2-TextDoe-BoW.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "AIB_TNC_BoW_2500_splited.ipynb",
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Resoure Preparation"
      ],
      "metadata": {
        "id": "_8C2S-7cMCHo"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Setup Library"
      ],
      "metadata": {
        "id": "pF789WUKMLah"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "06krfgOrz5oh",
        "outputId": "ba6afeb3-e4c7-4990-85d8-7a0c76cf3c31"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[K     |████████████████████████████████| 4.6 MB 3.8 MB/s \n",
            "\u001b[K     |████████████████████████████████| 511.7 MB 5.8 kB/s \n",
            "\u001b[K     |████████████████████████████████| 5.8 MB 27.9 MB/s \n",
            "\u001b[K     |████████████████████████████████| 438 kB 52.4 MB/s \n",
            "\u001b[K     |████████████████████████████████| 1.6 MB 42.2 MB/s \n",
            "\u001b[K     |████████████████████████████████| 11.5 MB 3.3 MB/s \n",
            "\u001b[K     |████████████████████████████████| 965 kB 4.3 MB/s \n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip -q install tensorflow_text\n",
        "!pip -q install pythainlp \n",
        "!pip -q install python-crfsuite"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from pythainlp import word_tokenize\n",
        "import re\n",
        "\n",
        "import gdown"
      ],
      "metadata": {
        "id": "QpK1Qrqw0C5E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Download Dataset"
      ],
      "metadata": {
        "id": "p_WCnFoUMXyl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "driveURL = \"https://drive.google.com/file/d/1SxotuRhV8QE_sPWCVJS0m4csgL4xTgj5/view?usp=sharing\"\n",
        "downloadURL = 'https://drive.google.com/uc?export=download&id='+driveURL.split('/')[-2]\n",
        "gdown.download(downloadURL)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cJrVDyF90E3H",
        "outputId": "2ab169f7-7171-44b1-ec64-aca31b65f381"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?export=download&id=1SxotuRhV8QE_sPWCVJS0m4csgL4xTgj5\n",
            "To: /content/TNC_DataSet_2500.zip\n",
            "100%|██████████| 11.6M/11.6M [00:00<00:00, 61.7MB/s]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'TNC_DataSet_2500.zip'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "driveURL = \"https://drive.google.com/file/d/1SSGqkBELTKHAy4M0yVXRBcFuCNOC-DTa/view?usp=sharing\"\n",
        "downloadURL = 'https://drive.google.com/uc?export=download&id='+driveURL.split('/')[-2]\n",
        "gdown.download(downloadURL)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bQqtlsJ4gor4",
        "outputId": "f497b618-5643-4f76-fde8-c01ac8e04b26"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?export=download&id=1SSGqkBELTKHAy4M0yVXRBcFuCNOC-DTa\n",
            "To: /content/TNC_TestSet_5000.csv\n",
            "100%|██████████| 27.3M/27.3M [00:00<00:00, 226MB/s]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'TNC_TestSet_5000.csv'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip TNC_DataSet_2500.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8W8KhaPC1vFR",
        "outputId": "e59f2220-2123-4314-8cf6-399466df5af9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  TNC_DataSet_2500.zip\n",
            "  inflating: dataset_info.txt        \n",
            "  inflating: TNC_TestSet_2500.csv    \n",
            "  inflating: TNC_TrainSet_2500.csv   \n",
            "  inflating: TNC_ValidationSet_2500.csv  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Preprocessing"
      ],
      "metadata": {
        "id": "OHGw1zgCMnKV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_df = pd.read_csv(\"TNC_TrainSet_2500.csv\")\n",
        "valid_df = pd.read_csv(\"TNC_ValidationSet_2500.csv\")\n",
        "\n",
        "test_df = pd.read_csv(\"TNC_TestSet_5000.csv\")"
      ],
      "metadata": {
        "id": "e24poYs70H2R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(test_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JAghrHVw3n9n",
        "outputId": "e34db623-cde8-462b-a59d-a47881ff2a5e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5400"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "all_df = pd.concat([train_df,valid_df,test_df]).reset_index(drop=True)"
