pypsa_outliar_detection_v1.ipynb

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      "authorship_tag": "ABX9TyNG/DqIvaaYWkU2K7jjUsyK",
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Cesare-Caputo/PyPSA-gridtracer/blob/main/pypsa_outliar_detection_v1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#setup"
      ],
      "metadata": {
        "id": "6TwfiUC1_Yg5"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LsgJl0_xud9A",
        "outputId": "2c10b339-6c7d-4888-9791-1166a8146c78"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Successfully installed click-plugins-1.1.1 cligj-0.7.2 fiona-1.8.21 geopandas-0.10.2 munch-2.5.0 pyproj-3.2.1 rtree-1.0.0\n",
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            "Successfully installed affine-2.3.1 rasterio-1.2.10 snuggs-1.4.7\n"
          ]
        }
      ],
      "source": [
        "!pip install --upgrade pyod\n",
        "!pip install geopandas rtree\n",
        "!pip install rasterio"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive/')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u4FHt9ZeAa2J",
        "outputId": "9140f913-384f-4b44-d495-3e2e70a35921"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive/\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from __future__ import division\n",
        "from __future__ import print_function\n",
        "\n",
        "import os\n",
        "import sys\n",
        "from time import time\n",
        "\n",
        "import numpy as np\n",
        "from numpy import percentile\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.font_manager\n",
        "\n",
        "# Import all models\n",
        "from pyod.models.abod import ABOD\n",
        "from pyod.models.cblof import CBLOF\n",
        "#from pyod.models.feature_bagging import FeatureBagging\n",
        "from pyod.models.hbos import HBOS\n",
        "from pyod.models.iforest import IForest\n",
        "from pyod.models.knn import KNN\n",
        "from pyod.models.lof import LOF\n",
        "from pyod.models.mcd import MCD\n",
        "from pyod.models.ocsvm import OCSVM\n",
        "from pyod.models.pca import PCA\n",
        "from pyod.models.lscp import LSCP\n",
        "\n",
        "\n",
        "\n",
        "from pyod.utils.data import generate_data\n",
        "from pyod.utils.data import evaluate_print\n",
        "\n",
        "import pandas as pd\n",
        "import geopandas as gpd"
      ],
      "metadata": {
        "id": "fYrwleDZ_D0c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#utils"
      ],
      "metadata": {
        "id": "0z13UEqqCUlP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def return_coords_df(df):\n",
        "    gh_twr_df = df\n",
        "    gh_twr_df['lon'] = gh_twr_df.geometry.apply(lambda p: p.x)\n",
        "    gh_twr_df['lat'] = gh_twr_df.geometry.apply(lambda p: p.y)\n",
        "\n",
        "    # represent points consistently as (lat, lon)\n",
        "    #coords_df = gh_twr_df[['lat', 'lon']]\n",
        "    coords_df = gh_twr_df[['lon', 'lat']] # for visualiszing MST which seems to flip them\n",
        "\n",
        "    return coords_df\n",
        "\n",
        "\n",
        "def return_nparr_from_df(df):\n",
        "  gh_twr_hits_coords_df = return_coords_df(df)\n",
        "  test_pts = gh_twr_hits_coords_df.to_numpy()\n",
        "  return test_pts\n",
        "\n",
        "\n",
        "def generate_clf_noisy_datasets(twr_df, mu = 0 , sigma = .1):\n",
        "    import numpy as np\n",
        "    X = return_nparr_from_df(twr_df)\n",
        "    y = np.zeros(X.shape[0])\n",
        "\n",
        "    ## create noise for classifier ####\n",
        "    noise = np.random.normal(mu, sigma, X.shape)\n",
        "\n",
        "    noisy_X = X + noise\n",
        "    noisy_y = np.ones(noisy_X.shape[0])\n",
        "    X_gen = np.concatenate((X, noisy_X))\n",
        "    y_gen = np.concatenate((y, noisy_y))\n",
        "    return X_gen, y_gen"
      ],
      "metadata": {
        "id": "F7bkuVPfCVww"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from pyod.utils.data import check_consistent_shape\n",
        "from pyod.utils.data import get_outliers_inliers\n",
        "def visualize(clf_name, X_train, y_train, X_test, y_test, y_train_pred,\n",
        "              y_test_pred, show_figure=True,\n",
        "              save_figure=False):  # pragma: no cover\n",
        "    \"\"\"Utility function for visualizing the results in examples.\n",
        "    Internal use only.\n",
        "    Parameters\n",
        "    ----------\n",
        "    clf_name : str\n",
        "        The name of the detector.\n",
        "    X_train : numpy array of shape (n_samples, n_features)\n",
        "        The training samples.\n",
        "    y_train : list or array of shape (n_samples,)\n",
        "        The ground truth of training samples.\n",
        "    X_test : numpy array of shape (n_samples, n_features)\n",
        "        The test samples.\n",
        "    y_test : list or array of shape (n_samples,)\n",
        "        The ground truth of test samples.\n",
