8: EllipticEnvelope_Anomaly_Detection.ipynb

{
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  "metadata": {
    "colab": {
      "name": "8: EllipticEnvelope_Anomaly_Detection.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMvsNoMXy8NM07WPikln+p+",
      "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/gulabpatel/Anomaly_Detection_Python/blob/main/8%3A%20EllipticEnvelope_Anomaly_Detection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BJtLYjtA9wjt"
      },
      "source": [
        "**Import libraries**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1s2YW3ii5e7j"
      },
      "source": [
        "# import libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.covariance import EllipticEnvelope\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s5mCbghe95Vk"
      },
      "source": [
        "**Get the training data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "pIjUTk9-922r",
        "outputId": "8c73ac63-c756-48e8-cec8-210020d2c84c"
      },
      "source": [
        "# create a dataframe\n",
        "df = pd.DataFrame(np.array([[0,1], [1,1], [1,2], [2,2], [5,6]]), columns = [\"x\", \"y\"], index = [0,1,2,3,4])\n",
        "# show data in a scatterplot\n",
        "plt.scatter(df[\"x\"], df[\"y\"], color = \"r\")\n",
        "plt.grid()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1k7Fgdnw-AHQ"
      },
      "source": [
        "# convert dataframe to arrays\n",
        "data = df[['x', 'y']].values"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_BLNykUh-Hjf"
      },
      "source": [
        "**Modeling**\n",
        "\n",
        "\n",
        "The most important parameter in defining the model is to set the “contamination” parameter, which basically defines the proportion of values that will be identified as outliers. The parameter value ranges between 0 and 0.5"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LqKgduqP-Dlz",
        "outputId": "6468ca5d-356d-43a4-8075-5f09551526be"
      },
      "source": [
        "# instantiate model\n",
        "model1 = EllipticEnvelope(contamination = 0.1) \n",
        "# fit model\n",
        "model1.fit(data)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "EllipticEnvelope(assume_centered=False, contamination=0.1, random_state=None,\n",
              "                 store_precision=True, support_fraction=None)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eqsQpJo4-UNG"
      },
      "source": [
        "**Prediction**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GAPuOvqa-WpN",
        "outputId": "900e21e0-ea5f-4a6d-af28-31170a31c1e4"
      },
      "source": [
        "# new data for prediction (data needs to be in arrays)\n",
        "new_data = np.array([[10,10], [1,1], [1,1], [1,1]])\n",
        "# predict on new data \n",
        "pred1 = model1.predict(new_data)\n",
        "print(pred1)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[-1  1  1  1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GaKF7P2Y_7Ne"
      },
      "source": [
        "**Example 2:**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "EfvbQ9jdAsQK",
        "outputId": "bcef72dc-920c-4e3c-aa7c-4643eeba9f28"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "from sklearn.covariance import EllipticEnvelope\n",
        "\n",
        "df = pd.read_csv('https://raw.githubusercontent.com/gulabpatel/Anomaly_Detection_Python/master/dataset/Salary.txt')\n",
        "df"
      ],
      "execution_count": 9,
      "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>ID</th>\n",
              "      <th>Age</th>\n",
              "      <th>Salary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1001</td>\n",
              "      <td>33</td>\n",
              "      <td>81200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1002</td>\n",
              "      <td>25</td>\n",
              "      <td>63537</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1003</td>\n",
              "      <td>24</td>\n",
              "      <td>51963</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1004</td>\n",
              "      <td>23</td>\n",
              "      <td>40199</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1005</td>\n",
              "      <td>34</td>\n",
              "      <td>39347</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>594</th>\n",
              "      <td>1602</td>\n",
              "      <td>28</td>\n",
              "      <td>874</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>595</th>\n",
              "      <td>1603</td>\n",
              "      <td>29</td>\n",
              "      <td>845</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>596</th>\n",
              "      <td>1604</td>\n",
              "      <td>33</td>\n",
              "      <td>616</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>597</th>\n",
              "      <td>1605</td>\n",
              "      <td>32</td>\n",
              "      <td>410</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>598</th>\n",
              "      <td>1606</td>\n",
              "      <td>29</td>\n",
              "      <td>350</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>599 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       ID  Age  Salary\n",
              "0    1001   33   81200\n",
              "1    1002   25   63537\n",
              "2    1003   24   51963\n",
              "3    1004   23   40199\n",
              "4    1005   34   39347\n",
              "..    ...  ...     ...\n",
              "594  1602   28     874\n",
              "595  1603   29     845\n",
              "596  1604   33     616\n",
              "597  1605   32     410\n",
              "598  1606   29     350\n",
              "\n",
              "[599 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 844
        },
        "id": "MSkkFudXAA1E",
        "outputId": "8c83294f-97b1-4dbb-adc9-082f845b1cbc"
      },
      "source": [
        "features = df.iloc[:,:].values\n",
        "\n",
        "outliers = EllipticEnvelope(contamination=0.1)\n",
        "\n",
        "outliers.fit(features)\n",
        "prediction = outliers.predict(features)\n",
        "\n",
        "print(prediction)\n",
        "\n",
        "df['outliers'] = prediction\n",
        "df"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[-1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1\n",
            " -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1\n",
            " -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1  1  1  1  1  1  1  1  1  1  1  1  1\n",
            "  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1\n",
            "  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1\n",
            "  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1\n",
            "  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1\n",
            "  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1\n",
            "  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1\n",
            "  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1\n",
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            "  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1\n",
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          "name": "stdout"
        },
        {
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          "data": {
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              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>Age</th>\n",
              "      <th>Salary</th>\n",
              "      <th>outliers</th>\n",
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              "<p>599 rows × 4 columns</p>\n",
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            ],
            "text/plain": [
              "       ID  Age  Salary  outliers\n",
              "0    1001   33   81200        -1\n",
              "1    1002   25   63537        -1\n",
              "2    1003   24   51963        -1\n",
              "3    1004   23   40199        -1\n",
              "4    1005   34   39347        -1\n",
              "..    ...  ...     ...       ...\n",
              "594  1602   28     874         1\n",
              "595  1603   29     845         1\n",
              "596  1604   33     616         1\n",
              "597  1605   32     410         1\n",
              "598  1606   29     350         1\n",
              "\n",
              "[599 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    }
  ]
}