5: PyOD_Outlier_Detection_KNN.ipynb

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      "name": "PyOD_Outlier_Detection_KNN.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S7e6ED42IQGQ"
      },
      "source": [
        "PyOD Documentation : https://pyod.readthedocs.io/en/latest/\n",
        "\n",
        "\n",
        "Video help : https://www.youtube.com/watch?v=QPjG_313GOw"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nWADDWlopyXL"
      },
      "source": [
        "# **Some Common type of Outlier Algorithms:**\n",
        "\n",
        "\n",
        "# 1.   Angle-Based Outlier Detection (ABOD):\n",
        "\n",
        "It considers the relationship between each point and its neighbor(s). It does not consider the relationships among these neighbors. The variance of its weighted cosine scores to all neighbors could be viewed as the outlying score\n",
        "ABOD performs well on multi-dimensional data\n",
        "PyOD provides two different versions of ABOD:\n",
        "\n",
        "1.Fast ABOD: Uses k-nearest neighbors to approximate\n",
        "\n",
        "2.Original ABOD: Considers all training points with high-time complexity\n",
        "\n",
        "# 2.   k-Nearest Neighbors Detector:\n",
        "\n",
        "For any data point, the distance to its kth nearest neighbor could be viewed as the outlying score\n",
        "PyOD supports three kNN detectors:\n",
        "\n",
        "**Largest:** Uses the distance of the kth neighbor as the outlier score\n",
        "\n",
        "**Mean:** Uses the average of all k neighbors as the outlier score\n",
        "\n",
        "**Median:** Uses the median of the distance to k neighbors as the outlier score\n",
        "# 3.   Isolation Forest:\n",
        "It uses the scikit-learn library internally. In this method, data partitioning is done using a set of trees. Isolation Forest provides an anomaly score looking at how isolated the point is in the structure. The anomaly score is then used to identify outliers from normal observations\n",
        "Isolation Forest performs well on multi-dimensional data\n",
        "\n",
        "# 4.   Histogram-based Outlier Detection:\n",
        "It is an efficient unsupervised method which assumes the feature independence and calculates the outlier score by building histograms\n",
        "It is much faster than multivariate approaches, but at the cost of less precision\n",
        "\n",
        "# 5.   Local Correlation Integral (LOCI):\n",
        "\n",
        "LOCI is very effective for detecting outliers and groups of outliers. It provides a LOCI plot for each point which summarizes a lot of the information about the data in the area around the point, determining clusters, micro-clusters, their diameters, and their inter-cluster distances.\n",
        "\n",
        "None of the existing outlier-detection methods can match this feature because they output only a single number for each point\n",
        "\n",
        "# 6.   Feature Bagging:\n",
        "\n",
        "A feature bagging detector fits a number of base detectors on various sub-samples of the dataset. It uses averaging or other combination methods to improve the prediction accuracy\n",
        "By default, Local Outlier Factor (LOF) is used as the base estimator. However, any estimator could be used as the base estimator, such as kNN and ABOD\n",
        "Feature bagging first constructs n sub-samples by randomly selecting a subset of features. This brings out the diversity of base estimators. Finally, the prediction score is generated by averaging or taking the maximum of all base detectors\n",
        "\n",
        "# 7.   Clustering Based Local Outlier Factor:\n",
        "\n",
        "It classifies the data into small clusters and large clusters. The anomaly score is then calculated based on the size of the cluster the point belongs to, as well as the distance to the nearest large cluster\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JNBD_QS1nJpJ",
        "outputId": "a714c933-33b6-454e-e4e7-86795e763035"
      },
      "source": [
        "!pip install pyod\n"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting pyod\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/5c/3c/992cf76fd71e5438b2e5400a417ef433e2b1a726570e692102afcacfda33/pyod-0.8.8.tar.gz (102kB)\n",
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            "\u001b[?25hRequirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from pyod) (1.0.1)\n",
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            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from numba>=0.35->pyod) (56.1.0)\n",
