtool: link: (cd \".libs\" && rm -f \"libmecab.so.2\" && ln -s \"libmecab.so.2.0.0\" \"libmecab.so.2\")\n",
            "libtool: link: (cd \".libs\" && rm -f \"libmecab.so\" && ln -s \"libmecab.so.2.0.0\" \"libmecab.so\")\n",
            "libtool: link: ar cru .libs/libmecab.a  viterbi.o tagger.o utils.o eval.o iconv_utils.o dictionary_rewriter.o dictionary_generator.o dictionary_compiler.o context_id.o connector.o nbest_generator.o writer.o string_buffer.o param.o tokenizer.o char_property.o dictionary.o feature_index.o lbfgs.o learner_tagger.o learner.o libmecab.o\n",
            "ar: `u' modifier ignored since `D' is the default (see `U')\n",
            "libtool: link: ranlib .libs/libmecab.a\n",
            "libtool: link: ( cd \".libs\" && rm -f \"libmecab.la\" && ln -s \"../libmecab.la\" \"libmecab.la\" )\n",
            "g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC=\"\\\"/usr/local/etc/mecabrc\\\"\"    -O3 -Wall  -c -o mecab.o mecab.cpp\n",
            "/bin/bash ../libtool --tag=CXX   --mode=link g++  -O3 -Wall    -o mecab mecab.o libmecab.la -lpthread -lpthread  -lstdc++ \n",
            "libtool: link: g++ -O3 -Wall -o .libs/mecab mecab.o  ./.libs/libmecab.so -lpthread -lstdc++\n",
            "g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC=\"\\\"/usr/local/etc/mecabrc\\\"\"    -O3 -Wall  -c -o mecab-dict-index.o mecab-dict-index.cpp\n",
            "/bin/bash ../libtool --tag=CXX   --mode=link g++  -O3 -Wall    -o mecab-dict-index mecab-dict-index.o libmecab.la -lpthread -lpthread  -lstdc++ \n",
            "libtool: link: g++ -O3 -Wall -o .libs/mecab-dict-index mecab-dict-index.o  ./.libs/libmecab.so -lpthread -lstdc++\n",
            "g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC=\"\\\"/usr/local/etc/mecabrc\\\"\"    -O3 -Wall  -c -o mecab-dict-gen.o mecab-dict-gen.cpp\n",
            "/bin/bash ../libtool --tag=CXX   --mode=link g++  -O3 -Wall    -o mecab-dict-gen mecab-dict-gen.o libmecab.la -lpthread -lpthread  -lstdc++ \n",
            "libtool: link: g++ -O3 -Wall -o .libs/mecab-dict-gen mecab-dict-gen.o  ./.libs/libmecab.so -lpthread -lstdc++\n",
            "g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC=\"\\\"/usr/local/etc/mecabrc\\\"\"    -O3 -Wall  -c -o mecab-cost-train.o mecab-cost-train.cpp\n",
            "/bin/bash ../libtool --tag=CXX   --mode=link g++  -O3 -Wall    -o mecab-cost-train mecab-cost-train.o libmecab.la -lpthread -lpthread  -lstdc++ \n",
            "libtool: link: g++ -O3 -Wall -o .libs/mecab-cost-train mecab-cost-train.o  ./.libs/libmecab.so -lpthread -lstdc++\n",
            "g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC=\"\\\"/usr/local/etc/mecabrc\\\"\"    -O3 -Wall  -c -o mecab-system-eval.o mecab-system-eval.cpp\n",
            "/bin/bash ../libtool --tag=CXX   --mode=link g++  -O3 -Wall    -o mecab-system-eval mecab-system-eval.o libmecab.la -lpthread -lpthread  -lstdc++ \n",
            "libtool: link: g++ -O3 -Wall -o .libs/mecab-system-eval mecab-system-eval.o  ./.libs/libmecab.so -lpthread -lstdc++\n",
            "g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC=\"\\\"/usr/local/etc/mecabrc\\\"\"    -O3 -Wall  -c -o mecab-test-gen.o mecab-test-gen.cpp\n",
            "/bin/bash ../libtool --tag=CXX   --mode=link g++  -O3 -Wall    -o mecab-test-gen mecab-test-gen.o libmecab.la -lpthread -lpthread  -lstdc++ \n",
            "libtool: link: g++ -O3 -Wall -o .libs/mecab-test-gen mecab-test-gen.o  ./.libs/libmecab.so -lpthread -lstdc++\n",
            "make[2]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2/src'\n",
            "Making all in man\n",
            "make[2]: Entering directory '/tmp/mecab-0.996-ko-0.9.2/man'\n",
            "make[2]: Nothing to be done for 'all'.\n",
            "make[2]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2/man'\n",
            "Making all in doc\n",
            "make[2]: Entering directory '/tmp/mecab-0.996-ko-0.9.2/doc'\n",
            "make[2]: Nothing to be done for 'all'.\n",
            "make[2]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2/doc'\n",
            "Making all in tests\n",
            "make[2]: Entering directory '/tmp/mecab-0.996-ko-0.9.2/tests'\n",
            "make[2]: Nothing to be done for 'all'.\n",
            "make[2]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2/tests'\n",
            "make[2]: Entering directory '/tmp/mecab-0.996-ko-0.9.2'\n",
            "make[2]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2'\n",
            "make[1]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2'\n",
            "+ make check\n",
            "Making check in src\n",
            "make[1]: Entering directory '/tmp/mecab-0.996-ko-0.9.2/src'\n",
            "make[1]: Nothing to be done for 'check'.\n",
            "make[1]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2/src'\n",
            "Making check in man\n",
            "make[1]: Entering directory '/tmp/mecab-0.996-ko-0.9.2/man'\n",
            "make[1]: Nothing to be done for 'check'.\n",
            "make[1]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2/man'\n",
            "Making check in doc\n",
            "make[1]: Entering directory '/tmp/mecab-0.996-ko-0.9.2/doc'\n",
            "make[1]: Nothing to be done for 'check'.\n",
            "make[1]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2/doc'\n",
            "Making check in tests\n",
            "make[1]: Entering directory '/tmp/mecab-0.996-ko-0.9.2/tests'\n",
            "make  check-TESTS\n",
            "make[2]: Entering directory '/tmp/mecab-0.996-ko-0.9.2/tests'\n",
            "./pos-id.def is not found. minimum setting is used\n",
            "reading ./unk.def ... 2\n",
            "emitting double-array: 100% |###########################################| \n",
            "./model.def is not found. skipped.\n",
            "./pos-id.def is not found. minimum setting is used\n",
            "reading ./dic.csv ... 177\n",
            "emitting double-array: 100% |###########################################| \n",
            "reading ./matrix.def ... 178x178\n",
            "emitting matrix      : 100% |###########################################| \n",
            "\n",
            "done!\n",
            "./pos-id.def is not found. minimum setting is used\n",
            "reading ./unk.def ... 2\n",
            "emitting double-array: 100% |###########################################| \n",
            "./model.def is not found. skipped.\n",
            "./pos-id.def is not found. minimum setting is used\n",
            "reading ./dic.csv ... 83\n",
            "emitting double-array: 100% |###########################################| \n",
            "reading ./matrix.def ... 84x84\n",
            "emitting matrix      : 100% |###########################################| \n",
            "\n",
            "done!\n",
            "./pos-id.def is not found. minimum setting is used\n",
            "reading ./unk.def ... 2\n",
            "emitting double-array: 100% |###########################################| \n",
            "./model.def is not found. skipped.\n",
            "./pos-id.def is not found. minimum setting is used\n",
            "reading ./dic.csv ... 450\n",
            "emitting double-array: 100% |###########################################| \n",
            "reading ./matrix.def ... 1x1\n",
            "\n",
            "done!\n",
            "./pos-id.def is not found. minimum setting is used\n",
