multi_text_classification_assignment_BirlaSoft.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "multi text classification_assignment_BirlaSoft.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPWkU7gokeLwFUWEg0qsAur",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/plaban1981/NLP-with-Python/blob/master/multi_text_classification_assignment_BirlaSoft.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Task : \n",
        "\n",
        "* Given input data with clean text after stopwords removal and after pre processing\n",
        "\n",
        "* Apply some multi label classification and get good accuracy"
      ],
      "metadata": {
        "id": "l2cz0y3_DV1D"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "PZq2lTOwgHK9",
        "outputId": "6d52ce32-6ab9-46d4-f8e8-9084630bb5eb"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-a8c59b19-73f2-49df-8e24-c1ed73e5b6e8\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-a8c59b19-73f2-49df-8e24-c1ed73e5b6e8\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving input_data (1).xlsx to input_data (1).xlsx\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "from google.colab import files\n",
        "files.upload()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Import Basic Data Science Libraries"
      ],
      "metadata": {
        "id": "SvsS2EPjEmTr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "PozNNKAsDl4p"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load data into a dataframe"
      ],
      "metadata": {
        "id": "Dkuhs7MNHaPG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_excel(\"/content/input_data (1).xlsx\")\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "wdyYF-OyHZPI",
        "outputId": "f03f7268-e519-455b-e6f8-c0295b555ba7"
      },
      "execution_count": 3,
      "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>Class</th>\n",
              "      <th>text_clean</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>side effect domperidone seen study included pa...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>two patient stopped domperidone side effect pa...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>treatment side effect reported xxxx patient co...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>patient side effect typically occurred early c...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>none side effect due significant cardiac arrhy...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0  Class                                         text_clean\n",
              "0           0      1  side effect domperidone seen study included pa...\n",
              "1           1      1  two patient stopped domperidone side effect pa...\n",
              "2           2      1  treatment side effect reported xxxx patient co...\n",
              "3           3      1  patient side effect typically occurred early c...\n",
              "4           4      1  none side effect due significant cardiac arrhy..."
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Removing the first column as it is not necessary"
      ],
      "metadata": {
        "id": "qy4xgYTbLDlz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.drop('Unnamed: 0',axis=1)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "o2ZIZwPfJu7s",
        "outputId": "8e90b1ee-491f-4c7c-be30-3a833561ab75"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Class</th>\n",
              "      <th>text_clean</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>two patient stopped domperidone side effect pa...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>treatment side effect reported xxxx patient co...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>patient side effect typically occurred early c...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>none side effect due significant cardiac arrhy...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1</td>\n",
              "      <td>difference adverse event seen among group</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Class                                         text_clean\n",
              "1      1  two patient stopped domperidone side effect pa...\n",
              "2      1  treatment side effect reported xxxx patient co...\n",
              "3      1  patient side effect typically occurred early c...\n",
              "4      1  none side effect due significant cardiac arrhy...\n",
              "5      1          difference adverse event seen among group"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FbGXUf7tMwP9",
        "outputId": "05bbda77-4bbb-4586-caea-4bd0f3282669"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1481, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Check for missing values"
      ],
      "metadata": {
        "id": "06jJBzZbSSKf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()[df.isnull().sum() > 0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CTDnAgbLSVNw",
        "outputId": "72d0b9ee-e7f9-4d3c-c9f0-ef4a880921cd"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "text_clean    49\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[df['text_clean'].isnull()]['Class'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z0yRLzD4TWto",
        "outputId": "084a59a7-b103-45a0-edfb-6bfbb0afefd4"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 3])"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* the clean_text column has some 49 rows as null and classes associated are 1 and 3"
      ],
      "metadata": {
        "id": "3k6iVvKiS3EZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Dropping rows with text_clean column having null values"
      ],
      "metadata": {
        "id": "aHc5MkDqUq1j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_new = df.dropna()"
      ],
      "metadata": {
        "id": "mSFhhkzUV-NT"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_new.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gx0LhjGjWFxW",
        "outputId": "71a80ccb-545f-439c-c7f3-eea9c0d27d38"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1432, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Class Count without dropping null values"
      ],
      "metadata": {
        "id": "L-VP8XQCXYhL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.Class.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z88lxRjVWpE5",
        "outputId": "143cda92-868b-414a-83ff-c2ce65b30728"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3    494\n",
              "2    494\n",
              "1    493\n",
              "Name: Class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Class Count post dropping null values"
      ],
      "metadata": {
        "id": "DI-Ehf4LX1bg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_new.Class.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JNOSFflCWuDh",
        "outputId": "23566fb0-e79f-44f3-ef90-82ab2ef3dd83"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2    494\n",
              "1    491\n",
              "3    447\n",
              "Name: Class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Visualize Labels"
      ],
      "metadata": {
        "id": "QnR8E-6VL0vQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x='Class',data=df_new)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "quwtNFxaL31O",
        "outputId": "be8cf3db-df30-49e6-a133-c2ff72ced7c2"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Data is balanced since all labels have equal proportion of data"
      ],
      "metadata": {
        "id": "RpxXaLrsM1Tp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Number of texts associated with each class"
      ],
      "metadata": {
        "id": "PB_CGZGlOC8C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "counts = []\n",