        "    y_train_pred : numpy array of shape (n_samples, n_features)\n",
        "        The predicted binary labels of the training samples.\n",
        "    y_test_pred : numpy array of shape (n_samples, n_features)\n",
        "        The predicted binary labels of the test samples.\n",
        "    show_figure : bool, optional (default=True)\n",
        "        If set to True, show the figure.\n",
        "    save_figure : bool, optional (default=False)\n",
        "        If set to True, save the figure to the local.\n",
        "    \"\"\"\n",
        "\n",
        "    def _add_sub_plot(X_inliers, X_outliers, sub_plot_title,\n",
        "                      inlier_color='blue', outlier_color='orange'):\n",
        "        \"\"\"Internal method to add subplot of inliers and outliers.\n",
        "        Parameters\n",
        "        ----------\n",
        "        X_inliers : numpy array of shape (n_samples, n_features)\n",
        "            Outliers.\n",
        "        X_outliers : numpy array of shape (n_samples, n_features)\n",
        "            Inliers.\n",
        "        sub_plot_title : str\n",
        "            Subplot title.\n",
        "        inlier_color : str, optional (default='blue')\n",
        "            The color of inliers.\n",
        "        outlier_color : str, optional (default='orange')\n",
        "            The color of outliers.\n",
        "        \"\"\"\n",
        "        plt.axis(\"equal\")\n",
        "        plt.scatter(X_inliers[:, 0], X_inliers[:, 1], label='inliers',\n",
        "                    color=inlier_color, s=4, alpha = .7)\n",
        "        plt.scatter(X_outliers[:, 0], X_outliers[:, 1],\n",
        "                    label='outliers', color=outlier_color, s=5, alpha = .3, marker='^')\n",
        "        plt.title(sub_plot_title, fontsize=15)\n",
        "        plt.xticks([])\n",
        "        plt.yticks([])\n",
        "        plt.legend(loc=3, prop={'size': 10})\n",
        "\n",
        "    # check input data shapes are consistent\n",
        "    X_train, y_train, X_test, y_test, y_train_pred, y_test_pred = \\\n",
        "        check_consistent_shape(X_train, y_train, X_test, y_test, y_train_pred,\n",
        "                               y_test_pred)\n",
        "\n",
        "    if X_train.shape[1] != 2:\n",
        "        raise ValueError(\"Input data has to be 2-d for visualization. The \"\n",
        "                         \"input data has {shape}.\".format(shape=X_train.shape))\n",
        "\n",
        "    X_train_outliers, X_train_inliers = get_outliers_inliers(X_train, y_train)\n",
        "    X_train_outliers_pred, X_train_inliers_pred = get_outliers_inliers(\n",
        "        X_train, y_train_pred)\n",
        "\n",
        "    X_test_outliers, X_test_inliers = get_outliers_inliers(X_test, y_test)\n",
        "    X_test_outliers_pred, X_test_inliers_pred = get_outliers_inliers(\n",
        "        X_test, y_test_pred)\n",
        "\n",
        "    # plot ground truth vs. predicted results\n",
        "    fig = plt.figure(figsize=(12, 10))\n",
        "    plt.suptitle(\"Demo of {clf_name} Detector\".format(clf_name=clf_name),\n",
        "                 fontsize=15)\n",
        "\n",
        "    fig.add_subplot(221)\n",
        "    _add_sub_plot(X_train_inliers, X_train_outliers, 'Train Set Ground Truth',\n",
        "                  inlier_color='blue', outlier_color='orange')\n",
        "\n",
        "    fig.add_subplot(222)\n",
        "    _add_sub_plot(X_train_inliers_pred, X_train_outliers_pred,\n",
        "                  'Train Set Prediction', inlier_color='blue',\n",
        "                  outlier_color='orange')\n",
        "\n",
        "    fig.add_subplot(223)\n",
        "    _add_sub_plot(X_test_inliers, X_test_outliers, 'Test Set Ground Truth',\n",
        "                  inlier_color='green', outlier_color='red')\n",
        "\n",
        "    fig.add_subplot(224)\n",
        "    _add_sub_plot(X_test_inliers_pred, X_test_outliers_pred,\n",
        "                  'Test Set Prediction', inlier_color='green',\n",
        "                  outlier_color='red')\n",
        "\n",
        "    if save_figure:\n",
        "        plt.savefig('{clf_name}.png'.format(clf_name=clf_name), dpi=300)\n",
        "\n",
        "    if show_figure:\n",
        "        plt.show()"
      ],
      "metadata": {
        "id": "vQwlDx9yJymo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# generate datasets for classifier"
      ],
      "metadata": {
        "id": "rK19FKSpd7Bk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##load tower OSM data for maxar countries"
      ],
      "metadata": {
        "id": "JXoXvo5fCW2S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dirs = ['ghana']\n",
        "country_dict = { 'ghana':'GH'}\n",
        "base_path = os.path.join('/content', 'drive', 'My Drive', 'PyPSA_Africa_images')\n",
        "out_path = os.path.join(base_path, 'MAXAR_256')\n",
        "\n",
        "for country in dirs:\n",
        "    country_code = country_dict[country]\n",
        "    max_ratio = 1.\n",
