text_classification_by_using_KNN.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "text classification by using KNN.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Durjoy001/Data-Science/blob/main/text_classification_by_using_KNN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MP07Uaowxdg5"
      },
      "source": [
        "import glob\r\n",
        "import pandas as pd\r\n",
        "import numpy as np\r\n",
        "import os"
      ],
      "execution_count": 174,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "85Je_5LMxeeO"
      },
      "source": [
        "from sklearn.naive_bayes import MultinomialNB\r\n",
        "from sklearn.feature_extraction.text import CountVectorizer\r\n",
        "from sklearn.model_selection import train_test_split\r\n",
        "from sklearn.metrics import accuracy_score\r\n",
        "import time\r\n",
        "import re \r\n",
        "\r\n",
        "from sklearn.pipeline import Pipeline\r\n",
        "from sklearn.feature_extraction.text import TfidfTransformer\r\n",
        "from sklearn.metrics import confusion_matrix\r\n",
        "import joblib"
      ],
      "execution_count": 175,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ICY6CIR3xehJ",
        "outputId": "f0cac2cf-08a2-41ae-cf07-6cff185919f5"
      },
      "source": [
        "from google.colab import drive\r\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 176,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "81nt_dT5xejh"
      },
      "source": [
        "path = \"/content/drive/MyDrive/Colab Notebooks/Assignment 2/songData/*.xlsx\"\r\n",
        "all_file = glob.glob(path)"
      ],
      "execution_count": 177,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OiCXtLU5xekh",
        "outputId": "bdca92c6-6354-4423-9992-7430f4cf8ef2"
      },
      "source": [
        "all_file\r\n"
      ],
      "execution_count": 178,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['/content/drive/MyDrive/Colab Notebooks/Assignment 2/songData/songType10.xlsx',\n",
              " '/content/drive/MyDrive/Colab Notebooks/Assignment 2/songData/songType1.xlsx',\n",
              " '/content/drive/MyDrive/Colab Notebooks/Assignment 2/songData/songType11.xlsx',\n",
              " '/content/drive/MyDrive/Colab Notebooks/Assignment 2/songData/songType9.xlsx',\n",
              " '/content/drive/MyDrive/Colab Notebooks/Assignment 2/songData/songType3.xlsx',\n",
              " '/content/drive/MyDrive/Colab Notebooks/Assignment 2/songData/songType14.xlsx',\n",
              " '/content/drive/MyDrive/Colab Notebooks/Assignment 2/songData/songType18.xlsx',\n",
              " '/content/drive/MyDrive/Colab Notebooks/Assignment 2/songData/songType17.xlsx',\n",
              " '/content/drive/MyDrive/Colab Notebooks/Assignment 2/songData/songType16.xlsx',\n",
              " '/content/drive/MyDrive/Colab Notebooks/Assignment 2/songData/songType2.xlsx',\n",
              " '/content/drive/MyDrive/Colab Notebooks/Assignment 2/songData/songType5.xlsx',\n",
              " '/content/drive/MyDrive/Colab Notebooks/Assignment 2/songData/songType15.xlsx',\n",
              " '/content/drive/MyDrive/Colab Notebooks/Assignment 2/songData/songType4.xlsx',\n",
              " '/content/drive/MyDrive/Colab Notebooks/Assignment 2/songData/songType12.xlsx',\n",
              " '/content/drive/MyDrive/Colab Notebooks/Assignment 2/songData/songType7.xlsx',\n",
              " '/content/drive/MyDrive/Colab Notebooks/Assignment 2/songData/songType20.xlsx',\n",
              " '/content/drive/MyDrive/Colab Notebooks/Assignment 2/songData/songType8.xlsx',\n",
              " '/content/drive/MyDrive/Colab Notebooks/Assignment 2/songData/songType13.xlsx',\n",
              " '/content/drive/MyDrive/Colab Notebooks/Assignment 2/songData/songType19.xlsx',\n",
              " '/content/drive/MyDrive/Colab Notebooks/Assignment 2/songData/songType6.xlsx',\n",
              " '/content/drive/MyDrive/Colab Notebooks/Assignment 2/songData/songType0.xlsx']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 178
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ExAA2ZCXxemi"
      },
      "source": [
        "names = []\r\n",
        "count = []\r\n",
        "clusters = []\r\n",
        "data = []\r\n",
        "\r\n",
        "for each in all_file:\r\n",
        "    temp = pd.read_excel(each)\r\n",
        "    \r\n",
        "    \r\n",
        "    count.append(len(temp))\r\n",
        "    names.append(each.split('/')[1][:-5])\r\n",
        "    clusters.append(temp['songType'])\r\n",
        "    \r\n",
        "    \r\n",
        "    data.append(temp)\r\n",
        "    \r\n",
        "cluster_ids =clusters"
      ],
      "execution_count": 197,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "5b5A35c6xen_",
        "outputId": "79a2df5a-0e60-4f4d-9178-fcd50f5f979e"
      },
      "source": [