            "reading ./unk.def ... 2\n",
            "emitting double-array: 100% |###########################################| \n",
            "./model.def is not found. skipped.\n",
            "./pos-id.def is not found. minimum setting is used\n",
            "reading ./dic.csv ... 162\n",
            "emitting double-array: 100% |###########################################| \n",
            "reading ./matrix.def ... 3x3\n",
            "emitting matrix      : 100% |###########################################| \n",
            "\n",
            "done!\n",
            "./pos-id.def is not found. minimum setting is used\n",
            "reading ./unk.def ... 2\n",
            "emitting double-array: 100% |###########################################| \n",
            "./model.def is not found. skipped.\n",
            "./pos-id.def is not found. minimum setting is used\n",
            "reading ./dic.csv ... 4\n",
            "emitting double-array: 100% |###########################################| \n",
            "reading ./matrix.def ... 1x1\n",
            "\n",
            "done!\n",
            "./pos-id.def is not found. minimum setting is used\n",
            "reading ./unk.def ... 11\n",
            "emitting double-array: 100% |###########################################| \n",
            "./model.def is not found. skipped.\n",
            "./pos-id.def is not found. minimum setting is used\n",
            "reading ./dic.csv ... 1\n",
            "reading ./matrix.def ... 1x1\n",
            "\n",
            "done!\n",
            "./pos-id.def is not found. minimum setting is used\n",
            "reading ./unk.def ... 2\n",
            "emitting double-array: 100% |###########################################| \n",
            "./model.def is not found. skipped.\n",
            "./pos-id.def is not found. minimum setting is used\n",
            "reading ./dic.csv ... 1\n",
            "reading ./matrix.def ... 1x1\n",
            "\n",
            "done!\n",
            "PASS: run-dics.sh\n",
            "PASS: run-eval.sh\n",
            "seed/pos-id.def is not found. minimum setting is used\n",
            "reading seed/unk.def ... 40\n",
            "emitting double-array: 100% |###########################################| \n",
            "seed/model.def is not found. skipped.\n",
            "seed/pos-id.def is not found. minimum setting is used\n",
            "reading seed/dic.csv ... 4335\n",
            "emitting double-array: 100% |###########################################| \n",
            "reading seed/matrix.def ... 1x1\n",
            "\n",
            "done!\n",
            "reading corpus ...\n",
            "Number of sentences: 34\n",
            "Number of features:  64108\n",
            "eta:                 0.00005\n",
            "freq:                1\n",
            "eval-size:           6\n",
            "unk-eval-size:       4\n",
            "threads:             1\n",
            "charset:             EUC-JP\n",
            "C(sigma^2):          1.00000\n",
            "\n",
            "iter=0 err=1.00000 F=0.35771 target=2406.28355 diff=1.00000\n",
            "iter=1 err=0.97059 F=0.65652 target=1484.25231 diff=0.38318\n",
            "iter=2 err=0.91176 F=0.79331 target=863.32765 diff=0.41834\n",
            "iter=3 err=0.85294 F=0.89213 target=596.72480 diff=0.30881\n",
            "iter=4 err=0.61765 F=0.95467 target=336.30744 diff=0.43641\n",
            "iter=5 err=0.50000 F=0.96702 target=246.53039 diff=0.26695\n",
            "iter=6 err=0.35294 F=0.95472 target=188.93963 diff=0.23361\n",
            "iter=7 err=0.20588 F=0.99106 target=168.62665 diff=0.10751\n",
            "iter=8 err=0.05882 F=0.99777 target=158.64865 diff=0.05917\n",
            "iter=9 err=0.08824 F=0.99665 target=154.14530 diff=0.02839\n",
            "iter=10 err=0.08824 F=0.99665 target=151.94257 diff=0.01429\n",
            "iter=11 err=0.02941 F=0.99888 target=147.20825 diff=0.03116\n",
            "iter=12 err=0.00000 F=1.00000 target=147.34956 diff=0.00096\n",
            "iter=13 err=0.02941 F=0.99888 target=146.32592 diff=0.00695\n",
            "iter=14 err=0.00000 F=1.00000 target=145.77299 diff=0.00378\n",
            "iter=15 err=0.02941 F=0.99888 target=145.24641 diff=0.00361\n",
            "iter=16 err=0.00000 F=1.00000 target=144.96490 diff=0.00194\n",
            "iter=17 err=0.02941 F=0.99888 target=144.90246 diff=0.00043\n",
            "iter=18 err=0.00000 F=1.00000 target=144.75959 diff=0.00099\n",
            "iter=19 err=0.00000 F=1.00000 target=144.71727 diff=0.00029\n",
            "iter=20 err=0.00000 F=1.00000 target=144.66337 diff=0.00037\n",
            "iter=21 err=0.00000 F=1.00000 target=144.61349 diff=0.00034\n",
            "iter=22 err=0.00000 F=1.00000 target=144.62987 diff=0.00011\n",
            "iter=23 err=0.00000 F=1.00000 target=144.60060 diff=0.00020\n",
            "iter=24 err=0.00000 F=1.00000 target=144.59125 diff=0.00006\n",
            "iter=25 err=0.00000 F=1.00000 target=144.58619 diff=0.00004\n",
            "iter=26 err=0.00000 F=1.00000 target=144.58219 diff=0.00003\n",
            "iter=27 err=0.00000 F=1.00000 target=144.58059 diff=0.00001\n",
            "\n",
            "Done! writing model file ... \n",
            "model-ipadic.c1.0.f1.model is not a binary model. reopen it as text mode...\n",
            "reading seed/unk.def ... 40\n",
            "reading seed/dic.csv ... 4335\n",
            "emitting model-ipadic.c1.0.f1.dic/left-id.def/ model-ipadic.c1.0.f1.dic/right-id.def\n",
            "emitting model-ipadic.c1.0.f1.dic/unk.def ... 40\n",
            "emitting model-ipadic.c1.0.f1.dic/dic.csv ... 4335\n",
            "emitting matrix      : 100% |###########################################| \n",
            "copying seed/char.def to model-ipadic.c1.0.f1.dic/char.def\n",
            "copying seed/rewrite.def to model-ipadic.c1.0.f1.dic/rewrite.def\n",
            "copying seed/dicrc to model-ipadic.c1.0.f1.dic/dicrc\n",
            "copying seed/feature.def to model-ipadic.c1.0.f1.dic/feature.def\n",
            "copying model-ipadic.c1.0.f1.model to model-ipadic.c1.0.f1.dic/model.def\n",
            "\n",
            "done!\n",
            "model-ipadic.c1.0.f1.dic/pos-id.def is not found. minimum setting is used\n",
            "reading model-ipadic.c1.0.f1.dic/unk.def ... 40\n",
            "emitting double-array: 100% |###########################################| \n",
            "model-ipadic.c1.0.f1.dic/pos-id.def is not found. minimum setting is used\n",
            "reading model-ipadic.c1.0.f1.dic/dic.csv ... 4335\n",
            "emitting double-array: 100% |###########################################| \n",
            "reading model-ipadic.c1.0.f1.dic/matrix.def ... 346x346\n",
            "emitting matrix      : 100% |###########################################| \n",
            "\n",
            "done!\n",
            "              precision          recall         F\n",
            "LEVEL 0:    12.8959(57/442) 11.8998(57/479) 12.3779\n",
            "LEVEL 1:    12.2172(54/442) 11.2735(54/479) 11.7264\n",
            "LEVEL 2:    11.7647(52/442) 10.8559(52/479) 11.2921\n",
            "LEVEL 4:    11.7647(52/442) 10.8559(52/479) 11.2921\n",
            "PASS: run-cost-train.sh\n",
            "==================\n",
            "All 3 tests passed\n",
            "==================\n",
            "make[2]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2/tests'\n",