        "category = []\n",
        "for k,v in df_new.Class.value_counts().to_dict().items():\n",
        "  category.append(k)\n",
        "  counts.append(v)\n",
        "df_stats = pd.DataFrame({'category' :category, 'number_of_comments':counts})\n",
        "df_stats"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "9bv5FJI5OTBW",
        "outputId": "9d91005e-41bf-406a-f9f9-a3253caf62d8"
      },
      "execution_count": 45,
      "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>category</th>\n",
              "      <th>number_of_comments</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>494</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>491</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>447</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   category  number_of_comments\n",
              "0         2                 494\n",
              "1         1                 491\n",
              "2         3                 447"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_stats.plot(x='category', y='number_of_comments', kind='bar', legend=False, grid=True, figsize=(8, 5))\n",
        "plt.title(\"Number of texts per category\")\n",
        "plt.ylabel('# of Occurrences', fontsize=12)\n",
        "plt.xlabel('category', fontsize=12)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "WykMZrhFLfAR",
        "outputId": "50976cac-1dc1-4547-d0d6-5d9c147b547f"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'category')"
            ]
          },
          "metadata": {},
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_dummies = pd.get_dummies(df_new['Class'])"
      ],
      "metadata": {
        "id": "1TY2H7LPihHK"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_dummies.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "gpUpQLyait9H",
        "outputId": "30b9fcba-1ad5-445b-a6e7-a73febde6c35"
      },
      "execution_count": 59,
      "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",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>1</th>\n",
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              "      <th>3</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
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              "      <th>1</th>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <th>3</th>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "  </tbody>\n",
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            "text/plain": [
              "   1  2  3\n",
              "1  1  0  0\n",
              "2  1  0  0\n",
              "3  1  0  0\n",
              "4  1  0  0\n",
              "5  1  0  0"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "final_df = pd.concat([df_new,df_dummies],axis=1)\n",
        "final_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "aP3EVbadi2bj",
        "outputId": "1a569195-0e16-4fb2-e855-6eaeb16b06a4"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
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              "        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>Class</th>\n",
              "      <th>text_clean</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>treatment side effect reported xxxx patient co...</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>patient side effect typically occurred early c...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>none side effect due significant cardiac arrhy...</td>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1</td>\n",
              "      <td>difference adverse event seen among group</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Class                                         text_clean  1  2  3\n",
              "1      1  two patient stopped domperidone side effect pa...  1  0  0\n",
              "2      1  treatment side effect reported xxxx patient co...  1  0  0\n",
              "3      1  patient side effect typically occurred early c...  1  0  0\n",
              "4      1  none side effect due significant cardiac arrhy...  1  0  0\n",
              "5      1          difference adverse event seen among group  1  0  0"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "final_df = final_df.drop('Class',axis=1)\n",
        "final_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "RBE-vEXEjAot",
        "outputId": "4595aac7-80f5-4af4-daa3-0fed260b51cd"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>text_clean</th>\n",
              "      <th>1</th>\n",
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              "      <th>3</th>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                          text_clean  1  2  3\n",
              "1  two patient stopped domperidone side effect pa...  1  0  0\n",
              "2  treatment side effect reported xxxx patient co...  1  0  0\n",
              "3  patient side effect typically occurred early c...  1  0  0\n",
              "4  none side effect due significant cardiac arrhy...  1  0  0\n",
              "5          difference adverse event seen among group  1  0  0"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Split the data to train and test sets:"
      ],
      "metadata": {
        "id": "veo8Ho4_X7SZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "train, test = train_test_split(final_df, random_state=42, test_size=0.20, shuffle=True)\n",
        "X_train = train.text_clean\n",
        "X_test = test.text_clean\n",
        "print(X_train.shape)\n",
        "print(X_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wf-3rKLZRsic",
        "outputId": "3b3da875-6170-4508-9c86-715a3721cd00"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1145,)\n",
            "(287,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training Classifier Model\n",
        "\n",
        "* Making use of scikitlearn pipeline utility\n",
        "* Training on the below classifiers\n",
        "    - OneVsRest multi-label strategy\n",
        "    - Naive Bayes\n",
        "    - LinearSVC\n",
        "    - LogisticRegression"
      ],
      "metadata": {
        "id": "vNPOljFdZY5D"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## OneVsRest multi-label strategy\n",
        "The Multi-label algorithm accepts a binary mask over multiple labels. The result for each prediction will be an array of 0s and 1s marking which class labels apply to each row input sample.\n"
      ],
      "metadata": {
        "id": "BtX-8UyHg62S"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Import Required Machine Learning Libraries"
      ],
      "metadata": {
        "id": "Pd_gCxUCgAVh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.multiclass import OneVsRestClassifier\n",
        "from sklearn.svm import LinearSVC\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.pipeline import Pipeline\n",
        "\n",
        "#\n",
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "from nltk.corpus import stopwords\n",
        "#\n",
        "stop_words = set(stopwords.words('english'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_pXMlaWJZexQ",
        "outputId": "d574dfb2-463c-4e81-887c-4f18d841105a"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "categories = df_new.Class.unique().tolist()\n",
        "categories"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8Q0OG2jZgYpJ",
        "outputId": "5b26fd97-5721-4f97-8aa4-80e87a830a59"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 2, 3]"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## # Define a pipeline combining a text feature extractor with multi lable classifier"
      ],
      "metadata": {
        "id": "Y-F4e4BnhN7w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## Naive Bayes Pipeline\n",
