reddit_posts_lr_rf.ipynb

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      "name": "reddit_posts_lr_rf.ipynb",
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      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l0AlPGKGE-Rr",
        "colab_type": "text"
      },
      "source": [
        "## Basic Machine Learning Models on Redddit Posts.\n",
        "In this notebook we will try to fit some basic machine learning models like Logistc Regression with One vs Rest Classifier as this is a multi class classification problem and Random Forest on the Redddit Texts. I will be making use of TFIDF to get the model working on text data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RNytXtuMqniR",
        "colab_type": "code",
        "outputId": "5bc41de4-b929-4dad-8d4f-ece4606fd622",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h2UUR0L_qo9p",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.multiclass import OneVsRestClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, precision_recall_fscore_support\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IQeh4xw7rUGa",
        "colab_type": "code",
        "outputId": "f9297e5c-080e-415b-a400-37d8b1808d55",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        }
      },
      "source": [
        "df = pd.read_csv('/content/drive/My Drive/Colab Notebooks/clean_reddit_posts.csv')\n",
        "df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Title</th>\n",
              "      <th>Body</th>\n",
              "      <th>SubReddit</th>\n",
              "      <th>Clean_Title</th>\n",
              "      <th>Clean_Body</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>What things about React annoy you the most?</td>\n",
              "      <td>Can be anything: missing features, boilerplate...</td>\n",
              "      <td>reactjs</td>\n",
              "      <td>What things about React annoy you the most</td>\n",
              "      <td>Can be anything missing features boilerplate b...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Tutorial: Building a contacts manager using Vu...</td>\n",
              "      <td>missing</td>\n",
              "      <td>vuejs</td>\n",
              "      <td>Tutorial Building a contacts manager using Vue...</td>\n",
              "      <td>missing</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Having trouble deciding what design pattern sh...</td>\n",
              "      <td>The structure of my project is the following:\\...</td>\n",
              "      <td>vuejs</td>\n",
              "      <td>Having trouble deciding what design pattern sh...</td>\n",
              "      <td>The structure of my project is the following A...</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Prettier rule for this?</td>\n",
              "      <td>I use Prettier and the auto format on save opt...</td>\n",
              "      <td>reactjs</td>\n",
              "      <td>Prettier rule for this</td>\n",
              "      <td>I use Prettier and the auto format on save opt...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Conditional Rendering in Vue JS - Beginner Tut...</td>\n",
              "      <td>missing</td>\n",
              "      <td>vuejs</td>\n",
              "      <td>Conditional Rendering in Vue JS Beginner Tutorial</td>\n",
              "      <td>missing</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ],
            "text/plain": [
              "                                               Title  ...                                         Clean_Body\n",
              "0        What things about React annoy you the most?  ...  Can be anything missing features boilerplate b...\n",
              "1  Tutorial: Building a contacts manager using Vu...  ...                                            missing\n",
              "2  Having trouble deciding what design pattern sh...  ...  The structure of my project is the following A...\n",
              "3                            Prettier rule for this?  ...  I use Prettier and the auto format on save opt...\n",
              "4  Conditional Rendering in Vue JS - Beginner Tut...  ...                                            missing\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xcFGHztCrago",
        "colab_type": "code",
        "outputId": "70e7aba6-108f-4a64-a6a1-e01f97f84336",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df = df.loc[:, ['Clean_Title', 'Clean_Body', 'SubReddit']]\n",
        "df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\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>Clean_Title</th>\n",
              "      <th>Clean_Body</th>\n",
              "      <th>SubReddit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>What things about React annoy you the most</td>\n",
              "      <td>Can be anything missing features boilerplate b...</td>\n",
              "      <td>reactjs</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Tutorial Building a contacts manager using Vue...</td>\n",
              "      <td>missing</td>\n",
              "      <td>vuejs</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Having trouble deciding what design pattern sh...</td>\n",
              "      <td>The structure of my project is the following A...</td>\n",
              "      <td>vuejs</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Prettier rule for this</td>\n",
              "      <td>I use Prettier and the auto format on save opt...</td>\n",
              "      <td>reactjs</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Conditional Rendering in Vue JS Beginner Tutorial</td>\n",
              "      <td>missing</td>\n",
              "      <td>vuejs</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                         Clean_Title  ... SubReddit\n",
              "0         What things about React annoy you the most  ...   reactjs\n",
              "1  Tutorial Building a contacts manager using Vue...  ...     vuejs\n",
              "2  Having trouble deciding what design pattern sh...  ...     vuejs\n",
              "3                             Prettier rule for this  ...   reactjs\n",
              "4  Conditional Rendering in Vue JS Beginner Tutorial  ...     vuejs\n",
              "\n",
              "[5 rows x 3 columns]"
            ]
          },
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          "execution_count": 4
        }
      ]
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      "cell_type": "code",
      "metadata": {
        "id": "QRbZGhsGreQS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['Text'] = df['Clean_Title'] + ' ' + df['Clean_Body']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7rg-LvLRrtQ3",