            "make[1]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2/tests'\n",
            "make[1]: Entering directory '/tmp/mecab-0.996-ko-0.9.2'\n",
            "make[1]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2'\n",
            "+ sudo make install\n",
            "Making install in src\n",
            "make[1]: Entering directory '/tmp/mecab-0.996-ko-0.9.2/src'\n",
            "make[2]: Entering directory '/tmp/mecab-0.996-ko-0.9.2/src'\n",
            "test -z \"/usr/local/lib\" || /bin/mkdir -p \"/usr/local/lib\"\n",
            " /bin/bash ../libtool   --mode=install /usr/bin/install -c   libmecab.la '/usr/local/lib'\n",
            "libtool: install: /usr/bin/install -c .libs/libmecab.so.2.0.0 /usr/local/lib/libmecab.so.2.0.0\n",
            "libtool: install: (cd /usr/local/lib && { ln -s -f libmecab.so.2.0.0 libmecab.so.2 || { rm -f libmecab.so.2 && ln -s libmecab.so.2.0.0 libmecab.so.2; }; })\n",
            "libtool: install: (cd /usr/local/lib && { ln -s -f libmecab.so.2.0.0 libmecab.so || { rm -f libmecab.so && ln -s libmecab.so.2.0.0 libmecab.so; }; })\n",
            "libtool: install: /usr/bin/install -c .libs/libmecab.lai /usr/local/lib/libmecab.la\n",
            "libtool: install: /usr/bin/install -c .libs/libmecab.a /usr/local/lib/libmecab.a\n",
            "libtool: install: chmod 644 /usr/local/lib/libmecab.a\n",
            "libtool: install: ranlib /usr/local/lib/libmecab.a\n",
            "libtool: finish: PATH=\"/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/snap/bin:/sbin\" ldconfig -n /usr/local/lib\n",
            "----------------------------------------------------------------------\n",
            "Libraries have been installed in:\n",
            "   /usr/local/lib\n",
            "\n",
            "If you ever happen to want to link against installed libraries\n",
            "in a given directory, LIBDIR, you must either use libtool, and\n",
            "specify the full pathname of the library, or use the `-LLIBDIR'\n",
            "flag during linking and do at least one of the following:\n",
            "   - add LIBDIR to the `LD_LIBRARY_PATH' environment variable\n",
            "     during execution\n",
            "   - add LIBDIR to the `LD_RUN_PATH' environment variable\n",
            "     during linking\n",
            "   - use the `-Wl,-rpath -Wl,LIBDIR' linker flag\n",
            "   - have your system administrator add LIBDIR to `/etc/ld.so.conf'\n",
            "\n",
            "See any operating system documentation about shared libraries for\n",
            "more information, such as the ld(1) and ld.so(8) manual pages.\n",
            "----------------------------------------------------------------------\n",
            "test -z \"/usr/local/bin\" || /bin/mkdir -p \"/usr/local/bin\"\n",
            "  /bin/bash ../libtool   --mode=install /usr/bin/install -c mecab '/usr/local/bin'\n",
            "libtool: install: /usr/bin/install -c .libs/mecab /usr/local/bin/mecab\n",
            "test -z \"/usr/local/libexec/mecab\" || /bin/mkdir -p \"/usr/local/libexec/mecab\"\n",
            "  /bin/bash ../libtool   --mode=install /usr/bin/install -c mecab-dict-index mecab-dict-gen mecab-cost-train mecab-system-eval mecab-test-gen '/usr/local/libexec/mecab'\n",
            "libtool: install: /usr/bin/install -c .libs/mecab-dict-index /usr/local/libexec/mecab/mecab-dict-index\n",
            "libtool: install: /usr/bin/install -c .libs/mecab-dict-gen /usr/local/libexec/mecab/mecab-dict-gen\n",
            "libtool: install: /usr/bin/install -c .libs/mecab-cost-train /usr/local/libexec/mecab/mecab-cost-train\n",
            "libtool: install: /usr/bin/install -c .libs/mecab-system-eval /usr/local/libexec/mecab/mecab-system-eval\n",
            "libtool: install: /usr/bin/install -c .libs/mecab-test-gen /usr/local/libexec/mecab/mecab-test-gen\n",
            "test -z \"/usr/local/include\" || /bin/mkdir -p \"/usr/local/include\"\n",
            " /usr/bin/install -c -m 644 mecab.h '/usr/local/include'\n",
            "make[2]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2/src'\n",
            "make[1]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2/src'\n",
            "Making install in man\n",
            "make[1]: Entering directory '/tmp/mecab-0.996-ko-0.9.2/man'\n",
            "make[2]: Entering directory '/tmp/mecab-0.996-ko-0.9.2/man'\n",
            "make[2]: Nothing to be done for 'install-exec-am'.\n",
            "test -z \"/usr/local/share/man/man1\" || /bin/mkdir -p \"/usr/local/share/man/man1\"\n",
            " /usr/bin/install -c -m 644 mecab.1 '/usr/local/share/man/man1'\n",
            "make[2]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2/man'\n",
            "make[1]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2/man'\n",
            "Making install in doc\n",
            "make[1]: Entering directory '/tmp/mecab-0.996-ko-0.9.2/doc'\n",
            "make[2]: Entering directory '/tmp/mecab-0.996-ko-0.9.2/doc'\n",
            "make[2]: Nothing to be done for 'install-exec-am'.\n",
            "make[2]: Nothing to be done for 'install-data-am'.\n",
            "make[2]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2/doc'\n",
            "make[1]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2/doc'\n",
            "Making install in tests\n",
            "make[1]: Entering directory '/tmp/mecab-0.996-ko-0.9.2/tests'\n",
            "make[2]: Entering directory '/tmp/mecab-0.996-ko-0.9.2/tests'\n",
            "make[2]: Nothing to be done for 'install-exec-am'.\n",
            "make[2]: Nothing to be done for 'install-data-am'.\n",
            "make[2]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2/tests'\n",
            "make[1]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2/tests'\n",
            "make[1]: Entering directory '/tmp/mecab-0.996-ko-0.9.2'\n",
            "make[2]: Entering directory '/tmp/mecab-0.996-ko-0.9.2'\n",
            "test -z \"/usr/local/bin\" || /bin/mkdir -p \"/usr/local/bin\"\n",
            " /usr/bin/install -c mecab-config '/usr/local/bin'\n",
            "test -z \"/usr/local/etc\" || /bin/mkdir -p \"/usr/local/etc\"\n",
            " /usr/bin/install -c -m 644 mecabrc '/usr/local/etc'\n",
            "make[2]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2'\n",
            "make[1]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2'\n",
            "+ [[ -d /usr/local/lib/mecab/dic/mecab-ko-dic ]]\n",
            "+ echo 'Install mecab-ko-dic'\n",
            "Install mecab-ko-dic\n",
            "+ install_mecab_ko_dic\n",
            "+ echo 'Install mecab-ko-dic'\n",
            "Install mecab-ko-dic\n",
            "+ cd /tmp\n",
            "+ curl -LO https://bitbucket.org/eunjeon/mecab-ko-dic/downloads/mecab-ko-dic-2.1.1-20180720.tar.gz\n",
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\n",
            "100 47.4M  100 47.4M    0     0  60.7M      0 --:--:-- --:--:-- --:--:-- 60.7M\n",
            "+ tar -zxvf mecab-ko-dic-2.1.1-20180720.tar.gz\n",
            "mecab-ko-dic-2.1.1-20180720/\n",
            "mecab-ko-dic-2.1.1-20180720/configure\n",
            "mecab-ko-dic-2.1.1-20180720/COPYING\n",
            "mecab-ko-dic-2.1.1-20180720/autogen.sh\n",
            "mecab-ko-dic-2.1.1-20180720/Place-station.csv\n",
            "mecab-ko-dic-2.1.1-20180720/NNG.csv\n",
            "mecab-ko-dic-2.1.1-20180720/README\n",
            "mecab-ko-dic-2.1.1-20180720/EF.csv\n",