        "NB_pipeline = Pipeline([\n",
        "                ('tfidf', TfidfVectorizer(stop_words=stop_words)),\n",
        "                ('clf', OneVsRestClassifier(MultinomialNB(\n",
        "                    fit_prior=True, class_prior=None))),\n",
        "            ])\n"
      ],
      "metadata": {
        "id": "EG2C2n9dgs93"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Processing the Naive Bayes pipeline"
      ],
      "metadata": {
        "id": "bOo0Zo7uhaFi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Naive_bayes_score = {}\n",
        "for category in categories:\n",
        "    print(f'Processing category : {category}')\n",
        "    # train the model using X_dtm & y\n",
        "    NB_pipeline.fit(X_train, train[category])\n",
        "    # compute the testing accuracy\n",
        "    prediction = NB_pipeline.predict(X_test)\n",
        "    print(f'Test accuracy for {category} is {accuracy_score(test[category], prediction)}')\n",
        "    Naive_bayes_score[category] = accuracy_score(test[category], prediction)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hI5oaEyNhZjQ",
        "outputId": "14f1ca68-9653-44d8-9542-8eef827d0247"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing category : 1\n",
            "Test accuracy for 1 is 0.8013937282229965\n",
            "Processing category : 2\n",
            "Test accuracy for 2 is 0.8013937282229965\n",
            "Processing category : 3\n",
            "Test accuracy for 3 is 0.7073170731707317\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Definining LinearSVC Pipeline"
      ],
      "metadata": {
        "id": "5y0hhYBIjyUV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "SVC_pipeline = Pipeline([\n",
        "                ('tfidf', TfidfVectorizer(stop_words=stop_words)),\n",
        "                ('clf', OneVsRestClassifier(LinearSVC(), n_jobs=-1)),\n",
        "            ])"
      ],
      "metadata": {
        "id": "lXpkKpw0iH8P"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Processing the LinearSVC pipeline"
      ],
      "metadata": {
        "id": "vbMIydfrj95L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "LinearSVC_score = {}\n",
        "for category in categories:\n",
        "    print(f'Processing category : {category}')\n",
        "    # train the model using X_dtm & y\n",
        "    SVC_pipeline.fit(X_train, train[category])\n",
        "    # compute the testing accuracy\n",
        "    prediction = SVC_pipeline.predict(X_test)\n",
        "    print(f'Test accuracy for {category} is {accuracy_score(test[category], prediction)}')\n",
        "    LinearSVC_score[category] = accuracy_score(test[category], prediction)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WRgb8ZlOkB-Y",
        "outputId": "e651d749-8af6-4957-c2ce-024bc2b3da67"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing category : 1\n",
            "Test accuracy for 1 is 0.8048780487804879\n",
            "Processing category : 2\n",
            "Test accuracy for 2 is 0.818815331010453\n",
            "Processing category : 3\n",
            "Test accuracy for 3 is 0.7386759581881533\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Defining the LogisticRegression Pipeline"
      ],
      "metadata": {
        "id": "G8BzEp8kkTDx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "LogReg_pipeline = Pipeline([\n",
        "                ('tfidf', TfidfVectorizer(stop_words=stop_words)),\n",
        "                ('clf', OneVsRestClassifier(LogisticRegression(solver='sag'), n_jobs=1)),\n",
        "            ])"
      ],
      "metadata": {
        "id": "60P4BlGJkX7h"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Processing the Logistic Regression Pipeline"
      ],
      "metadata": {
        "id": "ZTogSyUJkacA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lr_score = {}\n",
        "for category in categories:\n",
        "    print(f'Processing category : {category}')\n",
        "    # train the model using X_dtm & y\n",
        "    LogReg_pipeline.fit(X_train, train[category])\n",
        "    # compute the testing accuracy\n",
        "    prediction = LogReg_pipeline.predict(X_test)\n",
        "    print(f'Test accuracy for {category} is {accuracy_score(test[category], prediction)}')\n",
        "    lr_score[category] = accuracy_score(test[category], prediction)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mmp8xxFAldb4",
        "outputId": "c21d4202-fc2b-414e-b586-1b1be717e327"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing category : 1\n",
            "Test accuracy for 1 is 0.8048780487804879\n",
            "Processing category : 2\n",
            "Test accuracy for 2 is 0.7979094076655052\n",
            "Processing category : 3\n",
            "Test accuracy for 3 is 0.735191637630662\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Defining Decision Tree Pipeline"
      ],
      "metadata": {
        "id": "kwtwN1gWqBHT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dt_pipeline = Pipeline([\n",
        "                ('tfidf', TfidfVectorizer(stop_words=stop_words)),\n",
        "                ('clf', OneVsRestClassifier(DecisionTreeClassifier(random_state=42), n_jobs=1)),\n",
        "            ])"
      ],
      "metadata": {
        "id": "0GEQZ7OonUOB"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dt_score = {}\n",
        "for category in categories:\n",
        "    print(f'Processing category : {category}')\n",
        "    # train the model using X_dtm & y\n",
        "    dt_pipeline.fit(X_train, train[category])\n",
        "    # compute the testing accuracy\n",
        "    prediction = dt_pipeline.predict(X_test)\n",
        "    print(f'Test accuracy for {category} is {accuracy_score(test[category], prediction)}')\n",
        "    dt_score[category] = accuracy_score(test[category], prediction)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UriZA9App24f",
        "outputId": "2f94f5db-7925-419e-8f3f-e7e0acf1af0c"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing category : 1\n",
            "Test accuracy for 1 is 0.7386759581881533\n",
            "Processing category : 2\n",
            "Test accuracy for 2 is 0.7839721254355401\n",
            "Processing category : 3\n",
            "Test accuracy for 3 is 0.6341463414634146\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Defining a RandomForest Classifier Pipeline"
      ],
      "metadata": {
        "id": "gDvK91_YthLI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rf_pipeline = Pipeline([\n",
        "                ('tfidf', TfidfVectorizer(stop_words=stop_words)),\n",
        "                ('clf', OneVsRestClassifier(RandomForestClassifier(random_state=42), n_jobs=1)),\n",
        "            ])"
      ],
      "metadata": {
        "id": "yLATIYG6tnuT"
      },
      "execution_count": 215,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rf_score = {}\n",
        "for category in categories:\n",
        "    print(f'Processing category : {category}')\n",
        "    # train the model using X_dtm & y\n",
        "    rf_pipeline.fit(X_train, train[category])\n",
        "    # compute the testing accuracy\n",
        "    prediction = rf_pipeline.predict(X_test)\n",
        "    print(f'Test accuracy for {category} is {accuracy_score(test[category], prediction)}')\n",
        "    rf_score[category] = accuracy_score(test[category], prediction)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7Jv-ncPatu7U",
        "outputId": "5dca296d-11fb-4dc4-9c56-ab28c26a59aa"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing category : 1\n",
            "Test accuracy for 1 is 0.8257839721254355\n",