        "data = pd.concat(data)\r\n",
        "data = data.sample(frac = 1)\r\n",
        "\r\n",
        "data"
      ],
      "execution_count": 198,
      "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>Unnamed: 0</th>\n",
              "      <th>lyrics</th>\n",
              "      <th>songType</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>715</th>\n",
              "      <td>3204</td>\n",
              "      <td>এ শুধু অলস মায়া, এ শুধু মেঘের খেলা এ শুধু মনে...</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>115</th>\n",
              "      <td>3625</td>\n",
              "      <td>যখন সময় থমকে দাঁড়ায় নিরাশার পাখি দু’হাত বাড়ায় ...</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>577</th>\n",
              "      <td>3139</td>\n",
              "      <td>তুমি আসবে বলে কাছে ডাকবে বলে ভাল বাসবে ওগো শুধ...</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>150</th>\n",
              "      <td>1146</td>\n",
              "      <td>ভেঙ্গেছে ডানা দুটো আমার ভেঙ্গেছে কোন ঝড়ে নীলিম...</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>1155</td>\n",
              "      <td>স্বপনে তাহারে কুড়ায়ে পেয়েছি রেখেছি স্বপনে ঢাকি...</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>509</th>\n",
              "      <td>2298</td>\n",
              "      <td>ফুলের মত আমার এ প্রেম দুপায়ে যে মাড়িয়ে গেছে জা...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>174</th>\n",
              "      <td>704</td>\n",
              "      <td>ডাইনে বাঁয়ে, গঞ্জে গাঁয়ে, পুরানো অথবা নতুন অধ্...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>693</th>\n",
              "      <td>3182</td>\n",
              "      <td>মাঝে মাঝে তব দেখা পাই চিরদিন কেন পাই না? কেন ম...</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>112</th>\n",
              "      <td>3570</td>\n",
              "      <td>আমি মরিয়া হইবো শ্রীনন্দের নন্দন তোমাকে বানাবো ...</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>357</th>\n",
              "      <td>1797</td>\n",
              "      <td>তোমার পূজার ছলে তোমায় ভুলেই থাকি। বুঝতে নারি ক...</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4105 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Unnamed: 0                                             lyrics  songType\n",
              "715        3204  এ শুধু অলস মায়া, এ শুধু মেঘের খেলা এ শুধু মনে...        17\n",
              "115        3625  যখন সময় থমকে দাঁড়ায় নিরাশার পাখি দু’হাত বাড়ায় ...        12\n",
              "577        3139  তুমি আসবে বলে কাছে ডাকবে বলে ভাল বাসবে ওগো শুধ...         3\n",
              "150        1146  ভেঙ্গেছে ডানা দুটো আমার ভেঙ্গেছে কোন ঝড়ে নীলিম...        13\n",
              "37         1155  স্বপনে তাহারে কুড়ায়ে পেয়েছি রেখেছি স্বপনে ঢাকি...        14\n",
              "..          ...                                                ...       ...\n",
              "509        2298  ফুলের মত আমার এ প্রেম দুপায়ে যে মাড়িয়ে গেছে জা...         1\n",
              "174         704  ডাইনে বাঁয়ে, গঞ্জে গাঁয়ে, পুরানো অথবা নতুন অধ্...         1\n",
              "693        3182  মাঝে মাঝে তব দেখা পাই চিরদিন কেন পাই না? কেন ম...        17\n",
              "112        3570  আমি মরিয়া হইবো শ্রীনন্দের নন্দন তোমাকে বানাবো ...        12\n",
              "357        1797  তোমার পূজার ছলে তোমায় ভুলেই থাকি। বুঝতে নারি ক...        17\n",
              "\n",
              "[4105 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 198
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QfbEVlScxep-"
      },
      "source": [
        "stop_words = [ 'হাই', 'ফ্রিকুয়েন্সি', 'কিন্তু', 'অপ্রয়োজনীয়', 'শব্দ' ]"
      ],
      "execution_count": 199,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FpKwqTzyNlov"
      },
      "source": [
        "#stop_words = []"
      ],
      "execution_count": 182,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yprXtV_sxes3"
      },
      "source": [
        "data['lyrics'] = data['lyrics'].apply(lambda x: ' '.join([word for word in x.split() if word not in (stop_words)]))"
      ],
      "execution_count": 200,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NIDzhdFwxev6"
      },
      "source": [
        "X_train, X_test, y_train, y_test =  train_test_split(data['lyrics'], data['songType'], test_size=0.33, random_state=42 )"
      ],
      "execution_count": 201,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vorb67Q2yC_Y",
        "outputId": "de18d19f-ae4f-4180-b3e1-91d5aa24dc96"
      },
      "source": [
        "from sklearn.model_selection import ShuffleSplit\r\n",
        "from sklearn.model_selection import cross_val_score\r\n",
        "from sklearn import metrics\r\n",
        "\r\n",
        "total_data_count = len(data)\r\n",
        "per_class_counts = []\r\n",
        "unique_classes = np.unique(data['songType'].values)\r\n",
        "class_count_mean = np.mean(count)\r\n",