            "mecab-ko-dic-2.1.1-20180720/MAG.csv\n",
            "mecab-ko-dic-2.1.1-20180720/Preanalysis.csv\n",
            "mecab-ko-dic-2.1.1-20180720/NNB.csv\n",
            "mecab-ko-dic-2.1.1-20180720/Person-actor.csv\n",
            "mecab-ko-dic-2.1.1-20180720/VV.csv\n",
            "mecab-ko-dic-2.1.1-20180720/Makefile.in\n",
            "mecab-ko-dic-2.1.1-20180720/matrix.def\n",
            "mecab-ko-dic-2.1.1-20180720/EC.csv\n",
            "mecab-ko-dic-2.1.1-20180720/NNBC.csv\n",
            "mecab-ko-dic-2.1.1-20180720/clean\n",
            "mecab-ko-dic-2.1.1-20180720/ChangeLog\n",
            "mecab-ko-dic-2.1.1-20180720/J.csv\n",
            "mecab-ko-dic-2.1.1-20180720/.keep\n",
            "mecab-ko-dic-2.1.1-20180720/feature.def\n",
            "mecab-ko-dic-2.1.1-20180720/Foreign.csv\n",
            "mecab-ko-dic-2.1.1-20180720/XPN.csv\n",
            "mecab-ko-dic-2.1.1-20180720/EP.csv\n",
            "mecab-ko-dic-2.1.1-20180720/NR.csv\n",
            "mecab-ko-dic-2.1.1-20180720/left-id.def\n",
            "mecab-ko-dic-2.1.1-20180720/Place.csv\n",
            "mecab-ko-dic-2.1.1-20180720/Symbol.csv\n",
            "mecab-ko-dic-2.1.1-20180720/dicrc\n",
            "mecab-ko-dic-2.1.1-20180720/NP.csv\n",
            "mecab-ko-dic-2.1.1-20180720/ETM.csv\n",
            "mecab-ko-dic-2.1.1-20180720/IC.csv\n",
            "mecab-ko-dic-2.1.1-20180720/Place-address.csv\n",
            "mecab-ko-dic-2.1.1-20180720/Group.csv\n",
            "mecab-ko-dic-2.1.1-20180720/model.def\n",
            "mecab-ko-dic-2.1.1-20180720/XSN.csv\n",
            "mecab-ko-dic-2.1.1-20180720/INSTALL\n",
            "mecab-ko-dic-2.1.1-20180720/rewrite.def\n",
            "mecab-ko-dic-2.1.1-20180720/Inflect.csv\n",
            "mecab-ko-dic-2.1.1-20180720/configure.ac\n",
            "mecab-ko-dic-2.1.1-20180720/NNP.csv\n",
            "mecab-ko-dic-2.1.1-20180720/CoinedWord.csv\n",
            "mecab-ko-dic-2.1.1-20180720/XSV.csv\n",
            "mecab-ko-dic-2.1.1-20180720/pos-id.def\n",
            "mecab-ko-dic-2.1.1-20180720/Makefile.am\n",
            "mecab-ko-dic-2.1.1-20180720/unk.def\n",
            "mecab-ko-dic-2.1.1-20180720/missing\n",
            "mecab-ko-dic-2.1.1-20180720/VCP.csv\n",
            "mecab-ko-dic-2.1.1-20180720/install-sh\n",
            "mecab-ko-dic-2.1.1-20180720/Hanja.csv\n",
            "mecab-ko-dic-2.1.1-20180720/MAJ.csv\n",
            "mecab-ko-dic-2.1.1-20180720/XSA.csv\n",
            "mecab-ko-dic-2.1.1-20180720/Wikipedia.csv\n",
            "mecab-ko-dic-2.1.1-20180720/tools/\n",
            "mecab-ko-dic-2.1.1-20180720/tools/add-userdic.sh\n",
            "mecab-ko-dic-2.1.1-20180720/tools/mecab-bestn.sh\n",
            "mecab-ko-dic-2.1.1-20180720/tools/convert_for_using_store.sh\n",
            "mecab-ko-dic-2.1.1-20180720/user-dic/\n",
            "mecab-ko-dic-2.1.1-20180720/user-dic/nnp.csv\n",
            "mecab-ko-dic-2.1.1-20180720/user-dic/place.csv\n",
            "mecab-ko-dic-2.1.1-20180720/user-dic/person.csv\n",
            "mecab-ko-dic-2.1.1-20180720/user-dic/README.md\n",
            "mecab-ko-dic-2.1.1-20180720/NorthKorea.csv\n",
            "mecab-ko-dic-2.1.1-20180720/VX.csv\n",
            "mecab-ko-dic-2.1.1-20180720/right-id.def\n",
            "mecab-ko-dic-2.1.1-20180720/VA.csv\n",
            "mecab-ko-dic-2.1.1-20180720/char.def\n",
            "mecab-ko-dic-2.1.1-20180720/NEWS\n",
            "mecab-ko-dic-2.1.1-20180720/MM.csv\n",
            "mecab-ko-dic-2.1.1-20180720/ETN.csv\n",
            "mecab-ko-dic-2.1.1-20180720/AUTHORS\n",
            "mecab-ko-dic-2.1.1-20180720/Person.csv\n",
            "mecab-ko-dic-2.1.1-20180720/XR.csv\n",
            "mecab-ko-dic-2.1.1-20180720/VCN.csv\n",
            "+ cd mecab-ko-dic-2.1.1-20180720\n",
            "+ ./autogen.sh\n",
            "Looking in current directory for macros.\n",
            "configure.ac:2: warning: AM_INIT_AUTOMAKE: two- and three-arguments forms are deprecated.  For more info, see:\n",
            "configure.ac:2: http://www.gnu.org/software/automake/manual/automake.html#Modernize-AM_005fINIT_005fAUTOMAKE-invocation\n",
            "+ ./configure\n",
            "checking for a BSD-compatible install... /usr/bin/install -c\n",
            "checking whether build environment is sane... yes\n",
            "/tmp/mecab-ko-dic-2.1.1-20180720/missing: Unknown `--is-lightweight' option\n",
            "Try `/tmp/mecab-ko-dic-2.1.1-20180720/missing --help' for more information\n",
            "configure: WARNING: 'missing' script is too old or missing\n",
            "checking for a thread-safe mkdir -p... /bin/mkdir -p\n",
            "checking for gawk... no\n",
            "checking for mawk... mawk\n",
            "checking whether make sets $(MAKE)... yes\n",
            "checking whether make supports nested variables... yes\n",
            "checking for mecab-config... /usr/local/bin/mecab-config\n",
            "checking that generated files are newer than configure... done\n",
            "configure: creating ./config.status\n",
            "config.status: creating Makefile\n",
            "+ [[ Linux == \\L\\i\\n\\u\\x ]]\n",
            "+ mecab-config --libs-only-L\n",
            "+ sudo tee /etc/ld.so.conf.d/mecab.conf\n",
            "/usr/local/lib\n",
            "+ sudo ldconfig\n",
            "/sbin/ldconfig.real: /usr/local/lib/python3.6/dist-packages/ideep4py/lib/libmkldnn.so.0 is not a symbolic link\n",
            "\n",
            "+ make\n",
            "/usr/local/libexec/mecab/mecab-dict-index -d . -o . -f UTF-8 -t UTF-8\n",
            "reading ./unk.def ... 13\n",
            "emitting double-array: 100% |###########################################| \n",
            "reading ./Place-address.csv ... 19301\n",
            "reading ./XSN.csv ... 124\n",
            "reading ./XPN.csv ... 83\n",
            "reading ./MAJ.csv ... 240\n",
            "reading ./Foreign.csv ... 11690\n",
            "reading ./XR.csv ... 3637\n",
            "reading ./XSV.csv ... 23\n",
            "reading ./Person.csv ... 196459\n",
            "reading ./Group.csv ... 3176\n",
            "reading ./NNP.csv ... 2371\n",
            "reading ./IC.csv ... 1305\n",
            "reading ./VX.csv ... 125\n",
            "reading ./NNBC.csv ... 677\n",
            "reading ./EC.csv ... 2547\n",
            "reading ./Inflect.csv ... 44820\n",
            "reading ./NNG.csv ... 208524\n",
            "reading ./NorthKorea.csv ... 3\n",
            "reading ./Preanalysis.csv ... 5\n",
            "reading ./CoinedWord.csv ... 148\n",
            "reading ./VCP.csv ... 9\n",
            "reading ./Hanja.csv ... 125750\n",
            "reading ./NR.csv ... 482\n",
            "reading ./ETN.csv ... 14\n",
            "reading ./Symbol.csv ... 16\n",
            "reading ./VCN.csv ... 7\n",
            "reading ./J.csv ... 416\n",
            "reading ./ETM.csv ... 133\n",
            "reading ./MAG.csv ... 14242\n",
            "reading ./MM.csv ... 453\n",
            "reading ./EF.csv ... 1820\n",
            "reading ./NNB.csv ... 140\n",
            "reading ./Place-station.csv ... 1145\n",
            "reading ./Place.csv ... 30303\n",
            "reading ./NP.csv ... 342\n",
            "reading ./Person-actor.csv ... 99230\n",
            "reading ./VV.csv ... 7331\n",
            "reading ./XSA.csv ... 19\n",
            "reading ./EP.csv ... 51\n",
            "reading ./Wikipedia.csv ... 36762\n",
