Outliers_in_Numeric_Tabular_Data.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "#Tabular Data - Numeric - Scaled"
      ],
      "metadata": {
        "id": "9Ept1Fe6ek55"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "nIuRAya3c6F6"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.random.seed(42)\n",
        "X_inliers = 0.3 * np.random.randn(100, 2)\n",
        "X_inliers = np.r_[X_inliers + 2, X_inliers - 2]\n",
        "X_outliers = np.random.uniform(low=-4, high=4, size=(20, 2))\n",
        "X = np.r_[X_inliers, X_outliers]\n",
        "n_outliers = len(X_outliers)\n",
        "ground_truth = np.ones(len(X), dtype=int)\n",
        "ground_truth[-n_outliers:] = -1\n",
        "df = pd.DataFrame(X, columns=['Feature_1', 'Feature_2'])\n",
        "df['Ground_Truth'] = ground_truth\n",
        "print(df.head())\n",
        "print(df.tail())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WdIeSPLOdslJ",
        "outputId": "bb9d3b7b-77dc-4ab9-e958-3dea62640cd5"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Feature_1  Feature_2  Ground_Truth\n",
            "0   2.149014   1.958521             1\n",
            "1   2.194307   2.456909             1\n",
            "2   1.929754   1.929759             1\n",
            "3   2.473764   2.230230             1\n",
            "4   1.859158   2.162768             1\n",
            "     Feature_1  Feature_2  Ground_Truth\n",
            "215  -2.983516   0.177946            -1\n",
            "216   2.159948  -2.273432            -1\n",
            "217   0.983124  -3.317220            -1\n",
            "218  -3.586546   0.250837            -1\n",
            "219   0.325081   1.099439            -1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import LocalOutlierFactor\n",
        "\n",
        "clf = LocalOutlierFactor(n_neighbors=5, contamination=0.1)\n",
        "y_pred = clf.fit_predict(X)\n",
        "n_errors = (y_pred != ground_truth).sum()\n",
        "X_scores = clf.negative_outlier_factor_"
      ],
      "metadata": {
        "id": "ed-VOwEvdvG5"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.legend_handler import HandlerPathCollection\n",
        "\n",
        "\n",
        "def update_legend_marker_size(handle, orig):\n",
        "    \"Customize size of the legend marker\"\n",
        "    handle.update_from(orig)\n",
        "    handle.set_sizes([20])\n",
        "\n",
        "\n",
        "plt.scatter(X[:, 0], X[:, 1], color=\"k\", s=3.0, label=\"Data points\")\n",
        "radius = (X_scores.max() - X_scores) / (X_scores.max() - X_scores.min())\n",
        "scatter = plt.scatter(\n",
        "    X[:, 0],\n",
        "    X[:, 1],\n",
        "    s=1000 * radius,\n",
        "    edgecolors=\"r\",\n",
        "    facecolors=\"none\",\n",
        "    label=\"Outlier scores\",\n",
        ")\n",
        "plt.axis(\"tight\")\n",
        "plt.xlim((-5, 5))\n",
        "plt.ylim((-5, 5))\n",
        "plt.legend(\n",
        "    handler_map={scatter: HandlerPathCollection(update_func=update_legend_marker_size)}\n",
        ")\n",
        "plt.title(\"Local Outlier Factor (LOF)\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "SgAR4mFLdyFm",
        "outputId": "a71d186d-566b-467f-8162-0ebb996b0098"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "num_detected_outliers = np.sum(y_pred == -1)\n",
        "percentage_outliers = (num_detected_outliers / len(X)) * 100\n",
        "print(f\"Percentage of outliers detected in the dataset: {percentage_outliers:.2f}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s11dSt2xd_BA",
        "outputId": "9e315cc0-0014-47a8-b3bf-dce2f2e5606d"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Percentage of outliers detected in the dataset: 10.00%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inliers = X[y_pred == 1]\n",
        "df_inliers = pd.DataFrame(inliers, columns=['Feature_1', 'Feature_2'])"
      ],
      "metadata": {
        "id": "uHHVyRkdeCOp"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clf_inliers = LocalOutlierFactor(n_neighbors=5)\n",
        "y_pred_inliers = clf_inliers.fit_predict(inliers)\n",
        "X_scores_inliers = clf_inliers.negative_outlier_factor_"
      ],
      "metadata": {
        "id": "V3HyyI94eEl_"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def update_legend_marker_size(handle, orig):\n",
        "    \"Customize size of the legend marker\"\n",
        "    handle.update_from(orig)\n",
        "    handle.set_sizes([20])\n",
        "\n",
        "plt.scatter(inliers[:, 0], inliers[:, 1], color=\"k\", s=3.0, label=\"Data points\")\n",
        "radius_inliers = (X_scores_inliers.max() - X_scores_inliers) / (X_scores_inliers.max() - X_scores_inliers.min())\n",
        "scatter_inliers = plt.scatter(\n",
        "    inliers[:, 0],\n",
        "    inliers[:, 1],\n",
        "    s=1000 * radius_inliers,\n",
        "    edgecolors=\"r\",\n",
        "    facecolors=\"none\",\n",
        "    label=\"Outlier scores\",\n",