        "    print(f\"Making Examples for {country}!\")\n",
        "\n",
        "    # assess satellite imagery\n",
        "    data_path = os.path.join(base_path, 'maxar', country, 'raw')\n",
        "\n",
        "    #coverage_polygon = make_examples.make_polygon_list(data_path)\n",
        "    lines_file = os.path.join(data_path, f\"{country_code}_raw_lines.geojson\")\n",
        "    towers_file = os.path.join(data_path, f\"{country_code}_raw_towers.geojson\")\n",
        "\n",
        "    gh_line_df = gpd.read_file(lines_file) # ground truth\n",
        "    gh_twr_df = gpd.read_file(towers_file) # input into algo"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ERu7ycBlAsCT",
        "outputId": "f993cccc-2468-48ee-91f5-00ca454fa5cf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Making Examples for ghana!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dirs = ['sierra_leone']\n",
        "country_dict = {'sierra_leone': 'SL'}\n",
        "base_path = os.path.join('/content', 'drive', 'My Drive', 'PyPSA_Africa_images')\n",
        "out_path = os.path.join(base_path, 'MAXAR_256')\n",
        "\n",
        "for country in dirs:\n",
        "    country_code = country_dict[country]\n",
        "    max_ratio = 1.\n",
        "    print(f\"Making Examples for {country}!\")\n",
        "\n",
        "    # assess satellite imagery\n",
        "\n",
        "    data_path = os.path.join(base_path, 'maxar', country, 'raw')\n",
        "\n",
        "    #coverage_polygon = make_examples.make_polygon_list(data_path)\n",
        "    towers_file = os.path.join(data_path, f\"{country_code}_raw_towers.geojson\")\n",
        "    lines_file = os.path.join(data_path, f\"{country_code}_raw_lines.geojson\")\n",
        "\n",
        "    sl_twr_df = gpd.read_file(towers_file)\n",
        "\n",
        "    sl_line_df = gpd.read_file(lines_file)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ytqqiV_dd4Rp",
        "outputId": "2f6abc6d-89b4-49b8-c15f-9c578e806e42"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Making Examples for sierra_leone!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dirs = ['malawi']\n",
        "country_dict = {'malawi': 'MW'}\n",
        "base_path = os.path.join('/content', 'drive', 'My Drive', 'PyPSA_Africa_images')\n",
        "out_path = os.path.join(base_path, 'MAXAR_256')\n",
        "\n",
        "for country in dirs:\n",
        "    country_code = country_dict[country]\n",
        "    max_ratio = 1.\n",
        "    print(f\"Making Examples for {country}!\")\n",
        "\n",
        "    # assess satellite imagery\n",
        "\n",
        "    data_path = os.path.join(base_path, 'maxar', country, 'raw')\n",
        "\n",
        "    #coverage_polygon = make_examples.make_polygon_list(data_path)\n",
        "    towers_file = os.path.join(data_path, f\"{country_code}_raw_towers.geojson\")\n",
        "    lines_file = os.path.join(data_path, f\"{country_code}_raw_lines.geojson\")\n",
        "\n",
        "    mw_twr_df = gpd.read_file(towers_file)\n",
        "\n",
        "    mw_line_df = gpd.read_file(lines_file)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Iaz6N_dKeDEd",
        "outputId": "6a2789b6-e0e7-47c4-cc57-cddb85ee2126"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Making Examples for malawi!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dirs = ['chad']\n",
        "country_dict = {'chad': 'TD'}\n",
        "base_path = os.path.join('/content', 'drive', 'My Drive', 'PyPSA_Africa_images')\n",
        "out_path = os.path.join(base_path, 'MAXAR_256')\n",
        "\n",
        "for country in dirs:\n",
        "    country_code = country_dict[country]\n",
        "    max_ratio = 1.\n",
        "    print(f\"Making Examples for {country}!\")\n",
        "\n",
        "    # assess satellite imagery\n",
        "\n",
        "    data_path = os.path.join(base_path, 'maxar', country, 'raw')\n",
        "\n",
        "    #coverage_polygon = make_examples.make_polygon_list(data_path)\n",
        "    towers_file = os.path.join(data_path, f\"{country_code}_raw_towers.geojson\")\n",
        "    lines_file = os.path.join(data_path, f\"{country_code}_raw_lines.geojson\")\n",
        "\n",
        "    ch_twr_df = gpd.read_file(towers_file)\n",
        "\n",
        "    ch_line_df = gpd.read_file(lines_file)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "71Z53RdceG3d",
        "outputId": "a97eebc4-be3f-451e-8981-23fed4a9d9f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Making Examples for chad!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## add noise and convert to array format in lon lat"
      ],
      "metadata": {
        "id": "kPzQvrlbfDcz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "maxar_twr_df = gpd.GeoDataFrame(pd.concat([ch_twr_df, gh_twr_df, mw_twr_df, sl_twr_df], ignore_index = True))\n",
        "maxar_twr_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ZzCGnMNQfILN",
        "outputId": "d2cdff75-158f-4369-ff14-92e6835a5b41"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "             id tags.power tags.ref  Type Country                   geometry  \\\n",