            "reading ./VA.csv ... 2360\n",
            "emitting double-array: 100% |###########################################| \n",
            "reading ./matrix.def ... 3822x2693\n",
            "emitting matrix      : 100% |###########################################| \n",
            "\n",
            "done!\n",
            "echo To enable dictionary, rewrite /usr/local/etc/mecabrc as \\\"dicdir = /usr/local/lib/mecab/dic/mecab-ko-dic\\\"\n",
            "To enable dictionary, rewrite /usr/local/etc/mecabrc as \"dicdir = /usr/local/lib/mecab/dic/mecab-ko-dic\"\n",
            "+ sudo sh -c 'echo \"dicdir=/usr/local/lib/mecab/dic/mecab-ko-dic\" > /usr/local/etc/mecabrc'\n",
            "+ sudo make install\n",
            "make[1]: Entering directory '/tmp/mecab-ko-dic-2.1.1-20180720'\n",
            "make[1]: Nothing to be done for 'install-exec-am'.\n",
            " /bin/mkdir -p '/usr/local/lib/mecab/dic/mecab-ko-dic'\n",
            " /usr/bin/install -c -m 644 model.bin matrix.bin char.bin sys.dic unk.dic left-id.def right-id.def rewrite.def pos-id.def dicrc '/usr/local/lib/mecab/dic/mecab-ko-dic'\n",
            "make[1]: Leaving directory '/tmp/mecab-ko-dic-2.1.1-20180720'\n",
            "++ python3 -c 'import pkgutil; print(1 if pkgutil.find_loader(\"MeCab\") else 0)'\n",
            "+ [[ 0 == \\1 ]]\n",
            "+ echo 'Install mecab-python'\n",
            "Install mecab-python\n",
            "+ install_mecab_python\n",
            "+ pushd /tmp\n",
            "/tmp /tmp/mecab-ko-dic-2.1.1-20180720\n",
            "+ [[ ! -d mecab-python-0.996 ]]\n",
            "+ git clone https://bitbucket.org/eunjeon/mecab-python-0.996.git\n",
            "Cloning into 'mecab-python-0.996'...\n",
            "remote: Counting objects: 17, done.\u001b[K\n",
            "remote: Compressing objects: 100% (16/16), done.\u001b[K\n",
            "remote: Total 17 (delta 3), reused 0 (delta 0)\u001b[K\n",
            "Unpacking objects: 100% (17/17), done.\n",
            "+ popd\n",
            "/tmp/mecab-ko-dic-2.1.1-20180720\n",
            "+ python3 -m pip install /tmp/mecab-python-0.996\n",
            "Processing /tmp/mecab-python-0.996\n",
            "Building wheels for collected packages: mecab-python\n",
            "  Building wheel for mecab-python (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for mecab-python: filename=mecab_python-0.996_ko_0.9.2-cp36-cp36m-linux_x86_64.whl size=140571 sha256=3a77dd6345e9ca01419ae8fa2ed79d22cc9561daf9a5fce5059b9563a39aa3c2\n",
            "  Stored in directory: /root/.cache/pip/wheels/99/75/a6/e9e73a1dbd73579383644942ef18a6d17ad728a3052a1147fb\n",
            "Successfully built mecab-python\n",
            "Installing collected packages: mecab-python\n",
            "Successfully installed mecab-python-0.996-ko-0.9.2\n",
            "+ echo Done.\n",
            "Done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ccMashrba1_t",
        "outputId": "71f282d3-e9d0-416f-8aa2-9733afd667e8"
      },
      "source": [
        "from konlpy.tag import Mecab\n",
        "import re\n",
        "\n",
        "def text_preprocessing(text_list):\n",
        "  stopwords = ['을', '를', '이', '가', '은','는','null'] #불용어 설정\n",
        "  tokenizer = Mecab()\n",
        "  token_list = []\n",
        "\n",
        "  for text in text_list:\n",
        "    txt = re.sub('[^가-힣a-z]', ' ', text.lower()) #한글, 소문자 남기고 나머지 모두 제거\n",
        "    token = tokenizer.morphs(txt)\n",
        "    token = [t for t in token if t not in stopwords or type(t) != float] #형태소 분석 결과 중 stopwords에 해당하지 않는 것만 추출\n",
        "    token_list.append(token)\n",
        "\n",
        "  return token_list, tokenizer\n",
        "\n",
        "train['token'], mecab = text_preprocessing(train['content'])\n",
        "train['token']"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0                [이, 데일리, marketpoint, 현재, 코스닥, 기관, 억, 순매도]\n",
              "1         [실적, 기반, 저가, 에, 매집, 해야, 할, 월, 급등, 유망주, top, 전격...\n",
              "2               [하이스, 탁론, 선취, 수수료, 없, 는, 월, 최저, 금리, 상품, 출시]\n",
              "3            [종합, 경제, 정보, 미디어, 이, 데, 일리, 무단, 전재, 재, 배포, 금지]\n",
              "4                       [전국, 적, 인, 소비, 붐, 조성, 에, 기여, 할, 예정]\n",
              "                                ...                        \n",
              "118740    [미, fda, 임상, 상, 허가, 임박, 묻, 고, 따, 블, 로, 갈, 바이오,...\n",
              "118741                    [똑똑, 해진, 소비자, 한국, 도, 이젠, 소형차, 시대]\n",
              "118742                    [똑똑, 해진, 소비자, 한국, 도, 이젠, 소형차, 시대]\n",
              "118743                      [년, 한국, tv, 대중, 대, 인터넷, 연결, 된다]\n",
              "118744                      [년, 한국, tv, 대중, 대, 인터넷, 연결, 된다]\n",
              "Name: token, Length: 118745, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q-a2kKfsyioW"
      },
      "source": [
        "### 2. Vectorization \n",
        "\n",
        ": NLP를 컴퓨터가 이해할 수 있게 수치로 바꾸는 것\n",
        "- Count vectorization, TfIdf, Padding\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cD2pJw8xbMvI"
      },
      "source": [
        "이번 대회에서는 특별한 vectorization 기법을 사용하지 않고 keras의 texts_to_sequences를 통한 정수화만 진행해 주어도 높은 정확도를 얻을 수 있다.\n",
        "\n",
        "https://dacon.io/competitions/official/235658/codeshare/1839?page=1&dtype=recent&ptype=pub"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9jTmr2YN1Q0n",
        "outputId": "62471751-dc23-4107-8981-03e5b3df597e"
      },
      "source": [
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "\n",
        "def text2sequence(train_text, max_len=100):\n",
        "    \n",
        "    tokenizer = Tokenizer() #keras의 vectorizing 함수 호출\n",
        "    tokenizer.fit_on_texts(train_text) #train 문장에 fit\n",
        "    train_X_seq = tokenizer.texts_to_sequences(train_text) #각 토큰들에 정수 부여\n",
        "    vocab_size = len(tokenizer.word_index) + 1 #모델에 알려줄 vocabulary의 크기 계산\n",
        "    print('vocab_size : ', vocab_size)\n",
        "    X_train = pad_sequences(train_X_seq, maxlen = max_len) #설정한 문장의 최대 길이만큼 padding\n",
        "    \n",
        "    return X_train, vocab_size, tokenizer\n",
        "\n",
        "\n",
        "train_y = train['info']\n",
        "train_X, vocab_size, vectorizer = text2sequence(train['token'], max_len = 100)\n",
        "print(train_X.shape, train_y.shape)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "vocab_size :  37238\n",
            "(118745, 100) (118745,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UKO25IW-jucx",
        "outputId": "abec9e5c-8b03-4274-ce5c-ef0f2ac4a119"
      },
      "source": [
        "train_X"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[  0,   0,   0, ..., 230,  51, 880],\n",
              "       [  0,   0,   0, ...,  83, 570,  45],\n",
              "       [  0,   0,   0, ...,  69, 287, 322],\n",
              "       ...,\n",
              "       [  0,   0,   0, ..., 162, 171, 134],\n",
              "       [  0,   0,   0, ..., 137, 130,  60],\n",
              "       [  0,   0,   0, ..., 137, 130,  60]], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QH8wgslyibh8"
      },
      "source": [
        "위에 정수화 말고 tfidf 사용해서 모델돌려보고 결과 비교해보기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ua7CofU-kcpP"
      },
      "source": [
        "## 3. Embedding\n",
        ": Embedding을 통해 의미가 유사한 토큰들은 가깝게 임의의 차원에 뿌리자.\n",
        "- 벡터화 방법들은 단어의 중요도나 문서 안에서의 중요도는 구분 할 수 있지만 단어 사이의 유사도는 구별할 수 없음.\n",
        "- 비슷한 의미를 내포하고 있는 토큰들은 서로 가깝게, 그렇지 않은 토큰들은 서로 멀리 뿌리도록 하는 것이 임베딩의 목적\n",