      ],
      "metadata": {
        "id": "HXr8chAB0LJl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def replace_url(text):\n",
        "    URL_PATTERN = r\"\"\"(?i)\\b((?:https?:(?:/{1,3}|[a-z0-9%])|[a-z0-9.\\-]+[.](?:com|net|org|edu|gov|mil|aero|asia|biz|cat|coop|info|int|jobs|mobi|museum|name|post|pro|tel|travel|xxx|ac|ad|ae|af|ag|ai|al|am|an|ao|aq|ar|as|at|au|aw|ax|az|ba|bb|bd|be|bf|bg|bh|bi|bj|bm|bn|bo|br|bs|bt|bv|bw|by|bz|ca|cc|cd|cf|cg|ch|ci|ck|cl|cm|cn|co|cr|cs|cu|cv|cx|cy|cz|dd|de|dj|dk|dm|do|dz|ec|ee|eg|eh|er|es|et|eu|fi|fj|fk|fm|fo|fr|ga|gb|gd|ge|gf|gg|gh|gi|gl|gm|gn|gp|gq|gr|gs|gt|gu|gw|gy|hk|hm|hn|hr|ht|hu|id|ie|il|im|in|io|iq|ir|is|it|je|jm|jo|jp|ke|kg|kh|ki|km|kn|kp|kr|kw|ky|kz|la|lb|lc|li|lk|lr|ls|lt|lu|lv|ly|ma|mc|md|me|mg|mh|mk|ml|mm|mn|mo|mp|mq|mr|ms|mt|mu|mv|mw|mx|my|mz|na|nc|ne|nf|ng|ni|nl|no|np|nr|nu|nz|om|pa|pe|pf|pg|ph|pk|pl|pm|pn|pr|ps|pt|pw|py|qa|re|ro|rs|ru|rw|sa|sb|sc|sd|se|sg|sh|si|sj|Ja|sk|sl|sm|sn|so|sr|ss|st|su|sv|sx|sy|sz|tc|td|tf|tg|th|tj|tk|tl|tm|tn|to|tp|tr|tt|tv|tw|tz|ua|ug|uk|us|uy|uz|va|vc|ve|vg|vi|vn|vu|wf|ws|ye|yt|yu|za|zm|zw)/)(?:[^\\s()<>{}\\[\\]]+|\\([^\\s()]*?\\([^\\s()]+\\)[^\\s()]*?\\)|\\([^\\s]+?\\))+(?:\\([^\\s()]*?\\([^\\s()]+\\)[^\\s()]*?\\)|\\([^\\s]+?\\)|[^\\s`!()\\[\\]{};:'\".,<>?«»“”‘’])|(?:(?<!@)[a-z0-9]+(?:[.\\-][a-z0-9]+)*[.](?:com|net|org|edu|gov|mil|aero|asia|biz|cat|coop|info|int|jobs|mobi|museum|name|post|pro|tel|travel|xxx|ac|ad|ae|af|ag|ai|al|am|an|ao|aq|ar|as|at|au|aw|ax|az|ba|bb|bd|be|bf|bg|bh|bi|bj|bm|bn|bo|br|bs|bt|bv|bw|by|bz|ca|cc|cd|cf|cg|ch|ci|ck|cl|cm|cn|co|cr|cs|cu|cv|cx|cy|cz|dd|de|dj|dk|dm|do|dz|ec|ee|eg|eh|er|es|et|eu|fi|fj|fk|fm|fo|fr|ga|gb|gd|ge|gf|gg|gh|gi|gl|gm|gn|gp|gq|gr|gs|gt|gu|gw|gy|hk|hm|hn|hr|ht|hu|id|ie|il|im|in|io|iq|ir|is|it|je|jm|jo|jp|ke|kg|kh|ki|km|kn|kp|kr|kw|ky|kz|la|lb|lc|li|lk|lr|ls|lt|lu|lv|ly|ma|mc|md|me|mg|mh|mk|ml|mm|mn|mo|mp|mq|mr|ms|mt|mu|mv|mw|mx|my|mz|na|nc|ne|nf|ng|ni|nl|no|np|nr|nu|nz|om|pa|pe|pf|pg|ph|pk|pl|pm|pn|pr|ps|pt|pw|py|qa|re|ro|rs|ru|rw|sa|sb|sc|sd|se|sg|sh|si|sj|Ja|sk|sl|sm|sn|so|sr|ss|st|su|sv|sx|sy|sz|tc|td|tf|tg|th|tj|tk|tl|tm|tn|to|tp|tr|tt|tv|tw|tz|ua|ug|uk|us|uy|uz|va|vc|ve|vg|vi|vn|vu|wf|ws|ye|yt|yu|za|zm|zw)\\b/?(?!@)))\"\"\"\n",
        "    return re.sub(URL_PATTERN, 'xxurl', text)\n",
        "\n",
        "def replace_rep(text):\n",
        "    def _replace_rep(m):\n",
        "        c,cc = m.groups()\n",
        "        return f'{c}xxrep'\n",
        "    re_rep = re.compile(r'(\\S)(\\1{2,})')\n",
        "    return re_rep.sub(_replace_rep, text)\n",
        "\n",
        "def process_text(text):\n",
        "    #pre rules\n",
        "    res = text.lower().strip() \n",
        "    res = replace_url(res)\n",
        "    res = replace_rep(res) \n",
        "    \n",
        "    #tokenize\n",
        "    res = [word for word in word_tokenize(res) if word and not re.search(pattern=r\"\\s+\", string=word)]\n",
        "    return res\n",
        "    \n",
        "DOMAIN_DICT = {\n",
        "      0 : 'Imaginative',\n",
        "      1 : 'Natural & Pure Science',\n",
        "      2 : 'Applied Science',\n",
        "      3 : 'Social Science ',\n",
        "      4 : 'History',\n",
        "      5 : 'Commerce & Finance',\n",
        "      6 : 'Arts',\n",
        "      7 : 'Belief & Thought'\n",
        "  }"
      ],
      "metadata": {
        "id": "we9uFX6u0NW-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Data Preprocessing\n",
        "train_df['Processed'] = train_df.Texts.map(lambda x: '|'.join(process_text(x)))\n",
        "valid_df['Processed'] = valid_df.Texts.map(lambda x: '|'.join(process_text(x)))\n",
        "test_df['Processed'] = test_df.Texts.map(lambda x: '|'.join(process_text(x)))\n",
        "\n",
        "#Shuffle Row\n",
        "train_df = train_df.sample(frac=1,random_state=69).reset_index(drop=True)\n",
        "valid_df = valid_df.sample(frac=1,random_state=69).reset_index(drop=True)\n",
        "test_df = test_df.sample(frac=1,random_state=69).reset_index(drop=True)"
      ],
      "metadata": {
        "id": "ycedadBS0Nxg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Train a Bag of Words"
      ],
      "metadata": {