            "Processing category : 2\n",
            "Test accuracy for 2 is 0.8153310104529616\n",
            "Processing category : 3\n",
            "Test accuracy for 3 is 0.7177700348432056\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Comparing Models"
      ],
      "metadata": {
        "id": "R1GG3mjluA-c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_scores = {'RandomForestClassifier':rf_score,\n",
        "                'DecisionTreeClassifier':dt_score,\n",
        "                'LogisticRegression':lr_score,\n",
        "                'LinearSVC':LinearSVC_score,\n",
        "                'Naive_bayes':Naive_bayes_score\n",
        "                }"
      ],
      "metadata": {
        "id": "lCxcsBMRuDNz"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "name =[]\n",
        "label1 = []\n",
        "label2 = []\n",
        "label3 = []\n",
        "for k,v in model_scores.items():\n",
        "  name.append(k)\n",
        "  label1.append(v[1])\n",
        "  label2.append(v[2])\n",
        "  label3.append(v[3])"
      ],
      "metadata": {
        "id": "DVUI84T3uobN"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "name"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QjJGDNRFvSyA",
        "outputId": "cf559d91-8e95-4b0a-b8cf-d36449e44f3b"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['RandomForestClassifier',\n",
              " 'DecisionTreeClassifier',\n",
              " 'LogisticRegression',\n",
              " 'LinearSVC',\n",
              " 'Naive_bayes']"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_scores_df = pd.DataFrame({\"Model_name\":name,\n",
        "                                \"Class 1 scores\":label1,\n",
        "                                \"Class 2 scores\":label2,\n",
        "                                \"Class 3 scores\":label3,\n",
        "                                })\n",
        "model_scores_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "dxy7eh8zu8_O",
        "outputId": "154023ec-7ad7-4643-962b-0fe2cd73f819"
      },
      "execution_count": 83,
      "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>Model_name</th>\n",
              "      <th>Class 1 scores</th>\n",
              "      <th>Class 2 scores</th>\n",
              "      <th>Class 3 scores</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>RandomForestClassifier</td>\n",
              "      <td>0.825784</td>\n",
              "      <td>0.815331</td>\n",
              "      <td>0.717770</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>DecisionTreeClassifier</td>\n",
              "      <td>0.738676</td>\n",
              "      <td>0.783972</td>\n",
              "      <td>0.634146</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>LogisticRegression</td>\n",
              "      <td>0.804878</td>\n",
              "      <td>0.797909</td>\n",
              "      <td>0.735192</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>LinearSVC</td>\n",
              "      <td>0.804878</td>\n",
              "      <td>0.818815</td>\n",
              "      <td>0.738676</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Naive_bayes</td>\n",
              "      <td>0.801394</td>\n",
              "      <td>0.801394</td>\n",
              "      <td>0.707317</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               Model_name  Class 1 scores  Class 2 scores  Class 3 scores\n",
              "0  RandomForestClassifier        0.825784        0.815331        0.717770\n",
              "1  DecisionTreeClassifier        0.738676        0.783972        0.634146\n",
              "2      LogisticRegression        0.804878        0.797909        0.735192\n",
              "3               LinearSVC        0.804878        0.818815        0.738676\n",
              "4             Naive_bayes        0.801394        0.801394        0.707317"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Comparing the accuracy scores for all base models we can find that RandomForesClassifier gives the best performance"
      ],
      "metadata": {
        "id": "YEp2vc40v85F"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Hyperparameter Tuning RandomForestClassifier"
      ],
      "metadata": {
        "id": "1yJ03Lh-wMtc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_new.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "1XjSToJCu99z",
        "outputId": "0c89863e-b66c-4c28-f3f8-5e84f9227c85"
      },
      "execution_count": 124,
      "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>Class</th>\n",
              "      <th>text_clean</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>two patient stopped domperidone side effect pa...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>treatment side effect reported xxxx patient co...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>patient side effect typically occurred early c...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>none side effect due significant cardiac arrhy...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1</td>\n",
              "      <td>difference adverse event seen among group</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Class                                         text_clean\n",
              "1      1  two patient stopped domperidone side effect pa...\n",
              "2      1  treatment side effect reported xxxx patient co...\n",
              "3      1  patient side effect typically occurred early c...\n",
              "4      1  none side effect due significant cardiac arrhy...\n",
              "5      1          difference adverse event seen among group"
            ]
          },
          "metadata": {},
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def rep_labels(x):\n",
        "  x -=1\n",
        "  return x"
      ],
      "metadata": {
        "id": "_HATGu3sKoQB"
      },
      "execution_count": 127,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_new['Class'] = df_new['Class'].map(rep_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IxPOJg-SK8G0",
        "outputId": "084260bc-1c78-499f-cf18-3592268e2457"
      },
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_new.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Ru_YI7_rLWMN",
        "outputId": "84e8e470-127a-48cb-e22c-03cfeec64e52"
      },
      "execution_count": 129,
      "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>Class</th>\n",
              "      <th>text_clean</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>two patient stopped domperidone side effect pa...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>treatment side effect reported xxxx patient co...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>patient side effect typically occurred early c...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>none side effect due significant cardiac arrhy...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0</td>\n",
              "      <td>difference adverse event seen among group</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Class                                         text_clean\n",
              "1      0  two patient stopped domperidone side effect pa...\n",
              "2      0  treatment side effect reported xxxx patient co...\n",
              "3      0  patient side effect typically occurred early c...\n",
              "4      0  none side effect due significant cardiac arrhy...\n",
              "5      0          difference adverse event seen among group"
            ]
          },
          "metadata": {},
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Defining a RandomForestClassifier Pipeline with out One Versus Rest startegy"
      ],