        "\n",
        "\n",
        "**`Embedding()에 넣어야하는 대표적인 인자`**\n",
        "\n",
        "- 첫번째 인자 = 단어 집합의 크기. 즉, 총 단어의 개수\n",
        "- 두번째 인자 = 임베딩 벡터의 출력 차원. 임베딩한 후 벡터의 크기\n",
        "- input_length = 입력 시퀀스의 길이"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IdpmvpWRmKv_"
      },
      "source": [
        "#### word2vec\n",
        ": 주변 단어와의 관계를 통해 해당 단어의 의미적 특성을 파악\n",
        "- 단점: 사용자가 지정한 주변 단어의 개수에 대해서만 학습이 이루어지기 때문에 데이터 전체에 대한 정보를 담기 어렵다.\n",
        "- 구글의 사전 훈련된 word2vec bin 파일을 다운로드 https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ni4kVBXFkcNd"
      },
      "source": [
        "# gensim 모듈과 bin파일을 활용해 word2vec 모델을 로드\n",
        "# 구글마운트\n",
        "\n",
        "import gensim\n",
        "word2vec = gensim.models.KeyedVectors.load_word2vec_format('/content/drive/MyDrive/Colab Notebooks/fake news detection/GoogleNews-vectors-negative300.bin.gz', binary = True)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tL1ku_8ydO9H"
      },
      "source": [
        "word2vec에서 필요 단어만 추출"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3HTUiGU3emLJ"
      },
      "source": [
        "# vectorizer.word_index.items() # = vocabulary "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oqPKuN3HfKAC",
        "outputId": "d231ae8e-2041-4ef8-95a7-ddb6b6d8c258"
      },
      "source": [
        "# vocab_size = len(tokenizer.index_word) + 1\r\n",
        "embedding_matrix = np.zeros((vocab_size, 300)) # 300차원의 임베딩 매트릭스 생성\r\n",
        "\r\n",
        "# tokenizer에 있는 단어사전을 순회하면서 word2vec의 300차원 vector를 가져온다.\r\n",
        "count = 0\r\n",
        "\r\n",
        "for word, idx in vectorizer.word_index.items():\r\n",
        "    if word in word2vec:                    # 넘겨 받은 토큰이 word2vec에 존재하면(이미 훈련된 토큰이라는 의미.)\r\n",
        "      embedding_vector = word2vec[word]   # 해당 토큰에 해당하는 vector를 불러오고\r\n",
        "      embedding_matrix[idx] = embedding_vector # 해당 위치의 embedding_matrix에 저장\r\n",
        "   \r\n",
        "    else:\r\n",
        "     #  print(word, \"word2vec에 없는 단어입니다.\")\r\n",
        "      count += 1\r\n",
        "    pass\r\n",
        "        \r\n",
        "print('embedding_matrix.shape: ', embedding_matrix.shape)\r\n",
        "print('없는 단어 count:', count)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "embedding_matrix.shape:  (37238, 300)\n",
            "없는 단어 count: 34405\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2rVyWWrQDe69"
      },
      "source": [
        "### 4. modeling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DUmRPywPHp3S"
      },
      "source": [
        "keras embedding layer에 embedding_matrix를 가중치로 주어 이용"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CSH5LInC7C2_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "ac99684a-5195-4908-9569-102e560d18af"
      },
      "source": [
        "# tf.random.set_seed(42)\r\n",
        "\r\n",
        "from sklearn.model_selection import train_test_split\r\n",
        "\r\n",
        "# X_train, X_valid, y_train, y_valid = train_test_split(train_X, train_y, random_state = 42, test_size = 0.3)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-12-1a09e870a5c4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_seed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m42\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      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel_selection\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m# X_train, X_valid, y_train, y_valid = train_test_split(train_X, train_y, random_state = 42, test_size = 0.3)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'tf' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4qfRrctLOaND"
      },
      "source": [
        "##### validation o\r\n",
        "\r\n",
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics='accuracy')\r\n",
        "\r\n",
        "from tensorflow import keras\r\n",
        "checkpoint_cb = keras.callbacks.ModelCheckpoint(\"juhee_best_model.h5\",\r\n",
        "                                               save_best_only = True)\r\n",
        "\r\n",
        "history = model.fit(X_train, y_train,\r\n",
        "                 epochs=10,\r\n",
        "                 batch_size = 100, \r\n",
        "                 validation_data = (X_valid, y_valid), \r\n",
        "                 validation_batch_size = 100,\r\n",
        "                 callbacks = [checkpoint_cb])\r\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GSQCV5POdo7t"
      },
      "source": [
        "\r\n",
        "### 수정해보기\r\n",
        "\r\n",
        "- dropout 을 0.5\r\n",
        "- LSTM 출력 노드를 32로."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pLGk1EFcXgFW",
        "outputId": "f42ae8b2-a505-477b-cdfd-c6ed13068d0b"
      },
      "source": [
        "from keras.models import Sequential\n",
        "from keras import regularizers\n",
        "from keras.layers import Dense, Activation, Embedding, SpatialDropout1D, Dropout\n",
        "from keras.layers import LSTM\n",
        "import tensorflow as tf\n",
        "\n",
        "max_len=1000\n",
        "# vocab_size = 37238\n",
        "\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "model = Sequential()\n",
        "model.add(Embedding(vocab_size, 300,weights = [embedding_matrix], input_length = max_len)) #임베딩 가중치 적용 코드\n",
        "model.add(SpatialDropout1D(0.5))\n",
        "model.add(LSTM(64))  \n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(64, activation='relu', kernel_regularizer = regularizers.l2(0.001)))\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "\n",
        "model.summary()\n",
        "\n"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding (Embedding)        (None, 1000, 300)         11171400  \n",
            "_________________________________________________________________\n",
            "spatial_dropout1d (SpatialDr (None, 1000, 300)         0         \n",
            "_________________________________________________________________\n",
            "lstm (LSTM)                  (None, 64)                93440     \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 64)                4160      \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 65        \n",
            "=================================================================\n",
            "Total params: 11,269,065\n",
            "Trainable params: 11,269,065\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eMW-pKn37fyX",
        "outputId": "3ba02f56-338b-49a4-c334-e14fbda34890"
      },
      "source": [
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics='accuracy')\r\n",
        "\r\n",
        "from tensorflow import keras\r\n",
        "checkpoint_cb = keras.callbacks.ModelCheckpoint(\"juhee_best_model.h5\",\r\n",
        "                                               save_best_only = True)\r\n",
        "\r\n",
        "history = model.fit(train_X, train_y,\r\n",
        "                 epochs=10,\r\n",
        "                 batch_size = 100,\r\n",
        "                 callbacks = [checkpoint_cb])\r\n"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 1000) for input Tensor(\"embedding_input:0\", shape=(None, 1000), dtype=float32), but it was called on an input with incompatible shape (None, 100).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 1000) for input Tensor(\"embedding_input:0\", shape=(None, 1000), dtype=float32), but it was called on an input with incompatible shape (None, 100).\n",