        "id": "vqOr1e2PNH4P"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Set up Traning Variable "
      ],
      "metadata": {
        "id": "1sjVaYt5M10W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "tfidf = TfidfVectorizer(tokenizer=process_text, ngram_range=(1,2), min_df=30, max_df=0.8, sublinear_tf=True)\n",
        "tfidf_fit = tfidf.fit(all_df['Texts'])\n",
        "text_train = tfidf_fit.transform(train_df['Texts'])\n",
        "text_valid = tfidf_fit.transform(valid_df['Texts'])\n",
        "text_test = tfidf_fit.transform(test_df['Texts'])\n",
        "text_train.shape, text_valid.shape\n",
        "\n",
        "y_train = train_df['Category']\n",
        "y_valid = valid_df['Category']\n",
        "y_test = test_df['Category']\n",
        "\n",
        "\n",
        "text_train.shape, text_valid.shape, text_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CIakXEr80YUt",
        "outputId": "67f9a028-c21f-4288-d7a1-160986e487ef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/feature_extraction/text.py:517: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
            "  \"The parameter 'token_pattern' will not be used\"\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((12600, 40681), (2696, 40681), (5400, 40681))"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = text_train.toarray()\n",
        "X_valid = text_valid.toarray()\n",
        "X_test = text_test.toarray()\n",
        "X_train.shape, X_valid.shape, X_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AwRxccJF0aIe",
        "outputId": "8bec4bca-1b77-42c3-e64d-1ee02459051c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((12600, 40681), (2696, 40681), (5400, 40681))"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Fit data to model "
      ],
      "metadata": {
        "id": "FZEi__62NAaL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = LogisticRegression(C=2., penalty='l2', solver='liblinear', dual=False, multi_class='ovr')\n",
        "model.fit(X_train,y_train)\n",
        "model.score(X_valid,y_valid)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3BPqeaaT0c8s",
        "outputId": "7aa4b6d1-3311-47ea-af15-b734798efa35"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8501483679525222"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.score(X_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zNDc3f1w0evE",
        "outputId": "4a21ef22-7752-471d-cf37-d67446dc6474"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6453703703703704"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model Evaluation"
      ],
      "metadata": {
        "id": "yLY4v2S3Nc7R"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Evaluate on validation set "
      ],
      "metadata": {
        "id": "PflING3NNkbF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "y_val_pred = model.predict(X_valid)\n",
        "y_val_true = np.array(y_valid)\n",
        "print(classification_report(y_valid, y_val_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bD3kuwS00fKt",
        "outputId": "59a2633c-2512-4932-aaf6-178c701f92f5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                        precision    recall  f1-score   support\n",
            "\n",
            "       Applied Science       0.89      0.92      0.90       337\n",
            "                  Arts       0.78      0.70      0.74       337\n",
            "      Belief & Thought       0.92      0.85      0.88       337\n",
            "    Commerce & Finance       0.97      0.97      0.97       337\n",
            "               History       0.94      1.00      0.97       337\n",
            "           Imaginative       0.77      1.00      0.87       337\n",
            "Natural & Pure Science       0.75      0.58      0.65       337\n",
            "       Social Science        0.77      0.78      0.78       337\n",
            "\n",
            "              accuracy                           0.85      2696\n",
            "             macro avg       0.85      0.85      0.85      2696\n",
            "          weighted avg       0.85      0.85      0.85      2696\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sorted_y_class = ['Applied Science',\n",
        "                  'Arts',\n",
        "                  'Belief & Thought',\n",