              "0  6.225625e+09      tower     None  Node      TD  POINT (15.12929 12.14937)   \n",
              "1  5.938765e+09      tower     None  Node      TD  POINT (16.73191 10.28529)   \n",
              "2  5.938765e+09      tower     None  Node      TD  POINT (16.73693 10.28394)   \n",
              "3  6.225625e+09      tower     None  Node      TD  POINT (15.10714 12.17727)   \n",
              "4  5.938765e+09      tower     None  Node      TD  POINT (16.84126 10.20589)   \n",
              "\n",
              "  tags.material tags.height tags.structure tags.line_management tags.operator  \n",
              "0           NaN         NaN            NaN                  NaN           NaN  \n",
              "1           NaN         NaN            NaN                  NaN           NaN  \n",
              "2           NaN         NaN            NaN                  NaN           NaN  \n",
              "3           NaN         NaN            NaN                  NaN           NaN  \n",
              "4           NaN         NaN            NaN                  NaN           NaN  "
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              "      <th>0</th>\n",
              "      <td>6.225625e+09</td>\n",
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              "      <th>2</th>\n",
              "      <td>5.938765e+09</td>\n",
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              "      <th>3</th>\n",
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        "maxar_twr_df.tail()"
      ],
      "metadata": {
        "colab": {
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          "height": 206
        },
        "id": "ZklExCEnf1r9",
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      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "                 id tags.power tags.ref  Type       Country  \\\n",
              "21474  3.166191e+09      tower      NaN  Node  sierra-leone   \n",
              "21475  2.088170e+09      tower      NaN  Node  sierra-leone   \n",
              "21476  8.668409e+09      tower      NaN  Node  sierra-leone   \n",
              "21477  2.179090e+09      tower      NaN  Node  sierra-leone   \n",
              "21478  2.179091e+09      tower      NaN  Node  sierra-leone   \n",
              "\n",
              "                        geometry tags.material tags.height tags.structure  \\\n",
              "21474  POINT (-12.77661 8.51718)           NaN         NaN            NaN   \n",
              "21475  POINT (-12.18536 8.83500)           NaN         NaN            NaN   \n",
              "21476  POINT (-12.22714 8.74219)           NaN         NaN            NaN   \n",
              "21477  POINT (-13.05836 8.32456)           NaN         NaN            NaN   \n",
              "21478  POINT (-13.02260 8.32977)           NaN         NaN            NaN   \n",
              "\n",
              "      tags.line_management tags.operator  \n",
              "21474                  NaN           NaN  \n",
              "21475                  NaN           NaN  \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9416796d-f48f-4094-8056-8dd55742d026 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9416796d-f48f-4094-8056-8dd55742d026');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          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",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X, y = generate_clf_noisy_datasets(gh_twr_df)"
      ],
      "metadata": {
        "id": "mq15g421ChJM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# compare performance of model alternatives"
      ],
      "metadata": {
        "id": "iXH9hoe3ghZg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from pyod.utils.utility import standardizer\n",
        "from pyod.utils.utility import precision_n_scores\n",
        "from sklearn.metrics import roc_auc_score\n",
        "\n",
        "\n",
        "# define the number of iterations\n",
        "n_ite = 10\n",
        "n_classifiers = 10\n",
        "\n",
        "df_columns = ['Data', '#Samples', '# Dimensions', 'Outlier Perc',\n",
        "              'ABOD', 'CBLOF', 'HBOS', 'IForest', 'KNN', 'LOF',\n",
        "              'MCD', 'OCSVM', 'PCA']\n",
        "\n",
        "# initialize the container for saving the results\n",
        "roc_df = pd.DataFrame(columns=df_columns)\n",
        "prn_df = pd.DataFrame(columns=df_columns)\n",
        "time_df = pd.DataFrame(columns=df_columns)\n",
        "\n",
        "X = X\n",
        "y= y\n",
        "\n",
        "outliers_fraction = np.count_nonzero(y) / len(y)\n",
        "outliers_percentage = round(outliers_fraction * 100, ndigits=4)\n",
        "\n",
        "# construct containers for saving results\n",
        "roc_list = [ X.shape[0], X.shape[1], outliers_percentage]\n",
        "prn_list = [ X.shape[0], X.shape[1], outliers_percentage]\n",
        "time_list = [X.shape[0], X.shape[1], outliers_percentage]\n",
        "\n",
        "roc_mat = np.zeros([n_ite, n_classifiers])\n",
        "prn_mat = np.zeros([n_ite, n_classifiers])\n",
        "time_mat = np.zeros([n_ite, n_classifiers])\n",