      "metadata": {
        "id": "JWP8ydhPw-bC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rf_pipeline = Pipeline([\n",
        "                ('tfidf', TfidfVectorizer(stop_words=stop_words)),\n",
        "                ('rf', RandomForestClassifier()),\n",
        "            ])"
      ],
      "metadata": {
        "id": "RXR3UWZcwKT7"
      },
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Train Test Split"
      ],
      "metadata": {
        "id": "OVffgFAuxRWy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_new.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "dmMI934UHuUL",
        "outputId": "3ae67596-e3a2-4bc3-9b33-f01287ee743a"
      },
      "execution_count": 132,
      "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>Class</th>\n",
              "      <th>text_clean</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>two patient stopped domperidone side effect pa...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>treatment side effect reported xxxx patient co...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>patient side effect typically occurred early c...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>none side effect due significant cardiac arrhy...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0</td>\n",
              "      <td>difference adverse event seen among group</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Class                                         text_clean\n",
              "1      0  two patient stopped domperidone side effect pa...\n",
              "2      0  treatment side effect reported xxxx patient co...\n",
              "3      0  patient side effect typically occurred early c...\n",
              "4      0  none side effect due significant cardiac arrhy...\n",
              "5      0          difference adverse event seen among group"
            ]
          },
          "metadata": {},
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df_new['text_clean']\n",
        "y = df_new['Class']"
      ],
      "metadata": {
        "id": "ur7OVuhrxdEi"
      },
      "execution_count": 169,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train,X_test,y_train,y_test = train_test_split(X,y,random_state=42, test_size=0.20, stratify =y,shuffle=True)\n",
        "print(X_train.shape,y_train.shape)\n",
        "print(X_test.shape,y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t2ACq56vxUsi",
        "outputId": "3b2699e5-3c5e-46d0-8bf7-30527b7ec7da"
      },
      "execution_count": 170,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1145,) (1145,)\n",
            "(287,) (287,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "params = {\n",
        "    'rf__max_depth': [2,4, 5],\n",
        "    'rf__max_features': [2, 3],\n",
        "    'rf__min_samples_leaf': [3, 4, 5],\n",
        "    'rf__n_estimators': [100, 200, 300]\n",
        "}"
      ],
      "metadata": {
        "id": "96xUKNuoyx7x"
      },
      "execution_count": 172,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rf_gs = GridSearchCV(rf_pipeline,param_grid=params,n_jobs=-1, verbose=1, cv=3, scoring='accuracy')\n",
        "rf_gs.fit(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cl6VPxIkFRGX",
        "outputId": "21adc9a1-ef08-4f77-ac6b-34187f45687f"
      },
      "execution_count": 193,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 3 folds for each of 54 candidates, totalling 162 fits\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=3,\n",
              "             estimator=Pipeline(steps=[('tfidf',\n",
              "                                        TfidfVectorizer(stop_words={'a',\n",
              "                                                                    'about',\n",
              "                                                                    'above',\n",
              "                                                                    'after',\n",
              "                                                                    'again',\n",
              "                                                                    'against',\n",
              "                                                                    'ain',\n",
              "                                                                    'all', 'am',\n",
              "                                                                    'an', 'and',\n",
              "                                                                    'any',\n",
              "                                                                    'are',\n",
              "                                                                    'aren',\n",
              "                                                                    \"aren't\",\n",
              "                                                                    'as', 'at',\n",
              "                                                                    'be',\n",
              "                                                                    'because',\n",
              "                                                                    'been',\n",
              "                                                                    'before',\n",
              "                                                                    'being',\n",
              "                                                                    'below',\n",
              "                                                                    'between',\n",
              "                                                                    'both',\n",
              "                                                                    'but', 'by',\n",
              "                                                                    'can',\n",
              "                                                                    'couldn',\n",
              "                                                                    \"couldn't\", ...})),\n",
              "                                       ('rf',\n",
              "                                        RandomForestClassifier(max_depth=5,\n",
              "                                                               max_features=3,\n",
              "                                                               min_samples_leaf=300,\n",
              "                                                               random_state=42))]),\n",
              "             n_jobs=-1,\n",
              "             param_grid={'rf__max_depth': [2, 4, 5], 'rf__max_features': [2, 3],\n",
              "                         'rf__min_samples_leaf': [3, 4, 5],\n",
              "                         'rf__n_estimators': [100, 200, 300]},\n",
              "             scoring='accuracy', verbose=1)"
            ]
          },
          "metadata": {},
          "execution_count": 193
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Obtain the best Parameters obtained from GridDearchCV"
      ],
      "metadata": {
        "id": "FWrlrRITIM8g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rf_gs.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hE5swvFPF6Tj",
        "outputId": "28b737c1-5ce5-4f8a-b1c6-8f1f99c92607"
      },
      "execution_count": 194,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'rf__max_depth': 4,\n",
              " 'rf__max_features': 3,\n",
              " 'rf__min_samples_leaf': 3,\n",
              " 'rf__n_estimators': 300}"
            ]
          },
          "metadata": {},
          "execution_count": 194
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Choosing the best models giving a good onevsrest scores\n",