        "                  'Commerce & Finance',\n",
        "                  'History',\n",
        "                  'Imaginative',\n",
        "                  'Natural & Pure Science',\n",
        "                  'Social Science', ]"
      ],
      "metadata": {
        "id": "QhLuE4hqV_T-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#confusion matrix\n",
        "import numpy\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
        "from matplotlib.pyplot import figure\n",
        "\n",
        "cm = confusion_matrix(y_val_true, y_val_pred)\n",
        "cm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "epFWcU-RV_WP",
        "outputId": "8905e2a3-9a57-4ec2-809f-9ac9371603cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[310,   0,   0,   0,   0,   0,   4,  23],\n",
              "       [  0, 236,   9,   0,   2,  23,  49,  18],\n",
              "       [  0,   4, 287,   0,   0,  37,   7,   2],\n",
              "       [  1,   0,   0, 326,   0,   0,   0,  10],\n",
              "       [  0,   0,   0,   1, 336,   0,   0,   0],\n",
              "       [  0,   0,   0,   0,   0, 337,   0,   0],\n",
              "       [ 38,  44,  12,   1,   0,  22, 196,  24],\n",
              "       [  0,  19,   4,   9,  18,  16,   7, 264]])"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "disp = ConfusionMatrixDisplay(confusion_matrix=cm,\n",
        "                              display_labels=sorted_y_class)\n",
        "\n",
        "font = {'family' : 'normal',\n",
        "        'size'   : 14}\n",
        "\n",
        "matplotlib.rc('font', **font)\n",
        "\n",
        "figure(figsize=(100, 300), dpi=1000)\n",
        "disp.plot()\n",
        "plt.xticks(rotation='vertical')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9-3XCZVhV_YM",
        "outputId": "05f69a27-7778-43de-9889-8ccba4c98681"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 100000x300000 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "findfont: Font family ['normal'] not found. Falling back to DejaVu Sans.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Evaluate on test set "
      ],
      "metadata": {
        "id": "7UH7gk4TNubD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_test_pred = model.predict(X_test)\n",
        "y_test_true = np.array(y_test)\n",
        "print(classification_report(y_test, y_test_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OtB-SBCNgxL6",
        "outputId": "3a05ac00-bccf-46f4-8d0f-8686d75e0a72"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                        precision    recall  f1-score   support\n",
            "\n",
            "       Applied Science       0.78      0.47      0.59       675\n",
            "                  Arts       0.54      0.55      0.54       675\n",
            "      Belief & Thought       0.88      0.83      0.86       675\n",
            "    Commerce & Finance       0.76      0.93      0.83       675\n",
            "               History       0.73      0.19      0.30       675\n",
            "           Imaginative       0.66      0.97      0.78       675\n",
            "Natural & Pure Science       0.52      0.48      0.50       675\n",
            "       Social Science        0.49      0.75      0.59       675\n",
            "\n",
            "              accuracy                           0.65      5400\n",
            "             macro avg       0.67      0.65      0.62      5400\n",
            "          weighted avg       0.67      0.65      0.62      5400\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#confusion matrix\n",
        "new_cm = confusion_matrix(y_test_true, y_test_pred)\n",
        "new_cm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t94f_iN6gxN-",
        "outputId": "75924c4e-2600-4194-cb09-504f1bb5e041"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[318,   5,   0,  75,   4,   2, 223,  48],\n",
              "       [  8, 369,  35,   4,   3, 110,  17, 129],\n",
              "       [  0,   9, 562,   0,   0,  53,  51,   0],\n",
              "       [  3,   0,   1, 627,   8,   0,   1,  35],\n",
              "       [  0, 121,  12, 100, 125,  19,   0, 298],\n",
              "       [  0,  13,   3,   0,   0, 653,   3,   3],\n",