        ")\n",
        "plt.axis(\"tight\")\n",
        "plt.xlim((-5, 5))\n",
        "plt.ylim((-5, 5))\n",
        "plt.legend(\n",
        "    handler_map={scatter_inliers: HandlerPathCollection(update_func=update_legend_marker_size)}\n",
        ")\n",
        "plt.title(\"Local Outlier Factor (LOF) - Inliers Only\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "WGddy050eGzL",
        "outputId": "8f579924-f546-4257-e662-ac8ac1f80908"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "num_detected_outliers_filtered = np.sum(y_pred_inliers == -1)\n",
        "percentage_outliers_filtered = (num_detected_outliers_filtered / len(inliers)) * 100\n",
        "print(f\"Percentage of outliers in the new filtered dataset: {percentage_outliers_filtered:.2f}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g_JmHAPAeJ5U",
        "outputId": "12bb2fd4-cc00-4e2f-b067-8d85c305f64a"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Percentage of outliers in the new filtered dataset: 1.52%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "comparison_table = pd.DataFrame({\n",
        "    'Metric': ['Number of data points', 'Number of outliers detected', 'Percentage of outliers detected'],\n",
        "    'Initial Dataset': [len(X), num_detected_outliers, percentage_outliers],\n",
        "    'Filtered Dataset': [len(inliers), num_detected_outliers_filtered, percentage_outliers_filtered]\n",
        "})\n",
        "\n",
        "print(\"Comparison of Outlier Detection Metrics\")\n",
        "print(comparison_table)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2hPFPX27eNSp",
        "outputId": "4dfb8a04-d3be-4e12-fadd-531d07bc02c4"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Comparison of Outlier Detection Metrics\n",
            "                            Metric  Initial Dataset  Filtered Dataset\n",
            "0            Number of data points            220.0        198.000000\n",
            "1      Number of outliers detected             22.0          3.000000\n",
            "2  Percentage of outliers detected             10.0          1.515152\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Numeric Data without contamination - LOF - non scaled"
      ],
      "metadata": {
        "id": "4Z1J83n1ebDb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "np.random.seed(42)\n",
        "cluster1 = np.random.normal(loc=0, scale=1, size=(50, 10))\n",
        "cluster2 = np.random.normal(loc=5, scale=1, size=(50, 10))\n",
        "data = np.vstack((cluster1, cluster2))"
      ],
      "metadata": {
        "id": "4vPaqDiMfWS3"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame(data, columns=[f'feature_{i}' for i in (range(10))])\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "p243GPQhfghY",
        "outputId": "e3b00730-a513-4548-db09-5669ecdadbbd"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    feature_0  feature_1  feature_2  feature_3  feature_4  feature_5  \\\n",
              "0    0.496714  -0.138264   0.647689   1.523030  -0.234153  -0.234137   \n",
              "1   -0.463418  -0.465730   0.241962  -1.913280  -1.724918  -0.562288   \n",
              "2    1.465649  -0.225776   0.067528  -1.424748  -0.544383   0.110923   \n",
              "3   -0.601707   1.852278  -0.013497  -1.057711   0.822545  -1.220844   \n",
              "4    0.738467   0.171368  -0.115648  -0.301104  -1.478522  -0.719844   \n",
              "..        ...        ...        ...        ...        ...        ...   \n",
              "95   4.758764   5.352055   3.748461   6.443765   4.917849   6.117296   \n",
              "96   5.642723   6.329153   5.196521   5.709004   4.910264   6.440117   \n",
              "97   5.128104   4.318948   5.840644   4.347376   4.553817   3.110459   \n",
              "98   5.785800   5.425458   4.033024   4.952289   4.996397   3.841635   \n",
              "99   5.208383   2.958265   4.752823   4.318016   3.998380   4.718900   \n",
              "\n",
              "    feature_6  feature_7  feature_8  feature_9  \n",
              "0    1.579213   0.767435  -0.469474   0.542560  \n",
              "1   -1.012831   0.314247  -0.908024  -1.412304  \n",
              "2   -1.150994   0.375698  -0.600639  -0.291694  \n",
              "3    0.208864  -1.959670  -1.328186   0.196861  \n",
              "4   -0.460639   1.057122   0.343618  -1.763040  \n",
              "..        ...        ...        ...        ...  \n",
              "95   5.342725   5.456753   5.569767   5.447709  \n",
              "96   4.323608   6.800940   4.959842   3.569225  \n",
              "97   4.547694   2.576121   3.416097   5.760415  \n",
              "98   6.503398   5.877362   4.779036   5.026886  \n",
              "99   6.797687   5.640843   4.428821   5.572583  \n",
              "\n",
              "[100 rows x 10 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4810e645-2167-4f03-8b89-669daec2a199\" class=\"colab-df-container\">\n",