        "\n",
        "for i in range(n_ite):\n",
        "    print(\"ProcessingIteration'\", i + 1)\n",
        "    random_state = np.random.RandomState(i)\n",
        "\n",
        "    # 60% data for training and 40% for testing\n",
        "    X_train, X_test, y_train, y_test = \\\n",
        "        train_test_split(X, y, test_size=0.4, random_state=random_state)\n",
        "\n",
        "    # standardizing data for processing\n",
        "    X_train_norm, X_test_norm = standardizer(X_train, X_test)\n",
        "\n",
        "    classifiers = {'Angle-based Outlier Detector (ABOD)': ABOD(\n",
        "        contamination=outliers_fraction),\n",
        "        'Cluster-based Local Outlier Factor': CBLOF(\n",
        "            n_clusters=10,\n",
        "            contamination=outliers_fraction,\n",
        "            check_estimator=False,\n",
        "            random_state=random_state),\n",
        "        'Histogram-base Outlier Detection (HBOS)': HBOS(\n",
        "            contamination=outliers_fraction),\n",
        "        'Isolation Forest': IForest(contamination=outliers_fraction,\n",
        "                                    random_state=random_state),\n",
        "        'K Nearest Neighbors (KNN)': KNN(contamination=outliers_fraction),\n",
        "        'Local Outlier Factor (LOF)': LOF(\n",
        "            contamination=outliers_fraction),\n",
        "        'Minimum Covariance Determinant (MCD)': MCD(\n",
        "            contamination=outliers_fraction, random_state=random_state),\n",
        "        'One-class SVM (OCSVM)': OCSVM(contamination=outliers_fraction),\n",
        "        'Principal Component Analysis (PCA)': PCA(\n",
        "            contamination=outliers_fraction, random_state=random_state),\n",
        "    }\n",
        "    classifiers_indices = {\n",
        "        'Angle-based Outlier Detector (ABOD)': 0,\n",
        "        'Cluster-based Local Outlier Factor': 1,\n",
        "        'Histogram-base Outlier Detection (HBOS)': 3,\n",
        "        'Isolation Forest': 4,\n",
        "        'K Nearest Neighbors (KNN)': 5,\n",
        "        'Local Outlier Factor (LOF)': 6,\n",
        "        'Minimum Covariance Determinant (MCD)': 7,\n",
        "        'One-class SVM (OCSVM)': 8,\n",
        "        'Principal Component Analysis (PCA)': 9,\n",
        "    }\n",
        "\n",
        "    for clf_name, clf in classifiers.items():\n",
        "        t0 = time()\n",
        "        clf.fit(X_train_norm)\n",
        "        test_scores = clf.decision_function(X_test_norm)\n",
        "        t1 = time()\n",
        "        duration = round(t1 - t0, ndigits=4)\n",
        "\n",
        "        roc = round(roc_auc_score(y_test, test_scores), ndigits=4)\n",
        "        prn = round(precision_n_scores(y_test, test_scores), ndigits=4)\n",
        "\n",
        "        print('{clf_name} ROC:{roc}, precision @ rank n:{prn}, '\n",
        "              'execution time: {duration}s'.format(\n",
        "            clf_name=clf_name, roc=roc, prn=prn, duration=duration))\n",
        "\n",
        "        time_mat[i, classifiers_indices[clf_name]] = duration\n",
        "        roc_mat[i, classifiers_indices[clf_name]] = roc\n",
        "        prn_mat[i, classifiers_indices[clf_name]] = prn\n",
        "\n",
        "time_list = time_list + np.mean(time_mat, axis=0).tolist()\n",
        "temp_df = pd.DataFrame(time_list).transpose()\n",
        "temp_df.columns = df_columns\n",
        "time_df = pd.concat([time_df, temp_df], axis=0)\n",
        "\n",
        "roc_list = roc_list + np.mean(roc_mat, axis=0).tolist()\n",
        "temp_df = pd.DataFrame(roc_list).transpose()\n",
        "temp_df.columns = df_columns\n",
        "roc_df = pd.concat([roc_df, temp_df], axis=0)\n",
        "\n",
        "prn_list = prn_list + np.mean(prn_mat, axis=0).tolist()\n",
        "temp_df = pd.DataFrame(prn_list).transpose()\n",
        "temp_df.columns = df_columns\n",
        "prn_df = pd.concat([prn_df, temp_df], axis=0)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "rCuVnib0Ghbv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ba4f3ac9-2551-4fdf-b563-18e9de908dcd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ProcessingIteration' 1\n",
            "Angle-based Outlier Detector (ABOD) ROC:0.9618, precision @ rank n:0.9067, execution time: 10.6272s\n",
            "Cluster-based Local Outlier Factor ROC:0.5057, precision @ rank n:0.5074, execution time: 2.0704s\n",
            "Histogram-base Outlier Detection (HBOS) ROC:0.505, precision @ rank n:0.5063, execution time: 1.8802s\n",
            "Isolation Forest ROC:0.5324, precision @ rank n:0.5302, execution time: 2.4205s\n",
            "K Nearest Neighbors (KNN) ROC:0.9196, precision @ rank n:0.8414, execution time: 1.1615s\n",
            "Local Outlier Factor (LOF) ROC:0.8581, precision @ rank n:0.7835, execution time: 0.2347s\n",
            "Minimum Covariance Determinant (MCD) ROC:0.5001, precision @ rank n:0.5012, execution time: 5.9195s\n",