        "\r\n",
        "###########################3\r\n",
        "############### classifier setup ################\r\n",
        "\r\n",
        "from sklearn.neighbors import KNeighborsClassifier\r\n",
        "text_clf_svm = Pipeline([\r\n",
        "    \r\n",
        "    ('vect', CountVectorizer(\r\n",
        "                            analyzer=\"word\", \r\n",
        "                            lowercase=False,\r\n",
        "                            token_pattern=\"[\\S]*\",\r\n",
        "                            tokenizer=None,\r\n",
        "                            ngram_range=(1, 3),\r\n",
        "                            preprocessor=None)),                 \r\n",
        "    ('tfidf', TfidfTransformer()),\r\n",
        "    ('clf-svm',KNeighborsClassifier()),\r\n",
        "                                                                        \r\n",
        "])\r\n",
        "classifier = text_clf_svm.fit(list(X_train), list(y_train))\r\n",
        "\r\n",
        "\r\n",
        "cv = ShuffleSplit(n_splits=5, test_size=0.3, random_state=0)\r\n",
        "score = cross_val_score(text_clf_svm, list(X_train), list(y_train),cv=cv)\r\n",
        "\r\n",
        "print(\"Cross Accuracy: %0.2f (+/- %0.2f)\" % (score.mean(), score.std() * 2))\r\n",
        "\r\n",
        "\r\n",
        "predicted = classifier.predict(X_test)"
      ],
      "execution_count": 208,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cross Accuracy: 0.14 (+/- 0.06)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z-f19fdEyDA8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9de47ff1-8212-4e23-c2dc-699d08ff93c6"
      },
      "source": [
        "print(\"Cross Accuracy: %0.2f (+/- %0.2f)\" % (score.mean(), score.std() * 2))\r\n",
        "print(metrics.classification_report(y_test, predicted))"
      ],
      "execution_count": 209,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cross Accuracy: 0.14 (+/- 0.06)\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.00      0.00      0.00         1\n",
            "           1       0.28      0.12      0.17       337\n",
            "           2       0.00      0.00      0.00         5\n",
            "           3       0.50      0.12      0.19       237\n",
            "           4       0.00      0.00      0.00         1\n",
            "           5       0.00      0.00      0.00        20\n",
            "           6       0.28      0.17      0.21        29\n",
            "           7       0.50      0.01      0.03        67\n",
            "           8       0.00      0.00      0.00         8\n",
            "           9       0.00      0.00      0.00        25\n",
            "          10       0.00      0.00      0.00        15\n",
            "          11       0.00      0.00      0.00        15\n",
            "          12       0.03      0.31      0.06        39\n",
            "          13       0.26      0.27      0.27       242\n",
            "          14       0.33      0.05      0.09        20\n",
            "          15       0.00      0.00      0.00         1\n",
            "          16       0.00      0.00      0.00         3\n",
            "          17       0.83      0.07      0.13       270\n",
            "          18       0.00      0.00      0.00         2\n",
            "          19       0.00      0.00      0.00        15\n",
            "          20       0.00      0.00      0.00         3\n",
            "\n",
            "    accuracy                           0.13      1355\n",
            "   macro avg       0.14      0.05      0.05      1355\n",
            "weighted avg       0.40      0.13      0.16      1355\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xgf9wZASNOGQ"
      },
      "source": [
        "import pandas as pd\r\n",
        "confusion_matrix = list(metrics.confusion_matrix(y_test, predicted))"
      ],
      "execution_count": 210,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 610
        },
        "id": "6ZzCdz7mNOP3",
        "outputId": "18d47a81-1cdd-4e56-e961-39e7d269523a"
      },
      "source": [
        "import seaborn as sn\r\n",
        "import pandas as pd\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "array = confusion_matrix\r\n",
        "df_cm = pd.DataFrame(array, index = [i for i in names],\r\n",
        "              columns = [i for i in names])\r\n",
        "df_norm_col=(df_cm-df_cm.mean())/df_cm.std()\r\n",
        "#df_norm_col=df_cm-df_cm.mean()\r\n",
        "plt.figure(figsize = (15,10))\r\n",
        "sn.heatmap(df_norm_col, annot=True,cmap='viridis')"
      ],
      "execution_count": 211,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe284c41390>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 211
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1080x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}