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            "Requirement already satisfied: patsy>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from statsmodels->pyod) (0.5.1)\n",
            "Building wheels for collected packages: pyod\n",
            "  Building wheel for pyod (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyod: filename=pyod-0.8.8-cp37-none-any.whl size=116967 sha256=3b52ebee7bfc09051746da97f4dbea400f8545dc56bcf24a6fe595c170f49f2f\n",
            "  Stored in directory: /root/.cache/pip/wheels/54/dd/36/a12fa930455b7c38902f2dbf737175421ad30436e47ac5b24d\n",
            "Successfully built pyod\n",
            "Installing collected packages: pyod\n",
            "Successfully installed pyod-0.8.8\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DzfsuU68pBhr"
      },
      "source": [
        "#IMPORTING THE REQUIRED FUNCTION\n",
        "from pyod.models.knn import KNN  \n",
        "from pyod.utils.data import generate_data\n",
        "from pyod.utils.data import evaluate_print\n",
        "from pyod.utils.example import visualize\n"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-4sBpE0TshJp",
        "outputId": "6e35b033-e83d-4b04-aa75-7f5bd3bdc29c"
      },
      "source": [
        "# Data Generation:\n",
        "contamination = 0.1  # percentage of outliers\n",
        "n_train = 200  # number of training points\n",
        "n_test = 100  # number of testing points\n",
        "\n",
        "X_train, y_train, X_test, y_test = generate_data(\n",
        "    n_train=n_train, n_test=n_test, contamination=contamination)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pyod/utils/data.py:189: FutureWarning: behaviour=\"old\" is deprecated and will be removed in version 0.8.0. Please use behaviour=\"new\", which makes the returned datasets in the order of X_train, X_test, y_train, y_test.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TODqMGhKsu1c",
        "outputId": "23642a82-6ba4-4833-d99c-dd5b242eff03"
      },
      "source": [
        "# train kNN detector\n",
        "clf_name = 'KNN'\n",
        "clf = KNN()\n",
        "clf.fit(X_train)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNN(algorithm='auto', contamination=0.1, leaf_size=30, method='largest',\n",
              "  metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
              "  radius=1.0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MhFou5hxs2qZ"
      },
      "source": [
        "# get the prediction labels and outlier scores of the training data\n",
        "y_train_pred = clf.labels_  # binary labels (0: inliers, 1: outliers)\n",
        "y_train_scores = clf.decision_scores_  # raw outlier scores"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iB115MVD0lSQ",
        "outputId": "87314527-a4e1-4cff-c299-d19727e165f9"
      },
      "source": [
        "y_train_pred"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0,\n",
              "       0, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DOcKSbqWs9Jg"
      },
      "source": [
        "# get the prediction on the test data\n",
        "y_test_pred = clf.predict(X_test)  # outlier labels (0 or 1)\n",
        "y_test_scores = clf.decision_function(X_test)  # outlier scores"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KXJbhb1g087R",
        "outputId": "acd14e70-6433-473d-c4df-38f10651fd66"
      },
      "source": [
        "y_test_pred"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1,\n",
              "       0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4R3Yip5CtRdi",
        "outputId": "1158a0ee-7b21-41e3-cf1f-67010139ee2b"
      },
      "source": [
        "from pyod.utils.data import evaluate_print\n",
        "# evaluate and print the results\n",
        "print(\"\\nOn Training Data:\")\n",
        "evaluate_print(clf_name, y_train, y_train_scores)\n",
        "print(\"\\nOn Test Data:\")\n",
        "evaluate_print(clf_name, y_test, y_test_scores)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "On Training Data:\n",
            "KNN ROC:0.5963, precision @ rank n:0.15\n",
            "\n",
            "On Test Data:\n",
            "KNN ROC:0.6644, precision @ rank n:0.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 650
        },
        "id": "WsYij8L2tmuU",
        "outputId": "79f4be0b-84c1-412a-a5ac-a35d93665d96"
      },
      "source": [
        "#Visualize the outliers\n",
        "visualize(clf_name, X_train, y_train, X_test, y_test, y_train_pred,\n",
        "          y_test_pred, show_figure=True, save_figure=False)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x720 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}