              "       [ 63, 121,  14,   1,   1, 126, 324,  25],\n",
              "       [ 16,  49,  12,  20,  30,  32,   9, 507]])"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "disp = ConfusionMatrixDisplay(confusion_matrix=new_cm,\n",
        "                              display_labels=sorted_y_class)\n",
        "\n",
        "font = {'family' : 'normal',\n",
        "        'size'   : 14}\n",
        "\n",
        "matplotlib.rc('font', **font)\n",
        "\n",
        "figure(figsize=(100, 300), dpi=1000)\n",
        "disp.plot()\n",
        "plt.xticks(rotation='vertical')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "VJb1T0G9gxQM",
        "outputId": "ee6a1b60-7b74-4b6a-eff8-2207ab412463"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 100000x300000 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test_pred = model.predict(X_test)\n",
        "print(classification_report(y_test, y_test_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-N9uz6ki0hc9",
        "outputId": "de4f8ab9-7ecf-4d06-ae65-e3939ef70a95"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                        precision    recall  f1-score   support\n",
            "\n",
            "       Applied Science       0.78      0.47      0.59       675\n",
            "                  Arts       0.54      0.55      0.54       675\n",
            "      Belief & Thought       0.88      0.83      0.86       675\n",
            "    Commerce & Finance       0.76      0.93      0.83       675\n",
            "               History       0.73      0.19      0.30       675\n",
            "           Imaginative       0.66      0.97      0.78       675\n",
            "Natural & Pure Science       0.52      0.48      0.50       675\n",
            "       Social Science        0.49      0.75      0.59       675\n",
            "\n",
            "              accuracy                           0.65      5400\n",
            "             macro avg       0.67      0.65      0.62      5400\n",
            "          weighted avg       0.67      0.65      0.62      5400\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model testing"
      ],
      "metadata": {
        "id": "l7m9rnCjNzn_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sample = [\n",
        "          'ไอโซโทปที่พบมากที่สุดของไฮโดรเจน คือ โปรเทียม (ชื่อพบใช้น้อย สัญลักษณ์ 1H) ซึ่งมีโปรตอนหนึ่งตัวแต่ไม่มีนิวตรอน ในสารประกอบไอออนิก โปรเทียมสามารถรับประจุลบ (แอนไอออนซึ่งมีชื่อว่า ไฮไดรด์ และเขียนสัญลักษณ์ได้เป็น H-)',\n",
        "          'ปอบ เป็นผีจำพวกหนึ่ง ที่อยู่ในความเชื่อพื้นบ้านของไทย โดยเฉพาะในภาคอีสาน โดยเชื่อกันว่าเป็นผีที่กินของดิบ ๆ สด ๆ กินเท่าไหร่ก็ไม่อิ่ม',\n",
        "          'แสดงความยินดีกับนายชัชชาติ สิทธิพันธุ์ ที่ได้รับความไว้วางใจจากพี่น้องชาวกรุงเทพมหานคร เลือกเป็นผู้ว่าราชการกรุงเทพมหานคร'\n",
        "          ]\n",
        "\n",
        "sample_feature = tfidf_fit.transform(sample)\n",
        "model.predict(sample_feature)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "srFedhtU0jEK",
        "outputId": "5e167056-2ce3-43b4-ba8d-c79e6e96cc5d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Applied Science', 'Belief & Thought', 'Commerce & Finance'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sample = [\n",
        "          'ต้นโกงกาง เป็นต้นไม้ที่เติมโตในบริเวณป่าชายเลนที่ใกล้กับทะเล เป็นต้นที่ช่วยให้ระบบนิเวศป่าโกงกางมีความอุดมสมบูรณ์อย่างมาก และพบเจอหลายสายพันธุ์',\n",
        "          'ม้านิลมังกร หรือ ม้ามังกร สัตว์ประหลาดในวรรณคดีไทยเรื่องพระอภัยมณี ตามจินตนาการของสุนทรภู่ เป็นพาหนะของสุดสาคร',\n",
        "          'สกุลเงินดิจิทัล หรือ คริปโตเคอร์เรนซี คือ สินทรัพย์ดิจิทัล ประเภทหนึ่งที่มีการรักษาความปลอดภัยด้วยการเข้ารหัส ถูกออกแบบมาเพื่อใช้เป็นสื่อกลางในการแลกเปลี่ยนเช่นเดียวกับสกุลเงินทั่วไป เพียงแต่ไม่สามารถจับต้องได้'\n",
        "          ]\n",
        "\n",
        "sample_feature = tfidf_fit.transform(sample)\n",
        "model.predict(sample_feature)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_zCIks5V6M_7",
        "outputId": "c9fdc504-2411-40c9-d2a8-34726ef49d1a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Natural & Pure Science', 'Arts', 'Commerce & Finance'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    }
  ]
}