            "1188/1188 [==============================] - ETA: 0s - loss: 0.0949 - accuracy: 0.9668WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
            "1188/1188 [==============================] - 400s 337ms/step - loss: 0.0949 - accuracy: 0.9668\n",
            "Epoch 2/10\n",
            "1188/1188 [==============================] - ETA: 0s - loss: 0.0297 - accuracy: 0.9909WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
            "1188/1188 [==============================] - 399s 336ms/step - loss: 0.0297 - accuracy: 0.9909\n",
            "Epoch 3/10\n",
            "1188/1188 [==============================] - ETA: 0s - loss: 0.0149 - accuracy: 0.9957WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
            "1188/1188 [==============================] - 399s 336ms/step - loss: 0.0149 - accuracy: 0.9957\n",
            "Epoch 4/10\n",
            "1188/1188 [==============================] - ETA: 0s - loss: 0.0100 - accuracy: 0.9972WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
            "1188/1188 [==============================] - 400s 337ms/step - loss: 0.0100 - accuracy: 0.9972\n",
            "Epoch 5/10\n",
            "1188/1188 [==============================] - ETA: 0s - loss: 0.0067 - accuracy: 0.9981WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
            "1188/1188 [==============================] - 399s 335ms/step - loss: 0.0067 - accuracy: 0.9981\n",
            "Epoch 6/10\n",
            "1188/1188 [==============================] - ETA: 0s - loss: 0.0053 - accuracy: 0.9986WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
            "1188/1188 [==============================] - 407s 342ms/step - loss: 0.0053 - accuracy: 0.9986\n",
            "Epoch 7/10\n",
            "1188/1188 [==============================] - ETA: 0s - loss: 0.0039 - accuracy: 0.9989WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
            "1188/1188 [==============================] - 406s 342ms/step - loss: 0.0039 - accuracy: 0.9989\n",
            "Epoch 8/10\n",
            "1188/1188 [==============================] - ETA: 0s - loss: 0.0033 - accuracy: 0.9992WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
            "1188/1188 [==============================] - 400s 337ms/step - loss: 0.0033 - accuracy: 0.9992\n",
            "Epoch 9/10\n",
            "1188/1188 [==============================] - ETA: 0s - loss: 0.0034 - accuracy: 0.9990WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
            "1188/1188 [==============================] - 398s 335ms/step - loss: 0.0034 - accuracy: 0.9990\n",
            "Epoch 10/10\n",
            "1188/1188 [==============================] - ETA: 0s - loss: 0.0031 - accuracy: 0.9993WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
            "1188/1188 [==============================] - 403s 339ms/step - loss: 0.0031 - accuracy: 0.9993\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_ZguRA9rOSff"
      },
      "source": [
        ""
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8MjagKf-9QvK"
      },
      "source": [
        "#### 모델 평가 VALIDATE"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JNaPOZBlXgFX"
      },
      "source": [
        "### test 데이터 가공"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "Bz-V-dyPXgFY",
        "outputId": "0ca1e0ba-f629-44dc-cd22-52fb93b79b2b"
      },
      "source": [
        "# test = pd.read_csv('data/news_test.csv')\n",
        "test.head()"
      ],
      "execution_count": 19,
      "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>n_id</th>\n",
              "      <th>date</th>\n",
              "      <th>title</th>\n",
              "      <th>content</th>\n",
              "      <th>ord</th>\n",
              "      <th>id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NEWS00237</td>\n",
              "      <td>20200118</td>\n",
              "      <td>[주목!e스몰캡]코세스, 마이크로LED 시장 개화 최대수혜 기대</td>\n",
              "      <td>마이크로 LED TV 장비 양산 돌입- 전방업체 투자 확대로 본업도 호조연일 '신고가'</td>\n",
              "      <td>1</td>\n",
              "      <td>NEWS00237_1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NEWS00237</td>\n",
              "      <td>20200118</td>\n",
              "      <td>[주목!e스몰캡]코세스, 마이크로LED 시장 개화 최대수혜 기대</td>\n",
              "      <td>[이데일리 김대웅 기자] 반도체 장비 업체 코세스(089890)의 기술력이 마이크로...</td>\n",
              "      <td>2</td>\n",
              "      <td>NEWS00237_2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NEWS00237</td>\n",
              "      <td>20200118</td>\n",
              "      <td>[주목!e스몰캡]코세스, 마이크로LED 시장 개화 최대수혜 기대</td>\n",
              "      <td>최근 대형 업체들과 거래를 맺고 관련 장비들의 양산에 돌입하면서 주가도 연일 신고가...</td>\n",
              "      <td>3</td>\n",
              "      <td>NEWS00237_3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NEWS00237</td>\n",
              "      <td>20200118</td>\n",
              "      <td>[주목!e스몰캡]코세스, 마이크로LED 시장 개화 최대수혜 기대</td>\n",
              "      <td>TV를 필두로 올해부터 마이크로 LED의 시대가 본격적으로 개화할 것으로 예상되면서...</td>\n",
              "      <td>4</td>\n",
              "      <td>NEWS00237_4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NEWS00237</td>\n",
              "      <td>20200118</td>\n",
              "      <td>[주목!e스몰캡]코세스, 마이크로LED 시장 개화 최대수혜 기대</td>\n",
              "      <td>코세스는 반도체 장비를 제조, 판매하는 업체로 지난 2006년 11월 코스닥 시장에...</td>\n",
              "      <td>5</td>\n",
              "      <td>NEWS00237_5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        n_id      date  ... ord           id\n",
              "0  NEWS00237  20200118  ...   1  NEWS00237_1\n",
              "1  NEWS00237  20200118  ...   2  NEWS00237_2\n",
              "2  NEWS00237  20200118  ...   3  NEWS00237_3\n",
              "3  NEWS00237  20200118  ...   4  NEWS00237_4\n",
              "4  NEWS00237  20200118  ...   5  NEWS00237_5\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Em297kYx23m5",
        "outputId": "abb5ec52-84ee-4e5f-9f32-d7a765bc895e"
      },
      "source": [
        "test.shape"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(142565, 6)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hMHME2V_XgFf",
        "outputId": "b96e1f8f-643b-4e52-8254-ce7afa8b5162"
      },
      "source": [
        "# 1. preprocessing\n",
        "\n",
        "test['token'], mecab = text_preprocessing(test['content'])\n",
        "test['token']"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         [마이크, 로, led, tv, 장비, 양산, 돌입, 전방, 업체, 투자, 확대, ...\n",
              "1         [이, 데일리, 김대웅, 기자, 반도체, 장비, 업체, 코, 세스, 의, 기술력, ...\n",
              "2         [최근, 대형, 업체, 들, 과, 거래, 를, 맺, 고, 관련, 장비, 들, 의, ...\n",
              "3         [tv, 를, 필두, 로, 올해, 부터, 마이크, 로, led, 의, 시대, 가, ...\n",
              "4         [코, 세스, 는, 반도체, 장비, 를, 제조, 판매, 하, 는, 업체, 로, 지난...\n",
              "                                ...                        \n",
              "142560         [주식, 시장, 의, 역사, 를, 다시, 쓸, 역대, 급, 종목, 목표, 이상]\n",
              "142561                    [똑똑, 해진, 소비자, 한국, 도, 이젠, 소형차, 시대]\n",
              "142562                    [똑똑, 해진, 소비자, 한국, 도, 이젠, 소형차, 시대]\n",
              "142563                      [년, 한국, tv, 대중, 대, 인터넷, 연결, 된다]\n",
              "142564                      [년, 한국, tv, 대중, 대, 인터넷, 연결, 된다]\n",
              "Name: token, Length: 142565, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z4OHN9Fl9TOC",