            "One-class SVM (OCSVM) ROC:0.5005, precision @ rank n:0.5015, execution time: 68.3227s\n",
            "Principal Component Analysis (PCA) ROC:0.4942, precision @ rank n:0.5012, execution time: 0.011s\n",
            "ProcessingIteration' 2\n",
            "Angle-based Outlier Detector (ABOD) ROC:0.9585, precision @ rank n:0.9057, execution time: 6.3217s\n",
            "Cluster-based Local Outlier Factor ROC:0.5121, precision @ rank n:0.5122, execution time: 0.5065s\n",
            "Histogram-base Outlier Detection (HBOS) ROC:0.5013, precision @ rank n:0.497, execution time: 0.0102s\n",
            "Isolation Forest ROC:0.5321, precision @ rank n:0.5225, execution time: 2.2669s\n",
            "K Nearest Neighbors (KNN) ROC:0.9178, precision @ rank n:0.8409, execution time: 1.1469s\n",
            "Local Outlier Factor (LOF) ROC:0.8555, precision @ rank n:0.7834, execution time: 0.2276s\n",
            "Minimum Covariance Determinant (MCD) ROC:0.5036, precision @ rank n:0.5011, execution time: 4.9439s\n",
            "One-class SVM (OCSVM) ROC:0.5059, precision @ rank n:0.5025, execution time: 67.889s\n",
            "Principal Component Analysis (PCA) ROC:0.498, precision @ rank n:0.4992, execution time: 0.0094s\n",
            "ProcessingIteration' 3\n",
            "Angle-based Outlier Detector (ABOD) ROC:0.9614, precision @ rank n:0.906, execution time: 6.1913s\n",
            "Cluster-based Local Outlier Factor ROC:0.5072, precision @ rank n:0.5111, execution time: 0.6333s\n",
            "Histogram-base Outlier Detection (HBOS) ROC:0.5046, precision @ rank n:0.5069, execution time: 0.0065s\n",
            "Isolation Forest ROC:0.529, precision @ rank n:0.5264, execution time: 2.288s\n",
            "K Nearest Neighbors (KNN) ROC:0.9183, precision @ rank n:0.8416, execution time: 1.1625s\n",
            "Local Outlier Factor (LOF) ROC:0.8619, precision @ rank n:0.784, execution time: 0.2296s\n",
            "Minimum Covariance Determinant (MCD) ROC:0.5032, precision @ rank n:0.5067, execution time: 4.743s\n",
            "One-class SVM (OCSVM) ROC:0.5042, precision @ rank n:0.5041, execution time: 68.4919s\n",
            "Principal Component Analysis (PCA) ROC:0.5025, precision @ rank n:0.5061, execution time: 0.01s\n",
            "ProcessingIteration' 4\n",
            "Angle-based Outlier Detector (ABOD) ROC:0.9591, precision @ rank n:0.9034, execution time: 6.1306s\n",
            "Cluster-based Local Outlier Factor ROC:0.5, precision @ rank n:0.4998, execution time: 0.7104s\n",
            "Histogram-base Outlier Detection (HBOS) ROC:0.5008, precision @ rank n:0.4964, execution time: 0.0056s\n",
            "Isolation Forest ROC:0.5284, precision @ rank n:0.5268, execution time: 2.3304s\n",
            "K Nearest Neighbors (KNN) ROC:0.9154, precision @ rank n:0.8381, execution time: 1.154s\n",
            "Local Outlier Factor (LOF) ROC:0.8564, precision @ rank n:0.7804, execution time: 0.2226s\n",
            "Minimum Covariance Determinant (MCD) ROC:0.5084, precision @ rank n:0.5063, execution time: 6.0881s\n",
            "One-class SVM (OCSVM) ROC:0.5067, precision @ rank n:0.5047, execution time: 68.0045s\n",
            "Principal Component Analysis (PCA) ROC:0.5049, precision @ rank n:0.5066, execution time: 0.009s\n",
            "ProcessingIteration' 5\n",
            "Angle-based Outlier Detector (ABOD) ROC:0.9614, precision @ rank n:0.9066, execution time: 6.184s\n",
            "Cluster-based Local Outlier Factor ROC:0.5084, precision @ rank n:0.5057, execution time: 0.422s\n",
            "Histogram-base Outlier Detection (HBOS) ROC:0.5012, precision @ rank n:0.4974, execution time: 0.0109s\n",
            "Isolation Forest ROC:0.5272, precision @ rank n:0.5214, execution time: 2.3645s\n",
            "K Nearest Neighbors (KNN) ROC:0.9207, precision @ rank n:0.843, execution time: 1.1698s\n",
            "Local Outlier Factor (LOF) ROC:0.8647, precision @ rank n:0.7873, execution time: 0.2221s\n",
            "Minimum Covariance Determinant (MCD) ROC:0.5011, precision @ rank n:0.4967, execution time: 4.8124s\n",
            "One-class SVM (OCSVM) ROC:0.5001, precision @ rank n:0.4967, execution time: 68.1434s\n",
            "Principal Component Analysis (PCA) ROC:0.498, precision @ rank n:0.4967, execution time: 0.01s\n",
            "ProcessingIteration' 6\n",
            "Angle-based Outlier Detector (ABOD) ROC:0.9614, precision @ rank n:0.9084, execution time: 6.3683s\n",
            "Cluster-based Local Outlier Factor ROC:0.5065, precision @ rank n:0.5033, execution time: 0.5624s\n",
            "Histogram-base Outlier Detection (HBOS) ROC:0.5051, precision @ rank n:0.5043, execution time: 0.0084s\n",
            "Isolation Forest ROC:0.5272, precision @ rank n:0.523, execution time: 2.358s\n",
            "K Nearest Neighbors (KNN) ROC:0.9208, precision @ rank n:0.843, execution time: 1.1685s\n",
            "Local Outlier Factor (LOF) ROC:0.8617, precision @ rank n:0.7822, execution time: 0.2252s\n",
            "Minimum Covariance Determinant (MCD) ROC:0.5044, precision @ rank n:0.5038, execution time: 4.9357s\n",
            "One-class SVM (OCSVM) ROC:0.505, precision @ rank n:0.504, execution time: 67.9335s\n",