        "\n",
        "1. RandomForestClassifier\n"
      ],
      "metadata": {
        "id": "Rttg90c2Ox-B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rf_pipeline = Pipeline([\n",
        "                ('tfidf', TfidfVectorizer(stop_words=stop_words)),\n",
        "                ('clf', OneVsRestClassifier(RF, n_jobs=1)),\n",
        "            ])"
      ],
      "metadata": {
        "id": "2f3jhDbPR_gV"
      },
      "execution_count": 205,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rf_pipeline.fit(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5wVJxvHsKD7k",
        "outputId": "c04c1c48-b9a5-4d66-922f-66a67030be61"
      },
      "execution_count": 216,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('tfidf',\n",
              "                 TfidfVectorizer(stop_words={'a', 'about', 'above', 'after',\n",
              "                                             'again', 'against', 'ain', 'all',\n",
              "                                             'am', 'an', 'and', 'any', 'are',\n",
              "                                             'aren', \"aren't\", 'as', 'at', 'be',\n",
              "                                             'because', 'been', 'before',\n",
              "                                             'being', 'below', 'between',\n",
              "                                             'both', 'but', 'by', 'can',\n",
              "                                             'couldn', \"couldn't\", ...})),\n",
              "                ('clf',\n",
              "                 OneVsRestClassifier(estimator=RandomForestClassifier(random_state=42),\n",
              "                                     n_jobs=1))])"
            ]
          },
          "metadata": {},
          "execution_count": 216
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions=rf_pipeline.predict(X_test)"
      ],
      "metadata": {
        "id": "clByAN7QKLNv"
      },
      "execution_count": 217,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Evaluate the predictions made by the Model"
      ],
      "metadata": {
        "id": "iTlTORiWImP4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix,accuracy_score,classification_report,plot_roc_curve\n",
        "cf = confusion_matrix(y_test,predictions)\n",
        "accuracy = accuracy_score(y_test,predictions)\n",
        "sns.heatmap(cf,annot=True,fmt=\".2f\",xticklabels=[1,2,3],yticklabels=[1,2,3])\n",
        "plt.xlabel('Predicted Values')\n",
        "plt.ylabel('Actual Values')\n",
        "plt.title(f'Confusion Matrix : Validation Accuracy score RandomForestClassifier: {accuracy}')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "3zt1t09kJq3s",
        "outputId": "a2410c47-bc2f-4175-db28-558f3d4917bb"
      },
      "execution_count": 218,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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fVqU9YTvcKmZ4Vcq1qapyrIlfdxPi1l0zdz8fwN0/dvf+RLe0XmHthUt5+8o7lH/yWI9FT+68SFQLlh2OT28S1vMGthvc/Rl3PyCU04lukVbXT0Rt1mLL3ShuO97gsTDODKBLTH8XIN/dfy5n3M/LSYtdVlfgcXdf7O6/EjWA3cfM2oSLinlx48d+7g38Jea4shUwwsyuqEZZNkYvon3lx7Dcy4gucqYBhADjOnfv5O4didbXfNZul12Jak9WuPtyoluJpYFZ6fbyClHZz60kL075ccQZRMetWF2J2jxNDXn8mKidWx+iYKob8FwoU2lj5XlWztNgsTYYxLj7EqIGkfeZ2THharJBiOrvCKMNB64xs7Zm1iaMX+njxBWYDhxkZlubWSbwt9IBZpZtZv3NrClRYLWcqCos3pvAThY9Fl7fzE4kqv56fSPzBECIKHsCV5czuDnRAXIhUN/MrmXdK4V8oFN1nk4ws52IWpGfRhTVXh4i8I0SrhbeJ/q+xrh76RVuc6L1+TPRSfnWKs7yQ6Iy/yVsE38kartQqjnRlUyRmbUCroubPp+ovUJ5eS0mOoDfYmbNzWwbohbtG7NdHUN01deZaCfqStSeYyLRjvYR0QnxNjNramaNzKxHmPZh4DIz2ys8XbJDyAtE2+opoTahL+s//RWvwvXh7rlEV3/3m1nLsD4Pipn2FWBPorYFT1S0ADM7KuTRiILoYqJ9ZENlHA4MMbNtLXoC4VaiNjgVXQ09CPzNzHYNy8w0s+q8HuE2om25ERu/7UHUhmhXM/tjCM7+wrqBZHXLtTGqe6x5PYx/eviOG5jZ3mb2OzNraNH7rjLdfTVRG7bS41s+0DocE0tdB3Q3s3+aWXuA8N0/VU4w3JDoYmghsMbM+rG2JqLC7cbMdjazQ8JJ7RfWNjCurgeJ9uVtwvLamtmmPN36BDDIzDqHsl5DdOuxPO8R3ab7W/iOehDVpr8dhn8MnBG24wZEAfaCmFrbx4CLLHr0uCXRBVDp99sb2I21x5UFRCf9+0I5M8J2Xh+oF/a7sqduwnfeiCiYbBCG1wvDLAxrGPob2drHyIcSXVyVLvdBov3h8DBuKzPbPsyjM1Ht0Y3uXvrdfQycbdF72hoDgwnBXsjfC0Tf9cCYaUrzfLCZbRPmvRXR/jwybpzuRLWR8e9++xg4sPQ8ZmZ7ELW/+pxou+sQU6bSoGovokCnYpXdbwp3C04lui+4gqh69w1CK3yihnH/JTpI5obPpQ1ae7H+vdw5rL1Xej3rtxW5j+ie82yithtOtBHkEDUKWhKGj2dtg7gzWffppAOInhBaEv4fEDNsPOu2O1ln2ri8rJf/mGFl9zmJ2nA8SnTgySWqNYktZ2uiAKIQmFZePmLTQnk/Aq6MGXY+0f3JLcrJy9ZEQV25DeHiyuqse++/GdFGuIyoKvwM1m1g+zgVP53UjegecunTSc/FjNshlGc5UdXluaXfZRi+f0gvBP4b0mKX25IoaFlIdCV3LXFPJ8WVbZ178jHpbwH/Lid9ANG2XD+sv1eITqaLSvMTxjuP6KmM5URPCu0RU/YZoexPEp00y11PVVwfpY8z5od18lLc9A8T7X/NNvD9DiHa7lYQXUX9PW4bWa+MRBcy14Z1vDCs89KG951i8xgzr9OJtsWlYbpHK8jPetMTHbBnABdRjW2vgu2vb1iXFT2dVOVyhfXVK6b/KULbOTZ8jKjysSak7Ux0/Cx9SuxdooN2Q6JttTCs14/j5vVoGL+ItQ1tdyY6Ufwclv8Z0e2fjPgyEj2Bkh+mf5J1nyQsd7sheuz4o/D9LCY6eZcu+3qq3iamHtFFyNdhXt8Rni6q4Luo9HgW5pcf1tVjxBwXibavU2P6dyW66FpB9PRXbMPy1kS10gVh3bwP7BMzvAHRbY4iouNF2fmtnDyVlTnueBvbPR63fcQP7xW3XmK7ORUsN/672Cms65VE+9UlceNvS3Sr6Ofwvb4F7BiG9QzLWsnap+GWs7bB+SVENTorifat/wLN4+b/EKERfTl5vZDo3F76lNylVT12VNSVPvokInVYqN3byd1PS3ZeRETqirR4CZdIOgu3nwYR1YCIiEigH4AUqcPM7ByiattR7v5eZeOLiGxOdDtJREREUpJqYkRERCQlqU2MJMyqdx5UtV4N23nAfcnOQtprlKEfOa8N3yycWpX3AVVo9aLvq3y8adBmu01altRdqokRSREKYERE1qWaGBERST0lxcnOgdQBCmJERCT1FCfy5cuSqhTEiIhIyol7I75sphTEiIhI6ilRECMKYkREJBWpJkZQECMiIqlIDXsFPWItIiKpyEuq3lWBmQ0xsxlm9qWZDTezRma2rZlNMbPZZvacmTWs4VJJNSmIERGRlOPFa6rcVcbMtgT+AnRz992ADOAk4HbgLnffASgk+iFWqUMUxIiISOopKal6VzX1gcZmVh9oAuQChwAvhOHDgGMSXg7ZJApiREQk9VTjdpKZDTazqTHd4HVm5T4f+BfwI1HwsgT4BChy99KqnHnAlrVZRKmcGvaKiEjqqUbDXncfCgytaLiZtQT6A9sCRcDzQN9NzKHUAgUxIiKSehL7iHUf4Ad3XwhgZi8BPYAsM6sfamM6AvMTuVDZdLqdJCIiqad4TdW7yv0I7GdmTczMgN7AV8A44PgwzkBgZI2URTaaghgREUk9CWzY6+5TiBrwTgO+IDo3DgWuAC4xs9lAa+CRmiuQbAzdThIRkZTjntiX3bn7dcB1ccnfA/skdEGSUApiREQk9ehnBwQFMSIikor0A5CCghgREUlFqokRFMSIiEgqKl6d7BxIHaAgRkREUo9uJwkKYkREJBXpdpKgIEZERFKRamIEBTEiIpKKFMQICmJERCQFuRr2CgpiREQkFalNjKAgRkREUpFuJwkKYkREJBWpJkZQECMiIqlINTGCghgREUlFqokRFMSIiEgqWrMm2TmQOqBesjMgkghz8hcz4Nanyroel97HU+9OKxv+xDuf0PXPd1G4fFW50786eQZHX/8YR1//GK9OnlGW/tWP+Rx/yxMcfd2j3D5iHO5e42Wpy/55z41M+3o8Yya9VJZ2ZP/DeOeDl5mz6DN279q5wml79u7BuCmv8t7UN7jg4kFl6VttvSUjxzzNe1Pf4L5H/kmDBpv3tdWtd1/Lh1+N5vX3nitLu/jK83h1/HBGjnuaR0fcS7vsNuVOe+yJRzJ6ykuMnvISx554ZFn6rrvvwmsTnmXMRy9zza2X1XgZaoWXVL2TtKUgRtJCp+xWjLjqNEZcdRrDrzyFRg3qc0iXHQDIK1zGh7PmktOyebnTLlnxCw+9OZmn/u9knr78ZB56czJLV/4CwC3PjuXaUw7l1evP4seFRUz6ak5tFalOev6ZkZxxwvnrpH0981sGnzGEKR98UuF09erV4+Y7rmbggAvovX9//nBcP3bceTsA/nb9EB5+4EkO6nYkS4qWcuJpf6zRMtR1Lz37GoNOumidtIfvfZI/9DqZ/gefyrgxE/nzZeesN11mVgsuvOwcTjj8TI4/bCAXXnYOLTKjbf6Gf/6Nay65mUP3OZZO223FQb2710pZalRJSdU7SVsKYiTtTPn6Jzq2zaRD6xYA/OuF8fz1mAPBrNzxP5g5h/122YbMpo1o0aQR++2yDZO+msPCJctZ8ctv7L5tDmbGUfv+jnGffVebRalzPvrwE4oKl6yTNvubH/h+9pwNTtd1r98z54cf+XHuPFavXsNrL43isH4HA9D9wH14c+QYAF549lUOP/KQGsl7qpj64acsKVy6TtqK5SvKPjdp0rjcGsEDDt6fSRM+YknRUpYuWcakCR9x4CHdaZvdmmbNm/LZJ18C8PJzb9KnX68aLUOtUE2MoDYxkobenvo1/fbaBYBxn31H26xm7NyxbYXjFxQtp31MLU12y2YUFC2noGg52VnN1qZnNaNgyfKay3gaa5/TjgXz88r6cxfk03Wv3WnZKoulS5ZRXFwc0vNon9MuWdms04ZcdQHHDDiCZUtXcPqx5643PDunLbkL8sv68xbkk53Tluz27ciLSc/PjdJTnmpYBNXESJpZvaaYCV98x6F77siq31bzyNsfccFRaVB1Lpu9u269n55dj+K1F0dx+qAByc5O8iWwJsbMdjaz6THdUjP7q5m1MrMxZvZt+N+yFkom1aAgRiplZmdtYNhgM5tqZlMfeWNibWarXO/PmMMuW7WjdYumzFu4hPk/L2HArU/R7++PUFC0jJNve5pFS1asM027rGbkFS4r688vXE67rGa0y2pGftHampf8ouW0y2yGVF9ebgEdtmxf1p/TIZv83HwKFxfRIrM5GRkZIb09ebkFycpmSnj1hVEcdlTv9dLzcxeS0yG7rL99h2zycxeSn1dA+5j07JwoPeWtWVP1rhLu/rW7d3X3rsBewErgZeBKYKy77wiMDf1ShyiIkaq4oaIB7j7U3bu5e7dBRx5Ym3kq11ufzKJvt+hW0o5btmHc7ecx6qZBjLppEO2ymjP8ylNpk9l0nWm6/64TH86ay9KVv7B05S98OGsu3X/XibaZzWjaqCGf/5CLu/P6lJn02n37ZBQr5X027Uu23W4bttp6Sxo0qM/Rf+zHmLfGA/Dh+x9zRP9DATj+pD8w+s1xScxp3bTNdluVfe7Tr1e5bZDeH/chPXrtS4vM5rTIbE6PXvvy/rgPWZj/M8uXraDLXrsBcOyJRzD2rQm1lfWa4171rnp6A9+5+1ygPzAspA8DjklgCSQB1CZGADCzzysaBGRXMKxOWfXraibP+pFrTu5T6bgz5ubxwvtfcN2ph5LZtBGD++7Lqbc/A8DgfvuR2bQRAFedeAjXPjmaX1evoUfnThywa6eaLEKdd8//bmf/HnvTsnUWU758hztvu4+iwiXcePtVtGrdkseevZ+vvpzF6cefR3b7ttx+9w2ceeIFFBcX8/fLb+XJFx4kIyOD555+mW9mRY2k/3H9Xdz78B3831UXMeOLWTz31EuV5CK93fnQLezTYy9atsrivc/e4L93DKVnnx5su/02lJSUsGBeLtdd9g8AduvyO04+8ziuHnIzS4qWcv+dj/DimCcAuO/fD7OkKGogfP3lt3HbPdfTqNEWvPfuB0x4Z1LSypcw1WgTY2aDgcExSUPdfWgFo58EDA+fs909N3zOI0WOhZsT29zfeyERM8sHDgcK4wcBH7h7h8rmseqdB7Ux1aCdB9yX7CxsFhplbJHsLGwWvlk4tfzHBato1dN/r/LxpvGpN1VpWWbWEFgA7Oru+WZW5O5ZMcML3V3tYuoQ1cRIqdeBZu4+PX6AmY2v/eyIiGxAzTw63Q+Y5u6lj3Plm1mOu+eaWQ6gBlt1jIIYAcDdB21g2Cm1mRcRkUqFx/IT7GTW3koCeBUYCNwW/o+siYXKxlMQIyIiqSfB74kxs6bAoUDsS3huA0aY2SBgLqBn2+sYBTEiIpJ6EhzEuPsKoHVc2s9ETytJHaUgRkREUo9+TkBQECMiIinIS/QwpCiIERGRVKTfThIUxIiISCqqmaeTJMUoiBERkdSjmhhBQYyIiKQiBTGCghgREUlF+skcQUGMiIikItXECApiREQkFekRa0FBjIiIpCI9nSQoiBERkRTkup0kKIgREZFUpNtJgoIYERFJRfrtJEFBjIiIpCLVxAgKYkREJBWtUcNeURAjIiKpSLeTBAUxIiKSinQ7SVAQIyIiKUiPWAtAvWRnQEREpNpKvOpdFZhZlpm9YGazzGymme1vZq3MbIyZfRv+t6zhUkk1KYgREZHUk+AgBrgbeMvddwG6ADOBK4Gx7r4jMDb0Sx2i20kiIpJ6EvizA2aWCRwEnAng7r8Bv5lZf6BXGG0YMB64ImELlk2mmhgREUk5XuJV7sxssJlNjekGx81uW2Ah8JiZfWpmD5tZUyDb3XPDOHlAdm2WUSqnmhgREUk91Xg6yd2HAkM3MEp9YE/gInefYmZ3E3fryN3dzPRIVB2jmhgREUk9JSVV7yo3D5jn7lNC/wtEQU2+meUAhP8FNVIW2WgKYkREJPUksGGvu+cBP5nZziGpN/AV8CowMKQNBEbWRFFk4+l2koiIpJ7Ev+zuIuBpM2sIfA+cRbXfSTIAACAASURBVHShP8LMBgFzgQGJXqhsGgUxIiKScrw4sS+7c/fpQLdyBvVO6IIkoRTESMI0P+KmZGch7a1aMDHZWUh7W+9wVLKzIFWhnx0QFMSIpAwFMCJruYIYQUGMiIikIgUxgoIYERFJRfr9R0FBjIiIpCBfoyhGFMSIiEgqUgwj6GV3acnM7jCzFmbWwMzGmtlCMzst2fkSEUmU6vx2kqQvBTHp6TB3XwocBcwBdgD+L6k5EhFJpJJqdJK2dDspPZV+r0cCz7v7EjNLZn5ERBJKNSwCCmLS1etmNgtYBZxvZm2BX5KcJxGRxFENi6AgJi25+5VmdgewxN2LzWwl0D/Z+RIRSRRfk+wcSF2gNjFpyMyaABcAD4SkDpT/myAiIinJS6reSfpSEJOeHgN+A7qH/vnAzcnLjohIgqlhr6AgJl1t7+53AKsB3H0loJa9IpI2VBMjoDYx6eo3M2sMOICZbQ/8mtwsiYgkjoITAQUx6eo64C1gKzN7GugBnJnUHImIJJAXq3JZFMSkJXcfY2bTgP2IbiNd7O6LkpwtEZGEUU2MgIKYtGRmB4WPy8L/zmaGu7+XrDyJiCSSl6gmRhTEpKvYnxhoBOwDfAIckpzsiIgklmpiBBTEpCV3Pzq238y2Av6TpOyIiCSce2JrYsxsDlHtdTGwxt27mVkr4DmgE9Hv0A1w98KELlg2iR6x3jzMA36X7EyIiCRKDT1ifbC7d3X30peDXgmMdfcdgbGhX+oQ1cSkITO7h/B4NVGg2hWYlrwciYgkVkntPJ3UH+gVPg8DxgNX1MaCpWoUxKSnqTGf1wDD3X1SsjIjIpJo1WnYa2aDgcExSUPdfWj8LIHRZubAQ2F4trvnhuF5QPYmZFlqgIKYNOTuw5KdBxGRmlSdICYEJPFBS7wD3H2+mbUDxpjZrLh5eAhwpA5REJNGzOwL1t5GWmcQ0T64ey1nSUSkRniCwwl3nx/+F5jZy0RPdeabWY6755pZDlCQ2KXKplIQk16OSnYGRERqQyLfE2NmTYF67r4sfD4MuBF4FRgI3Bb+j0zYQiUhFMSkEXefm+w8iIjUhgQ/Yp0NvGxmEJ0Xn3H3t8zsY2CEmQ0C5gIDErlQ2XQKYtKQme0H3EP0WHVDIANY4e4tkpoxEZEEKU7g00nu/j3QpZz0n4HeCVuQJJyCmPR0L3AS8DzQDTgD2CmpORIRSaBEv+xOUpNedpem3H02kOHuxe7+GNA32XkSEUkUL7Eqd5K+VBOTnlaaWUNgupndAeSigFVE0kiin06S1KQTWxoxs73Dx9OJvtsLgRXAVsBxycqXiEiiqSZGQDUx6WaomTUDniV6S+9XwA1JzpOISMIVl+gaXFQTk1bcfQ+id8WsAV4ws8/M7Eoz65TUjCXB7G8m8+m0d5j68Wgmf/hmuePcdeeNzPrqfaZ9MoY9uu5Wln766Scwc8b7zJzxPqeffkJtZTklPPHsy/Q/9VyOOe08/u+62/j119/Kht161wPs3efYCqf93xPP0W/AnzjqpLOZNOWTsvT3J0/lqJPOpt+AP/HwkyNqNP+p4M57b+aLbycy7oO1ryTJysrk2ZcfZtIno3j25YfJzCz/QcMTTu7PpE9GMemTUZxwcv+y9N27dObdSa/wwbS3uOn2q2q8DLXBveqdpC8FMWnG3b929xvcvTPRU0mZwFgz2+x+O6nPoSfQbe/D2G//I9Yb1q/vIey4w7bs0vkAzj//Cu679x8AtGyZxd+vHkL3A45i/x5H8verh5CVlVnbWa+T8hcu4ukXRvLco//llacepKSkhFHvTADgy5nfsHTZ8gqn/e6HuYwaO4GRTz3Ig3fezE3/upfi4mKKi4u5+d/38cC/b+LVpx/izXfG890Pm/frjkY88zKnHD94nbQLh5zN+xMm02Ovfrw/YTIXDjl7vemysjK59IoLOLL3SRxxyIlcesUFZcHObXdey2UXX0v3Pfuy3XbbcEifA2ulLDWpxK3KnaQvBTFpyszqAe2IXuLUFL0uex1HH304Tz79AgBTPppGZlYm7du347DDevLO2IkUFhZRVLSEd8ZO5PDDeyU3s3XImuJifv31N9asKWbVL7/Stk0riouL+fd9j3DpBYMqnO7diZPp17snDRs2pGOH9mzdsQNfzPyGL2Z+w9YdO7DVljk0aNCAfr178u7EybVYorpn8gefUFi4ZJ20w484hBHDXwFgxPBX6Hvk+q8u6dW7B++N+5CioiUsWbKU98Z9yMF9DqBddhuaN2/GtKmfA/D8syPLnT7VuFuVO0lfCmLSjJkdaGb3A/OAy4CJwM7uXnE9fzTdLmbWO7SpiU1PyUez3Z1Rbw5nyuRRnD3o1PWGb9mhPfN+WlDWP39eLlt2aB+lz4tJnx+lC2S3bcOZJx9Hnz+ewcH9T6F50yb02HcvnnnxNQ4+YD/atmlV4bQFC3+mfXbbtfNq14aChYsoWLiI9u3i03+u0XKkorbtWlOQvwiAgvxFtG3Xer1x2udks2B+bll/7oI82udkk5OTzYIF+THp+bTPaVfzma5hup0koCAmrZjZT8A/gK+Aru5+uLs/5u5LKpnuL0S/CXIR8KWZ9Y8ZfGsl0w42s6lmNrWkZMUmliBxeh58LPvs25ejjj6N888/kwMP2DfZWUp5S5YuY9zEybz9/GO8O/JpVv3yKyNHvcPocRM55fg/JDt7mxXXmVm3kwRQEJNuDnD3A9z9Xnevzu2jc4C93P0YoBfwdzO7OAzb4BHA3Ye6ezd371avXtONy3UNWLAgD4CFC39m5MhR7L1313WGz1+QR8etOpT1b9kxh/kL8qL0jjHpW0bpApOnTmfLDtm0aplFg/r16d2zO/c/8hQ/zsvliBP/xGHHDeSXX36l34A/rTdtu7atyctfWNafX7CIdm3b0K5tG/IK4tPXr2XY3C0s+Jl22W0AaJfdhkULF683Tl5uPh22zCnrz+nQnrzcfHJz8+nQITsmPZu83NS/u1xcUq/KnaQvfbtpZBN+ALKeuy8P85hDFMj0M7M7qSSIqYuaNGlMs2ZNyz4f2qcnM2Z8vc44r78+mtNPPR6AfffZk6VLlpKXV8Do0RM4tM9BZGVlkpWVyaF9DmL06Am1Xoa6KCe7LZ9/OYtVv/yCuzNl6nTOOPFYJrz2DKNfHMboF4fRqNEWjBrx6HrTHnzAfowaO4HffvuNeQvy+HHeAn7/u53YbZed+HHeAuYtyGP16tWMGjuBgw/YLwmlq9tGjxrHgJOPAWDAycfw9pvvrjfO+LGT6HlIdzIzW5CZ2YKeh3Rn/NhJFOQvYtmy5ezZbXcATjipP2+VM32q8Wp0kr70nhgByDezru4+HcDdl5vZUcCjwO+Tm7Xqy85uywvPPwJA/foZPPvsK7w9ejyDzzkdgKH/e5I3R42lb99D+HrmJFauWsXZZ18CQGFhEbfc+h8mf/AGADffcheFhUXJKUgds/uuu3DowQcw4KyLyMjIYJedtueE/v0qHH/cxMnMmPUNF55zBjtstw2HH3Igfzj1XOpnZHD1JReQkZEBwFVDzufcS66huLiYY486jB2226a2ilQn3f/wP+l+wD60ap3FJzPe5V+33cu9d/2Phx6/i5NPP455Py3g3DOj7bVL1105/U8nctlfrqWoaAl3/fNBRo2LHlO/844HKCqK7iT/7dKb+M/9t9Ko8Ra8O2Yi7455L2nlSxTdJhIA071VMbOOwBp3X+++iZn1cPcqPZ5dv+GW2phq0KoFE5Odhc3C1jsclewsbBZyi77apChkUvvjq3y86ZH3giKeNKWamDRiZvewgdpTd/9LBenzNjDNZvd+GRGp+0qSnQGpExTEpJepyc6AiEht8NRrric1QEFMGnH3YcnOg4hIbVijNjGCgpi0ZGZtgSuAzkCj0nR3PyRpmRIRSSDVxAjoEet09TQwE9iW6Fes5wAfJzNDIiKJVFKNrqrMLMPMPjWz10P/tmY2xcxmm9lzZtYwoYWQTaYgJj21dvdHgNXuPsHd/wSoFkZE0oZjVe6q4WKiC8BStwN3ufsOQCFQ8Q+ESVIoiElPq8P/XDM70sz2ACr+YRsRkRST6JqY8KqJI4GHQ78RXfy9EEYZBhyToOxLgqhNTHq62cwygUuBe4AWwJDkZklEJHGKq1HDYmaDgcExSUPdfWjcaP8BLgeah/7WQJG7rwn984AtNy63UlMUxKQhd389fFwCHJzMvIiI1ISSatwlCgFLfNBSJryhvMDdPzGzXpucOak1CmLSkJk9RjkvvQttY0REUl5JYp9O6gH8wcyOIHqiswVwN5BlZvVDbUxHYH4iFyqbTm1i0tPrwBuhG0u0Qy5Pao5ERBIokT8A6e5/c/eO7t4JOAl4191PBcYBx4fRBgIjE1kG2XSqiUlD7v5ibL+ZDQfeT1J2REQSrpZ+duAK4Fkzuxn4FHikdhYrVaUgZvOwI9Au2ZkQEUmUEquZl925+3hgfPj8PbBPjSxIEkJBTBoys2WsW4uaR3RFISKSFoqTnQGpExTEpCF3b175WCIiqas6TydJ+lLD3jRkZmOrkiYikqpKsCp3kr5UE5NGzKwR0ARoY2YtoWzvbYFe0iQiaaQqTx1J+lMQk17OBf4KdAA+YW0QsxS4N1mZEhFJNN1OElAQk1bc/W7gbjO7yN3vSXZ+RERqSi09Yi11nNrEpKcSM8sq7TGzlmZ2QTIzJCKSSMVW9U7Sl4KY9HSOuxeV9rh7IXBOEvMjIpJQif4Va0lNup2UnjLMzNzdAcwsA2iY5DyJiCSMghMBBTHp6i3gOTN7KPSfG9JERNKC6zaRoCAmXV0BDAbOD/1jgP8lLzsiIomlmhgBtYlJS+5e4u4Puvvx7n488BWgp5VEJG0UV6OT9KWamDRlZnsAJwMDgB+Al5KbIxGRxNF7YgQUxKQVM9uJKHA5GVgEPAeYux+c1IyJiCSYbicJKIhJN7OAicBR7j4bwMyGJDdLIiKJpyBGQG1i0s0fgVxgnJn9z8x6g379TETSj1ejk/SlICaNuPsr7n4SsAswjuh3lNqZ2QNmdlhycycikjglVvVO0peCmDTk7ivc/Rl3PxroCHxK9Ni1iEha0NNJAmoTk/bCTw4MDV2NOqPD/jW9iM3aud0uZwtdd9S4Bxp1TXYWpApKdKNIUE2MSMpQACOyViJ/O8nMGpnZR2b2mZnNMLMbQvq2ZjbFzGab2XNmpp9vqWN0VBQRkZST4Ia9vwKHuHsXoCvQ18z2A24H7nL3HYBCYFBCCyGbTEGMiIiknETWxHhkeehtEDoHDgFeCOnDgGMSlX9JDAUxIiKSctaYV7kzs8FmNjWmGxw/PzPLMLPpQAHR7819BxS5+5owyjxgy9oroVSFGvaKiEjKqU6zXnev9OEGdy8GuppZFvAy0asqpI5TECMiIimnpt7Y6+5FZjYO2B/IMrP6oTamIzC/hhYrG0m3k0REJOWU4FXuKmNmbUMNDGbWGDgUmEn00tDjw2gDgZE1VBzZSKqJERGRlJPgt8TkAMPMLIPo4n6Eu79uZl8Bz5rZzUQvDX0ksYuVTaUgRkREUk4ibye5++fAHuWkfw/sk8BFSYIpiBERkZRTrDf2CgpiREQkBdVUw15JLQpiREQk5bhqYgQFMSIikoJUEyOgIEZERFKQfsVaQEGMiIikIIUwAgpiREQkBa1RGCMoiBERkRSkhr0CCmJERCQFqWGvgIIYERFJQaqJEVAQIyIiKUg1MQIKYkREJAUVu2piREGMiIikIL0nRkBBjIiIpCC1iRFQECMiIilIbWIEFMSIiEgK0u0kAQUxIiKSgnQ7SUBBjIiIpCA9nSQA9ZKdARERkeoqwavcVcbMtjKzcWb2lZnNMLOLQ3orMxtjZt+G/y1rvGBSLQpiREQk5ZRUo6uCNcCl7t4Z2A/4s5l1Bq4Exrr7jsDY0C91iIIYERFJOV6Nv0rn5Z7r7tPC52XATGBLoD8wLIw2DDimhoojG0ltYkREJOXU1NNJZtYJ2AOYAmS7e24YlAdk18hCZaMpiJG00DKnNWffeRGZbTJxhwnDx/DOY2/S/68DOOik3ixbvBSAF+94hi/Gf7re9Lv17Mop156FZdRj4nNjefOBVwBo07Ed5907hKZZzZj75ff8b8g9FK9eU6tlqyta5rRm4J1/pkWbLNyd94e/w7jHRgHQa2Bfep5xOCXFJXz57jRevu3p9abv3LMLA8I6nvTcWEY/MBKA1h3bMujev9I0qzk/fvk9jw+5h+LVxbVatrqi3hYNOPCVa6nXsD5WP4MFr09h1j9fpE2Pzux23anUa1ifos9/4NMhQ/Hi9W+UbDXgQHb+67EAfP2fl/lpxEQAMnfflj3vPpeMRg3JHzudL655olbLVRO8Gg17zWwwMDgmaai7Dy1nvGbAi8Bf3X2pmcUuz81MrYnrGAUxkhZK1hTz3M3D+HHGDzRq2ohrX7uDryZ+DsDoR97g7f+9WuG0Vq8ep914Nv8+7UYW5y3m2ldvY/qYqSyYPY8TrjyN0Y+8zkevTeL0WwZz4ImHMP6p0bVVrDqleE0xL978JD/N+IEtmjbib6/dxsyJn9OibRZdDu3GLf3+jzW/raF56xbrTWv1jJNuHMR/T7uZwryfufLVf/D5mKnkzZ7PsVeexruPvMHU1z7g5FvOoceJh/DeU2OSUMLkK/l1Ne8fdzPFK3/F6mdw4KvXUTDuc/b87/lMOuEWVnyfxy6XH8/WAw5i7vDx60zbIKspu1x6HOMPvxoceo2+hby3p7F6yQq63v4npl/6MIXTZrP/M5fT7pAuFLz7WXIKmSDF1aiJCQHLekFLLDNrQBTAPO3uL4XkfDPLcfdcM8sBCjY2v1Iz1CZG0sKShUX8OOMHAH5Z8Qu5380nq32rKk27XdcdKJibx8KfCihevYYpr02i62F7A7BL992Y+uaHAHzw4nj2PGyfmilACli6sIifwjr+dcUv5IV1fNCph/H2AyNZ81tUQ7Xs56XrTdup6w4snJvHop8KKF5dzNTXPqBLWMc7d9+VaW9OBmDyi+PL0jdXxSt/BaBegwzq1c/Ai0vw1WtY8X0eAAsnfEHOUetvh+167U7BhC9YXbSC1UtWUDDhC9odvDtbtMuifrPGFE6bDcCPIyaS07db7RWohiT46SQDHgFmuvudMYNeBQaGzwOBkQkviGwSBTGSdlp3bMvWnTvx/fRvAeg9sC83jPo3Z91xAU1aNF1v/KzsVixesKisvzD3Z1pmt6JZy+asXLqCklBtvzj3Z7KyqxYYpbtWHduyVedtmTN9Nu22y2GHfXbh8lduYchz17PN7tuvN35WdisKF/xc1l8Y1mXTls1ZuXRl2Touyl2sdVzPOPidW+n35YMUvPcFhZ9+h9WvR1aXbQHocNS+NO6w/jpqnNOKVTHr+JfcxTTOaUXjnJasyl0cl576Twq7e5W7KugBnA4cYmbTQ3cEcBtwqJl9C/QJ/VKH6HaSAGBm+xDd9v04PFrYF5jl7m8mOWvVskWTRvz5gcsYfuPj/LJ8FeOeeptX//sCuHPspSdx4jUDeezy+5OdzZS2RZMtOPeBS3k+rOOMjHo0yWzGHcdczTZdtufs+4bw9wMvTHY2U1eJM67PVTRo0YR9HhtC81068vG597LbDaeTsUUDCsZ/DuW0h9ncJLJhr7u/D1gFg3snbEGScKqJEczsOuC/wANm9g/gXqApcKWZXV3JtIPNbKqZTf162fe1kNuKZdTP4M8PXsbkVyYy7e0pACxdtAQvKcHdmfDsO2zbZYf1pivKX0yrDm3K+lvmtKYwfzHLC5fRpEVT6mVEu0mrnNYU5S9eb/rNSb36GQx+8FI+emUi09/+CIDCvMVln+d+9h1eUkKzVs3Xma4ofzEtO7Qu628Z1uWKwmU0adGkbB1n5bTa7NdxqdVLV7Jo0ldkH9yFwk++5f1jbmRCv7+zaPIslodbS7FW5S6mccw6bpTTilW5i1mVW0jjnFZx6YW1UoaalMhHrCV1KYgRgOOJqlMPAv4MHOPuNwGHAyduaEJ3H+ru3dy9287Nt6v5nG7AWbdfQO7seYx+5PWytMy2WWWf9zx8X+Z/89N60/3w2WyyO+XQpmM7MhrUZ9+jezB9zMcAzPpwBt2O2B+A7sf14tPRH9dwKeq2028/j7zZ8xn7yBtlaZ+N/pid9tsVgHbb5pDRoD7LFy9bZ7q5n31Hu045tO7YlowGGXQ7ujufj5kKwNcfzmDPI/YDYL/jevHZ6Km1VJq6p2Hr5jRo0QSAeo0a0Pag37Ns9gIatokaS9drWJ+dLjyaH4a9s960BeM/p12v39MgsykNMpvSrtfvKRj/Ob8WFLFm+Spa7hkF8FsPOJC8tz+pvULVkGL3KneSvnQ7SQDWuHsxsNLMvnP3pQDuvsrMUqLeesduu9D9uJ78NHMu17/5TyB6nHrfPxzA1p074Q6L5hXwxFUPAZDVriVn3n4+/znrVkqKS3jq2oe55IlrqJdRj/dHvMuCb+cB8MJtT3LuPUM49tKT+HHGHCaOGJu0Mibb9t12Zr/jejJv5lyuevMOAEbeMZwPRrzL6XdcwN/f/hdrVq/hiUvvAyCzXUtOu/1c7jvrNkqKS3j22ke56ImrqZdRjw9GjCM3rONXbnuaQff8laMvPYmfZvzAByPeTVoZk61Ruyz2/O/5WEY9rJ4x/9XJ5I/5lF2vPYX2ffaAesacYe+waNJXAGR12ZZOZ/Rh+qX/Y3XRCr6+62V6vnUTAF/f+TKri1YA8NmVj7Ln3edFj1i/+xn5Y6cnrYyJol+xFgCrzrP2kp7MbApwsLuvNLN67l4S0jOBce6+Z1Xm86dOx2tjqkFbqOK0Vhz+i67tasMxec9U1AalSvbf8uAqH28+nD9uk5YldZf2VgE4yN1/BSgNYIIGrH28UESkztAFuICCGAFKA5hy0hcBi8obJiKSTLqdJKAgRkREUpCeOhJQECMiIimo2FPimQOpYQpiREQk5ahNjICCGBERSUFqEyOgIEZERFKQ2sQIKIgREZEUVKLbSYKCGBERSUGqiRFQECMiIilITycJKIgREZEUpNtJAgpiREQkBel2koCCGBERSUGqiRFQECMiIilINTECCmJERCQFFXtxsrMgdUC9ZGdARESkuty9yl1lzOxRMyswsy9j0lqZ2Rgz+zb8b1mjBZKNoiBGRERSTgle5a4KHgf6xqVdCYx19x2BsaFf6hgFMSIiknISWRPj7u8Bi+OS+wPDwudhwDGJLYEkgtrEiIhIyqnO00lmNhgYHJM01N2HVjJZtrvnhs95QHb1cii1QUGMiIiknOo8nRQClsqClg1N72amx6HqIAUxIiKScmrhZwfyzSzH3XPNLAcoqOkFSvWpTYyIiKScRLaJqcCrwMDweSAwMiEZl4RSTYyIiKScRL6x18yGA72ANmY2D7gOuA0YYWaDgLnAgIQtUBJGQYyIiKScTahhKW9eJ1cwqHfCFiI1QkGMiIiknCq+/0XSnIIYERFJOYmsiZHUpSBGRERSTi08nSQpQEGMiIiknEQ27JXUpSBGRERSjm4nCSiIERGRFFSdN/ZK+lIQIyIiKUc1MQIKYkRSxq+UsIVesi0CqE2MREzRrGyuzGxwFX7JVjaR1nPN0zqWzZUu62RzNjjZGdhMaD3XPK1j2SwpiBEREZGUpCBGREREUpKCGNmcqQ1B7dB6rnlax7JZUsNeERERSUmqiREREZGUpCBGREREUpKCGNnsmNmjZlZgZl8mOy/pysy2MrNxZvaVmc0ws4uTnad0ZGaNzOwjM/ssrOcbkp0nkdqkNjGy2TGzg4DlwBPuvluy85OOzCwHyHH3aWbWHPgEOMbdv0py1tKKmRnQ1N2Xm1kD4H3gYnefnOSsidQK1cTIZsfd3wMWJzsf6czdc919Wvi8DJgJbJncXKUfjywPvQ1CpytT2WwoiBGRGmVmnYA9gCnJzUl6MrMMM5sOFABj3F3rWTYbCmJEpMaYWTPgReCv7r402flJR+5e7O5dgY7APmamW6Sy2VAQIyI1IrTReBF42t1fSnZ+0p27FwHjgL7JzotIbVEQIyIJFxqcPgLMdPc7k52fdGVmbc0sK3xuDBwKzEpurkRqj4IY2eyY2XDgQ2BnM5tnZoOSnac01AM4HTjEzKaH7ohkZyoN5QDjzOxz4GOiNjGvJzlPIrVGj1iLiIhISlJNjIiIiKQkBTEiIiKSkhTEiIiISEpSECMiIiIpSUGMiIiIpCQFMSIpzMyKw+PLX5rZ82bWZBPm9biZHR8+P2xmnTcwbi8z674Ry5hjZm3i0h4zs3Pj0o4xs1FVyauIbL4UxIiktlXu3jX8GvdvwHmxA82s/sbM1N3PruQXp3sB1Q5iKjAcOCku7aSQLiJSIQUxIuljIrBDqCWZaGavAl+FHwj8p5l9bGafl9Z6WOReM/vazN4B2pXOyMzGm1m38LmvmU0zs8/MbGz4QcfzgCGhFujA8ObYF8MyPjazHmHa1mY22sxmmNnDgJWT77HALmaWE6ZpCvQBXjGza8P8vjSzoeFNwOuIrd0xs25mNr50Pmb2qJl9ZGafmln/kL5rSJse1seOCVj3IpIECmJE0kCocekHfBGS9gQudvedgEHAEnffG9gbOMfMtgWOBXYGOgNnUE7Nipm1Bf4HHOfuXYAT3H0O8CBwV6gFmgjcHfr3Bo4DHg6zuA543913BV4Gto5fhrsXE/3G0oCQdDQwPvxg5L3uvneoaWoMHFWN1XI18K677wMcDPwzBEjnAXeHH03sBsyrxjxFpA7ZqKpmEakzGpvZ9PB5ItHvFXUHPnL3H0L6YcDuMW1IMoEdgYOA4SGIWGBm75Yz//2A90rn5e6LK8hHH6BzTEVJi/AL1gcBfwzTvmFmhRVMPxz4F1EwdBLwZEg/2MwuB5oArYAZwGsVzCPeYcAfzOyy0N+IKIj6ELjazDoCI9g/YgAAAblJREFUL7n7t1Wcn4jUMQpiRFLbqlCjUCYEEitik4CL3P3tuPES+VtG9YD93P2XcvJSFR8AOWbWhSgIO8nMGgH3A93c/Sczu54oEIm3hrW1yrHDjagG6eu48Wea2RTgSOBNMzvX3csL4ESkjtPtJJH09zZwvpk1ADCzncJtlfeAE0ObmRyiWy7xJgMHhdtPmFmrkL4MaB4z3mjgotIeMysNrN4DTglp/YCW5WXQox9xew4YBowKwVBpQLIo1OpU9DTSHGCv8Pm4uHJfVNqOxsz2CP+3A7539/8CI4HdK5iviNRx/9/eHaMoEENxGP9e4W0EGy9i4xG29A6DJ7BcsLKxcw+yi6CMNnbbbuMFJBaJqIXbP/l+zUDCMEn3J+8NMcRI728JHIFtRPTAJ/UU9gs4tbkVtczypJTyB3wAm4jYUYMG1JLO5NbYC8yAcWuUPXL/S6qjhqADtaz0+88618CoPSmlnKn9OD01kHy/eK8DFhHxA1wexufAANi378/b+BToWxlu2PYuKSFvsZYkSSl5EiNJklIyxEiSpJQMMZIkKSVDjCRJSskQI0mSUjLESJKklAwxkiQppSuQO5Kc4ufyMwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Classification Report"
      ],
      "metadata": {
        "id": "b8TtiqbHUt_G"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test,predictions))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tyUs_pB4UwQE",
        "outputId": "9c175e71-4403-4861-877a-35cf3076c589"
      },
      "execution_count": 226,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.71      0.76      0.73        98\n",
            "           1       0.69      0.85      0.76        99\n",
            "           2       0.63      0.43      0.51        90\n",
            "\n",
            "    accuracy                           0.69       287\n",
            "   macro avg       0.68      0.68      0.67       287\n",
            "weighted avg       0.68      0.69      0.67       287\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* **note** : class 0 corresponds to class 1, class 1 corresponds toclass 2 and class 2 corresponds to class 3"
      ],
      "metadata": {
        "id": "1kiwqPhmU9fY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import roc_curve, auc,roc_auc_score\n",
        "y_prob = rf_pipeline.predict_proba(X_test)\n",
        "\n",
        "macro_roc_auc_ovo = roc_auc_score(y_test, y_prob, multi_class=\"ovo\", average=\"macro\")\n",
        "weighted_roc_auc_ovo = roc_auc_score(\n",
        "    y_test, y_prob, multi_class=\"ovo\", average=\"weighted\"\n",
        ")\n",
        "macro_roc_auc_ovr = roc_auc_score(y_test, y_prob, multi_class=\"ovr\", average=\"macro\")\n",
        "weighted_roc_auc_ovr = roc_auc_score(\n",
        "    y_test, y_prob, multi_class=\"ovr\", average=\"weighted\"\n",
        ")\n",
        "print(\n",
        "    \"One-vs-One ROC AUC scores:\\n{:.6f} (macro),\\n{:.6f} \"\n",
        "    \"(weighted by prevalence)\".format(macro_roc_auc_ovo, weighted_roc_auc_ovo)\n",
        ")\n",
        "print(\n",
        "    \"One-vs-Rest ROC AUC scores:\\n{:.6f} (macro),\\n{:.6f} \"\n",
        "    \"(weighted by prevalence)\".format(macro_roc_auc_ovr, weighted_roc_auc_ovr)\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SS-_PyyPNQbQ",
        "outputId": "6218b6ec-04ab-484d-92a8-45cda38a94a4"
      },
      "execution_count": 219,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "One-vs-One ROC AUC scores:\n",
            "0.868248 (macro),\n",
            "0.869556 (weighted by prevalence)\n",
            "One-vs-Rest ROC AUC scores:\n",
            "0.869821 (macro),\n",
            "0.871989 (weighted by prevalence)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "QUGZd-KBUgZK"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}