              "    <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>feature_0</th>\n",
              "      <th>feature_1</th>\n",
              "      <th>feature_2</th>\n",
              "      <th>feature_3</th>\n",
              "      <th>feature_4</th>\n",
              "      <th>feature_5</th>\n",
              "      <th>feature_6</th>\n",
              "      <th>feature_7</th>\n",
              "      <th>feature_8</th>\n",
              "      <th>feature_9</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.496714</td>\n",
              "      <td>-0.138264</td>\n",
              "      <td>0.647689</td>\n",
              "      <td>1.523030</td>\n",
              "      <td>-0.234153</td>\n",
              "      <td>-0.234137</td>\n",
              "      <td>1.579213</td>\n",
              "      <td>0.767435</td>\n",
              "      <td>-0.469474</td>\n",
              "      <td>0.542560</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.463418</td>\n",
              "      <td>-0.465730</td>\n",
              "      <td>0.241962</td>\n",
              "      <td>-1.913280</td>\n",
              "      <td>-1.724918</td>\n",
              "      <td>-0.562288</td>\n",
              "      <td>-1.012831</td>\n",
              "      <td>0.314247</td>\n",
              "      <td>-0.908024</td>\n",
              "      <td>-1.412304</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.465649</td>\n",
              "      <td>-0.225776</td>\n",
              "      <td>0.067528</td>\n",
              "      <td>-1.424748</td>\n",
              "      <td>-0.544383</td>\n",
              "      <td>0.110923</td>\n",
              "      <td>-1.150994</td>\n",
              "      <td>0.375698</td>\n",
              "      <td>-0.600639</td>\n",
              "      <td>-0.291694</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.601707</td>\n",
              "      <td>1.852278</td>\n",
              "      <td>-0.013497</td>\n",
              "      <td>-1.057711</td>\n",
              "      <td>0.822545</td>\n",
              "      <td>-1.220844</td>\n",
              "      <td>0.208864</td>\n",
              "      <td>-1.959670</td>\n",
              "      <td>-1.328186</td>\n",
              "      <td>0.196861</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.738467</td>\n",
              "      <td>0.171368</td>\n",
              "      <td>-0.115648</td>\n",
              "      <td>-0.301104</td>\n",
              "      <td>-1.478522</td>\n",
              "      <td>-0.719844</td>\n",
              "      <td>-0.460639</td>\n",
              "      <td>1.057122</td>\n",
              "      <td>0.343618</td>\n",
              "      <td>-1.763040</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>4.758764</td>\n",
              "      <td>5.352055</td>\n",
              "      <td>3.748461</td>\n",
              "      <td>6.443765</td>\n",
              "      <td>4.917849</td>\n",
              "      <td>6.117296</td>\n",
              "      <td>5.342725</td>\n",
              "      <td>5.456753</td>\n",
              "      <td>5.569767</td>\n",
              "      <td>5.447709</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>5.642723</td>\n",
              "      <td>6.329153</td>\n",
              "      <td>5.196521</td>\n",
              "      <td>5.709004</td>\n",
              "      <td>4.910264</td>\n",
              "      <td>6.440117</td>\n",
              "      <td>4.323608</td>\n",
              "      <td>6.800940</td>\n",
              "      <td>4.959842</td>\n",
              "      <td>3.569225</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>5.128104</td>\n",
              "      <td>4.318948</td>\n",
              "      <td>5.840644</td>\n",
              "      <td>4.347376</td>\n",
              "      <td>4.553817</td>\n",
              "      <td>3.110459</td>\n",
              "      <td>4.547694</td>\n",
              "      <td>2.576121</td>\n",
              "      <td>3.416097</td>\n",
              "      <td>5.760415</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>5.785800</td>\n",
              "      <td>5.425458</td>\n",
              "      <td>4.033024</td>\n",
              "      <td>4.952289</td>\n",
              "      <td>4.996397</td>\n",
              "      <td>3.841635</td>\n",
              "      <td>6.503398</td>\n",
              "      <td>5.877362</td>\n",
              "      <td>4.779036</td>\n",
              "      <td>5.026886</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>5.208383</td>\n",
              "      <td>2.958265</td>\n",
              "      <td>4.752823</td>\n",
              "      <td>4.318016</td>\n",
              "      <td>3.998380</td>\n",
              "      <td>4.718900</td>\n",
              "      <td>6.797687</td>\n",
              "      <td>5.640843</td>\n",
              "      <td>4.428821</td>\n",