        "outputId": "35d3e04d-2c04-4ef8-a29b-22777924c0c7"
      },
      "source": [
        "# 2. 벡터화\r\n",
        "max_len=100\r\n",
        "\r\n",
        "test_X_seq = vectorizer.texts_to_sequences(test['token']) #각 토큰들에 정수 부여\r\n",
        "vocab_size = len(vectorizer.word_index) + 1 #모델에 알려줄 vocabulary의 크기 계산\r\n",
        "print('vocab_size: ', vocab_size)\r\n",
        "X_test = pad_sequences(test_X_seq, maxlen = max_len) #설정한 문장의 최대 길이만큼 padding\r\n",
        "    \r\n",
        "    \r\n",
        "print(X_test.shape)\r\n"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "vocab_size:  37238\n",
            "(142565, 100)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8SCuAQCB4hiX",
        "outputId": "0a1c28fb-89c4-4ea3-dc4b-afc8d583b28d"
      },
      "source": [
        "\r\n",
        "# model.load_weights(\"tutorial-3.h5\")\r\n",
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics='accuracy')\r\n",
        "result = model.predict(X_test)\r\n",
        "result"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Model was constructed with shape (None, 1000) for input Tensor(\"embedding_input:0\", shape=(None, 1000), dtype=float32), but it was called on an input with incompatible shape (None, 100).\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[2.6065826e-01],\n",
              "       [1.2226167e-04],\n",
              "       [8.7600085e-05],\n",
              "       ...,\n",
              "       [9.9999583e-01],\n",
              "       [9.9998236e-01],\n",
              "       [9.9998236e-01]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fqYWvS6e20tY",
        "outputId": "68fb1828-7a28-43dc-bb1d-79ce28270b4e"
      },
      "source": [
        "result.shape"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(142565, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8vo9XxgrAv_q"
      },
      "source": [
        "### submit"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "id": "lUp-hs-4okEJ",
        "outputId": "97b6246c-a946-452a-bf05-eae0ad86aea6"
      },
      "source": [
        "submission = pd.read_csv(\"/content/drive/MyDrive/Colab Notebooks/fake news detection/sample_submission.csv\")\r\n",
        "submission.head()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>id</th>\n",
              "      <th>info</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NEWS00237_1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NEWS00237_2</td>\n",
              "      <td>0</td>\n",
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              "      <td>NEWS00237_5</td>\n",
              "      <td>0</td>\n",
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              "  </tbody>\n",
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              "</div>"
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            "text/plain": [
              "            id  info\n",
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              "2  NEWS00237_3     0\n",
              "3  NEWS00237_4     0\n",
              "4  NEWS00237_5     0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-szV9TIG2yip",
        "outputId": "9a020c31-2fcd-4f5e-819e-6c33e9734bca"
      },
      "source": [
        "submission.shape"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(142565, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "magyFfZeXgFn",
        "outputId": "33c695e7-b591-4acc-e52a-e21284acf6e3"
      },
      "source": [
        "\n",
        "\n",
        "submission.loc[:,'info'] = np.where(result> 0.5, 1,0).reshape(-1)\n",
        "\n",
        "submission.loc[:,[\"id\",\"info\"]].to_csv(\"juhee-new_train dropout0.5.csv\", index = False)\n",
        "submission"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "    .dataframe thead th {\n",
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              "      <th>info</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NEWS00237_2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NEWS00237_3</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NEWS00237_4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NEWS00237_5</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142560</th>\n",
              "      <td>NEWS09482_72</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142561</th>\n",
              "      <td>NEWS09482_73</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142562</th>\n",
              "      <td>NEWS09482_74</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142563</th>\n",
              "      <td>NEWS09482_75</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>142564</th>\n",
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              "      <td>1</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>142565 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                  id  info\n",
              "0        NEWS00237_1     0\n",
              "1        NEWS00237_2     0\n",
              "2        NEWS00237_3     0\n",
              "3        NEWS00237_4     0\n",
              "4        NEWS00237_5     0\n",
              "...              ...   ...\n",
              "142560  NEWS09482_72     1\n",
              "142561  NEWS09482_73     1\n",
              "142562  NEWS09482_74     1\n",
              "142563  NEWS09482_75     1\n",
              "142564  NEWS09482_76     1\n",
              "\n",
              "[142565 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "58lBZNQZXgFr"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3CQs1w4lXgFt"
      },
      "source": [
        "##  베이스라인\n",
        "\n",
        "\n",
        "- Train 데이터에서 라벨링된 광고 정보가 Test 데이터에 나오는 여부에 따라 1, 0 으로 예측\n",
        "- Rule-Based 알고리즘을 활용\n",
        "\n",
        "https://dacon.io/competitions/official/235658/codeshare/1844?page=1&dtype=recent&ptype=pub"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U1oGuS4oXgFu"
      },
      "source": [
        "train_unique_ad_sentence = train.query('info == \"1\"')['content'].unique()\n",
        "test_unique_sentence = test['content'].unique()\n",
        "\n",
        "print(len(train_unique_ad_sentence))\n",
        "print(len(test_unique_sentence))\n",
        "print(len(set(train_unique_ad_sentence) & set(test_unique_sentence)))\n",
        "\n",
        "# Test 데이터에 있는 65,827개의 고유 content 중 1,973개가 Train 데이터에서 나온 광고성 문구와 같다"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H_6qADq-XgFw"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sFEuxfjLXgFx"
      },
      "source": [
        "#  Train 데이터의 광고성 문구와 같으면 1, 아니면 0으로 예측하는 Rule-Based 알고리즘을 구축\n",
        "\n",
        "\n",
        "from tqdm import tqdm\n",
        "\n",
        "test_content = test['content'].values\n",
        "\n",
        "for idx, sent in enumerate(tqdm(test_content)) : #Test 데이터에 있는 모든 content들에 대하여\n",
        "\n",
        "    if sent in train_unique_ad_sentence: # Train 데이터의 광고성 문구와 같은지 비교\n",
        "        submission['info'].iloc[idx] = 1 # 같으면 1\n",
        "\n",
        "    else : \n",
        "        submission['info'].iloc[idx] = 0 # 다르면 0\n",
        "\n",
        "print(submission.head())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LYE4y2V7XgFy"
      },
      "source": [
        "submission.to_csv('simple_baseline.csv', index = False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R0EkwOUeYuMK"
      },
      "source": [
        "데이터 개수\r\n",
        "\r\n",
        "count.mean()\r\n"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}