            "Principal Component Analysis (PCA) ROC:0.5043, precision @ rank n:0.5047, execution time: 0.0142s\n",
            "ProcessingIteration' 7\n",
            "Angle-based Outlier Detector (ABOD) ROC:0.9572, precision @ rank n:0.8988, execution time: 6.2346s\n",
            "Cluster-based Local Outlier Factor ROC:0.508, precision @ rank n:0.4981, execution time: 0.5574s\n",
            "Histogram-base Outlier Detection (HBOS) ROC:0.5013, precision @ rank n:0.4996, execution time: 0.0069s\n",
            "Isolation Forest ROC:0.5251, precision @ rank n:0.5165, execution time: 2.3354s\n",
            "K Nearest Neighbors (KNN) ROC:0.9184, precision @ rank n:0.838, execution time: 1.1762s\n",
            "Local Outlier Factor (LOF) ROC:0.8606, precision @ rank n:0.7873, execution time: 0.2254s\n",
            "Minimum Covariance Determinant (MCD) ROC:0.4975, precision @ rank n:0.4961, execution time: 5.5056s\n",
            "One-class SVM (OCSVM) ROC:0.4999, precision @ rank n:0.4991, execution time: 68.5412s\n",
            "Principal Component Analysis (PCA) ROC:0.5031, precision @ rank n:0.5014, execution time: 0.0097s\n",
            "ProcessingIteration' 8\n",
            "Angle-based Outlier Detector (ABOD) ROC:0.9627, precision @ rank n:0.9094, execution time: 6.3428s\n",
            "Cluster-based Local Outlier Factor ROC:0.5106, precision @ rank n:0.516, execution time: 0.3841s\n",
            "Histogram-base Outlier Detection (HBOS) ROC:0.4983, precision @ rank n:0.5024, execution time: 0.0076s\n",
            "Isolation Forest ROC:0.524, precision @ rank n:0.524, execution time: 2.3736s\n",
            "K Nearest Neighbors (KNN) ROC:0.922, precision @ rank n:0.8457, execution time: 1.1667s\n",
            "Local Outlier Factor (LOF) ROC:0.8631, precision @ rank n:0.7858, execution time: 0.2231s\n",
            "Minimum Covariance Determinant (MCD) ROC:0.4981, precision @ rank n:0.5007, execution time: 5.1485s\n",
            "One-class SVM (OCSVM) ROC:0.5019, precision @ rank n:0.5049, execution time: 67.696s\n",
            "Principal Component Analysis (PCA) ROC:0.4964, precision @ rank n:0.5009, execution time: 0.0089s\n",
            "ProcessingIteration' 9\n",
            "Angle-based Outlier Detector (ABOD) ROC:0.9603, precision @ rank n:0.9057, execution time: 6.2996s\n",
            "Cluster-based Local Outlier Factor ROC:0.5063, precision @ rank n:0.5146, execution time: 0.61s\n",
            "Histogram-base Outlier Detection (HBOS) ROC:0.5022, precision @ rank n:0.508, execution time: 0.007s\n",
            "Isolation Forest ROC:0.5257, precision @ rank n:0.5265, execution time: 2.3645s\n",
            "K Nearest Neighbors (KNN) ROC:0.918, precision @ rank n:0.8413, execution time: 1.1709s\n",
            "Local Outlier Factor (LOF) ROC:0.8598, precision @ rank n:0.7853, execution time: 0.2281s\n",
            "Minimum Covariance Determinant (MCD) ROC:0.4987, precision @ rank n:0.5019, execution time: 5.3449s\n",
            "One-class SVM (OCSVM) ROC:0.5047, precision @ rank n:0.5051, execution time: 68.0619s\n",
            "Principal Component Analysis (PCA) ROC:0.4974, precision @ rank n:0.5039, execution time: 0.0088s\n",
            "ProcessingIteration' 10\n",
            "Angle-based Outlier Detector (ABOD) ROC:0.9591, precision @ rank n:0.9043, execution time: 6.2414s\n",
            "Cluster-based Local Outlier Factor ROC:0.5081, precision @ rank n:0.5135, execution time: 0.4508s\n",
            "Histogram-base Outlier Detection (HBOS) ROC:0.5006, precision @ rank n:0.5019, execution time: 0.0079s\n",
            "Isolation Forest ROC:0.5227, precision @ rank n:0.5161, execution time: 2.3468s\n",
            "K Nearest Neighbors (KNN) ROC:0.9176, precision @ rank n:0.8402, execution time: 1.1474s\n",
            "Local Outlier Factor (LOF) ROC:0.8634, precision @ rank n:0.7899, execution time: 0.2282s\n",
            "Minimum Covariance Determinant (MCD) ROC:0.4976, precision @ rank n:0.5023, execution time: 6.1122s\n",
            "One-class SVM (OCSVM) ROC:0.4956, precision @ rank n:0.4964, execution time: 68.2205s\n",
            "Principal Component Analysis (PCA) ROC:0.4999, precision @ rank n:0.5006, execution time: 0.0096s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8shq1KCJqD_r"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "based on the above, ABOD, KNN or LOF could be good options"
      ],
      "metadata": {
        "id": "x1zXiM_ZnKat"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#ECOD/CBLOF model"
      ],
      "metadata": {
        "id": "XW6xWjG2p_bT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://arxiv.org/abs/2201.00382"
      ],
      "metadata": {
        "id": "SqWg-g38qDm2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from pyod.models.cblof import CBLOF\n",
        "clf = CBLOF()\n",
        "#clf.fit(X_train)\n",
        "clf.fit(X_train_norm)\n",
        "test_scores = clf.decision_function(X_test_norm)\n",
        "#t1 = time()\n",
        "#duration = round(t1 - t0, ndigits=4)\n",
        "\n",
        "roc = round(roc_auc_score(y_test, test_scores), ndigits=4)\n",