              "      <td>5.572583</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 10 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "    .colab-df-convert:hover {\n",
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              "    .colab-df-buttons div {\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        const dataTable =\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
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              "    border: 2px solid var(--fill-color);\n",
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              "    border-bottom-color: var(--fill-color);\n",
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              "      spin 1s steps(1) infinite;\n",
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              "    0% {\n",
              "      border-color: transparent;\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      .colab-df-generate:hover {\n",
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              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
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              "\n",
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              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 100,\n  \"fields\": [\n    {\n      \"column\": \"feature_0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.7358157202382816,\n        \"min\": -1.9187712152990415,\n        \"max\": 7.526932425873621,\n        \"num_unique_values\": 100,\n        \"samples\": [\n          3.6555494889657227,\n          5.638592458777374,\n          4.47727697948096\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"feature_1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.64178220873927,\n        \"min\": -2.301921164735585,\n        \"max\": 7.075400798645439,\n        \"num_unique_values\": 100,\n        \"samples\": [\n          4.081348053515802,\n          3.33847993773104,\n          6.0490092258368895\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"feature_2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.796498495104352,\n        \"min\": -3.2412673400690726,\n        \"max\": 7.560084538268795,\n        \"num_unique_values\": 100,\n        \"samples\": [\n          3.9958592332479324,\n          4.9339202013526835,\n          4.29565630945724\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"feature_3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.700253320166504,\n        \"min\": -1.913280244657798,\n        \"max\": 7.270692857804396,\n        \"num_unique_values\": 100,\n        \"samples\": [\n          4.232202434895871,\n          3.7889838002375433,\n          3.5915387036364406\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"feature_4\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.775817342549596,\n        \"min\": -2.6197451040897444,\n        \"max\": 7.57335980324986,\n        \"num_unique_values\": 100,\n        \"samples\": [\n          4.965315112613219,\n          4.3481638921978405,\n          3.4433708264760963\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"feature_5\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.6835423090088333,\n        \"min\": -1.9520877995225019,\n        \"max\": 7.63238206483739,\n        \"num_unique_values\": 100,\n        \"samples\": [\n          5.234214732536521,\n          5.047398671316414,\n          5.60600995134564\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"feature_6\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.578759386899525,\n        \"min\": -2.025142586657607,\n        \"max\": 6.797686526849523,\n        \"num_unique_values\": 100,\n        \"samples\": [\n          6.550500492814077,\n          4.139586634716047,\n          3.719570647503717\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"feature_7\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.7517571356384516,\n        \"min\": -1.9596701238797756,\n        \"max\": 7.455300139910895,\n        \"num_unique_values\": 100,\n        \"samples\": [\n          4.001645959261209,\n          4.615444455770175,\n          6.754794181984365\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"feature_8\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.575330472924609,\n        \"min\": -1.7033824393551547,\n        \"max\": 6.665474444462577,\n        \"num_unique_values\": 100,\n        \"samples\": [\n          5.984322398476584,\n          6.0062928092144405,\n          2.918070592116278\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"feature_9\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.8417170070453617,\n        \"min\": -1.9875689146008928,\n        \"max\": 7.16325472330546,\n        \"num_unique_values\": 100,\n        \"samples\": [\n          4.786011155774491,\n          4.423108130476852,\n          6.696456368290038\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(df['feature_0'], df['feature_1'])\n",
        "plt.xlabel('Feature 0')\n",