        "colab_type": "code",
        "outputId": "550bbd18-4d43-4b85-82d0-68df03b88b4b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "subreddit = {\n",
        "    'reactjs': 0,\n",
        "    'angular': 1,\n",
        "    'vuejs': 2\n",
        "}\n",
        "\n",
        "df['SubReddit'] = df['SubReddit'].map(subreddit)\n",
        "df['SubReddit'].value_counts(dropna=False)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    993\n",
              "0    953\n",
              "2    871\n",
              "Name: SubReddit, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4N-c28oLsvzd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['Text'] = df['Text'].fillna('missing')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tKVRpf2UsKh3",
        "colab_type": "code",
        "outputId": "abdda390-742b-4af2-c2d2-fd22c27e4e39",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(df['Text'], df['SubReddit'], test_size=0.2, random_state=42)\n",
        "print(X_train.shape)\n",
        "print(X_test.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(2253,)\n",
            "(564,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MugYesBHsbcA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "word_vectorizer = TfidfVectorizer(\n",
        "    sublinear_tf=True,\n",
        "    strip_accents='unicode',\n",
        "    analyzer='word',\n",
        "    token_pattern=r'\\w{1,}',\n",
        "    stop_words='english',\n",
        "    ngram_range=(1, 1),\n",
        "    max_features=10000)\n",
        "\n",
        "word_vectorizer.fit(df['Text'])\n",
        "train_word_features = word_vectorizer.transform(X_train)\n",
        "test_word_features = word_vectorizer.transform(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MD2uwGm-slmJ",
        "colab_type": "code",
        "outputId": "80b04db0-3100-4748-9b53-ea3dc3c4c113",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "classifier = OneVsRestClassifier(LogisticRegression(class_weight = \"balanced\", C=0.5, solver='sag'))\n",
        "classifier.fit(train_word_features, y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "OneVsRestClassifier(estimator=LogisticRegression(C=0.5, class_weight='balanced',\n",
              "                                                 dual=False, fit_intercept=True,\n",
              "                                                 intercept_scaling=1,\n",
              "                                                 l1_ratio=None, max_iter=100,\n",
              "                                                 multi_class='warn',\n",
              "                                                 n_jobs=None, penalty='l2',\n",
              "                                                 random_state=None,\n",
              "                                                 solver='sag', tol=0.0001,\n",
              "                                                 verbose=0, warm_start=False),\n",
              "                    n_jobs=None)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_xGFLckwtIde",
        "colab_type": "code",
        "outputId": "de31f31e-259a-4dec-f879-fa0fe3b02b1f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "y_pred_ovr = classifier.predict(test_word_features)\n",
        "print(accuracy_score(y_test, y_pred_ovr))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8812056737588653\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0CCLrfXJtRuY",
        "colab_type": "code",
        "outputId": "e2e538c2-38b0-413e-bd62-6b1e00b7c6f7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "rf = RandomForestClassifier(n_estimators=100, max_depth=None, random_state=0)\n",
        "rf.fit(train_word_features, y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
              "                       max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
              "                       n_jobs=None, oob_score=False, random_state=0, verbose=0,\n",
              "                       warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KT7jjAQHtofR",
        "colab_type": "code",
        "outputId": "1f236132-b0f2-42cf-dd48-ae90f906205b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "y_pred_rf = rf.predict(test_word_features)\n",
        "print(accuracy_score(y_test, y_pred_rf))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8652482269503546\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UPQkxsbMtwIw",
        "colab_type": "code",
        "outputId": "aa98b1dd-4a6d-4b5a-f237-398f5f69e28e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        }
      },
      "source": [
        "\n",
        "# Creates a confusion matrix\n",
        "cm = confusion_matrix(y_test, y_pred_ovr) \n",
        "\n",
        "# Transform to df for easier plotting\n",
        "cm_df = pd.DataFrame(cm,\n",
        "                     index = ['React','Angular','Vue'], \n",
        "                     columns = ['React','Angular','Vue'])\n",
        "\n",
        "plt.figure(figsize=(8,5))\n",
        "sns.heatmap(cm_df, annot=True, fmt=\"d\")\n",
        "plt.title('One vs Rest Logistic Regression \\nAccuracy:{0:.3f}'.format(accuracy_score(y_test, y_pred_ovr)))\n",
        "plt.ylabel('True label')\n",
        "plt.xlabel('Predicted label')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JH7HUEr1m77fkEr34cPIUB9Aq5t/bllFUl6ykY8l6TQVcGRF/l9STbCDsCsC7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\nHBxAzczMcnAANTMzy+H/AS7IjcPhmAWtAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3y4X62dTx44R",
        "colab_type": "code",
        "outputId": "4cbdb9cf-7a08-41cb-88e8-bbc6c3ebf2f2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        }
      },
      "source": [
        "# Creates a confusion matrix\n",
        "cm = confusion_matrix(y_test, y_pred_rf) \n",
        "\n",
        "# Transform to df for easier plotting\n",
        "cm_df = pd.DataFrame(cm,\n",
        "                     index = ['React','Angular','Vue'], \n",
        "                     columns = ['React','Angular','Vue'])\n",
        "\n",
        "plt.figure(figsize=(8,5))\n",
        "sns.heatmap(cm_df, annot=True, fmt=\"d\")\n",
        "plt.title('Random Forest Classifier \\nAccuracy:{0:.3f}'.format(accuracy_score(y_test, y_pred_rf)))\n",
        "plt.ylabel('True label')\n",
        "plt.xlabel('Predicted label')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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yqNUh3Lkm0Ih4tLFbGefeHHhO0vuSXpf0hqTXG3uBpP6SXpL00uSpEyp9LzVt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\npWHRAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ozde9QQPy_B7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}