        "plt.ylabel('Feature 1')\n",
        "plt.title('Scatter Plot of First Two Features')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "EEVnd9gGfkal",
        "outputId": "dac8ea0e-71a7-4cbf-9781-69617e010f45"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import LocalOutlierFactor\n",
        "lof = LocalOutlierFactor(n_neighbors=10)\n",
        "outliers = lof.fit_predict(df)\n",
        "lof_scores = -lof.negative_outlier_factor_"
      ],
      "metadata": {
        "id": "IhRWxLwifqmo"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lof = LocalOutlierFactor(n_neighbors=20)\n",
        "outliers = lof.fit_predict(df)\n",
        "outlier_mask = outliers == -1\n",
        "inlier_mask = outliers != -1\n",
        "plt.scatter(df.loc[inlier_mask, 'feature_0'], df.loc[inlier_mask, 'feature_1'], label='Inliers', c='blue')\n",
        "plt.scatter(df.loc[outlier_mask, 'feature_0'], df.loc[outlier_mask, 'feature_1'], label='Outliers', c='red', edgecolor='k', s=100)\n",
        "plt.xlabel('Feature 0')\n",
        "plt.ylabel('Feature 1')\n",
        "plt.title('Scatter Plot of First Two Features with Outliers')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "7_W-KdUIfzaB",
        "outputId": "e29bcee7-2b86-47bf-be20-0ba9735e3da7"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "num_outliers = int(0.4 * df.shape[0])\n",
        "outliers = np.random.uniform(low=-10, high=15, size=(num_outliers, 10))"
      ],
      "metadata": {
        "id": "ZxmR5dDgf2L7"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_with_outliers = np.vstack((data, outliers))\n",
        "df_with_outliers = pd.DataFrame(data_with_outliers, columns=[f'feature_{i}' for i in range(10)])"
      ],
      "metadata": {
        "id": "FK17Wxh8gAzz"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lof = LocalOutlierFactor(n_neighbors=20)\n",
        "outliers_prediction = lof.fit_predict(df_with_outliers)"
      ],
      "metadata": {
        "id": "2uXVId6BgEI2"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "outlier_mask = outliers_prediction == -1\n",
        "inlier_mask = outliers_prediction != -1"
      ],
      "metadata": {
        "id": "Jdqm29CBgIqe"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "percent_outliers_detected = (outlier_mask.sum() / df_with_outliers.shape[0]) * 100"
      ],
      "metadata": {
        "id": "LUYEbgf1gSGe"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Percentage of outliers detected by LOF: {percent_outliers_detected:.2f}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IxRe90pDgass",
        "outputId": "a1d3d48c-4acf-435f-bdea-af5dadcfb312"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Percentage of outliers detected by LOF: 28.57%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(df_with_outliers.loc[inlier_mask, 'feature_0'], df_with_outliers.loc[inlier_mask, 'feature_1'], label='Inliers', c='blue')\n",
        "plt.scatter(df_with_outliers.loc[outlier_mask, 'feature_0'], df_with_outliers.loc[outlier_mask, 'feature_1'], label='Outliers', c='red', edgecolor='k', s=100)\n",
        "plt.xlabel('Feature 0')\n",
        "plt.ylabel('Feature 1')\n",
        "plt.title('Scatter Plot of First Two Features with Outliers')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "TkiopUG3hczQ",
        "outputId": "55f5a051-4e48-437a-bd75-eb7bfd433f3b"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "filtered_data = df_with_outliers.loc[inlier_mask]"
      ],
      "metadata": {
        "id": "jkhTWdUrhoWK"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lof = LocalOutlierFactor(n_neighbors=20)\n",
        "outliers_prediction = lof.fit_predict(filtered_data)"
      ],
      "metadata": {
        "id": "ETh69xWiidso"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "outlier_mask = outliers_prediction == -1\n",
        "inlier_mask = outliers_prediction != -1"
      ],
      "metadata": {
        "id": "FHQ732uHig26"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "percent_outliers_detected = (outlier_mask.sum() / df_with_outliers.shape[0]) * 100"
      ],
      "metadata": {
        "id": "Vh77NpLmii7p"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Percentage of outliers detected in the filtered data by LOF: {percent_outliers_detected:.2f}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FJZ9tnStik0h",
        "outputId": "055ae923-8641-448d-9a0e-30843648bc54"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Percentage of outliers detected in the filtered data by LOF: 0.00%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(filtered_data['feature_0'], filtered_data['feature_1'], label='Inliers', c='blue')\n",
        "plt.xlabel('Feature 0')\n",
        "plt.ylabel('Feature 1')\n",
        "plt.title('Scatter Plot of Filtered Data (With Outliers Highlighted)')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "6fmj3OsOioAE",
        "outputId": "846d3596-26cc-4bc8-f029-118656d42a23"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}