        "prn = round(precision_n_scores(y_test, test_scores), ndigits=4)\n",
        "\n",
        "print('{clf_name} ROC:{roc}, precision @ rank n:{prn}, '\n",
        "      'execution time: {duration}s'.format(\n",
        "    clf_name=clf_name, roc=roc, prn=prn, duration=duration))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lgZ4ogBxvNtN",
        "outputId": "a6190f3e-b706-4dad-e4cc-f8f10d1e4497"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LOF ROC:0.5027, precision @ rank n:0.5065, execution time: 2064.9308s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pyod.models.ecod import ECOD\n",
        "clf_name = 'ECOD'\n",
        "clf = ECOD()\n",
        "#clf.fit(X_train)\n",
        "clf.fit(X_train_norm)\n",
        "test_scores = clf.decision_function(X_test_norm)\n",
        "#t1 = time()\n",
        "#duration = round(t1 - t0, ndigits=4)\n",
        "\n",
        "roc = round(roc_auc_score(y_test, test_scores), ndigits=4)\n",
        "prn = round(precision_n_scores(y_test, test_scores), ndigits=4)\n",
        "\n",
        "print('{clf_name} ROC:{roc}, precision @ rank n:{prn}, '\n",
        "      'execution time: {duration}s'.format(\n",
        "    clf_name=clf_name, roc=roc, prn=prn, duration=duration))"
      ],
      "metadata": {
        "id": "9Vv0CzXtqBJL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# get outlier scores\n",
        "y_train_scores = clf.decision_scores_  # raw outlier scores on the train data\n",
        "y_test_scores = clf.decision_function(X_test)  # predict raw outlier scores on test"
      ],
      "metadata": {
        "id": "yyKIC2HPqXPm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 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)"
      ],
      "metadata": {
        "id": "MKaXqxhmqfRj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# inference function on new data points"
      ],
      "metadata": {
        "id": "XJlqjwjcMyc5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from pyod.utils.utility import standardizer\n",
        "from pyod.utils.utility import precision_n_scores\n",
        "from sklearn.metrics import roc_auc_score\n",
        "X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.33, random_state=42)\n",
        "# train LOF detector\n",
        "clf_name = 'LOF'\n",
        "clf = LOF(contamination = .5)\n",
        "clf.fit(X_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z83KKtALHJud",
        "outputId": "cd896e5c-92dd-4dd2-f7f8-df4c823999ac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LOF(algorithm='auto', contamination=0.5, leaf_size=30, metric='minkowski',\n",
              "  metric_params=None, n_jobs=1, n_neighbors=20, novelty=True, p=2)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "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"
      ],
      "metadata": {
        "id": "zsyyrK8dHVkk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from pyod.utils.data import generate_data\n",
        "from pyod.utils.data import evaluate_print\n",
        "\n",
        "# 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\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)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5iOCAgjYIFlX",
        "outputId": "e3e6c093-9a94-40fe-baea-86a8ee79ce62"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "On Training Data:\n",
            "LOF ROC:0.8622, precision @ rank n:0.7878\n",
            "\n",
            "On Test Data:\n",
            "LOF ROC:0.8655, precision @ rank n:0.7871\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# visualize the results\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)"
      ],
      "metadata": {
        "id": "YMpZEAt9Kp65",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 650
        },
        "outputId": "b5920cb6-05bb-44fe-a955-83041f0d3429"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x720 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#save model to use in other scripts"
      ],
      "metadata": {
        "id": "2PZFpo1A97Fw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from joblib import dump, load\n",
        "model_name = 'grid_outlier_detection_v1.joblib'\n",
        "\n",
        "model_path = '/content/drive/MyDrive/PyPSA_Africa_images/models/outlier_detection/'+model_name\n",
        "# save the model\n",
        "dump(clf, model_path)\n",
        "# load the model\n",
        "#clf = load('grid_outlier_detection_v1.joblib')"
      ],
      "metadata": {
        "id": "HxZJ_lfIL4co",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "121852f7-56d2-4c63-9f8c-84e2049ad2fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['/content/drive/MyDrive/PyPSA_Africa_images/models/outlier_detection/grid_outlier_detection_v1.joblib']"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def grid_outlier_inference(model_path, twr_df):\n",
        "    clf = load(model_path)\n",
        "    X = return_nparr_from_df(twr_df)\n",
        "    # get the prediction on the new data\n",
        "    y_test_pred = clf.predict(X)  # outlier labels (0 or 1)\n",
        "    y_test_scores = clf.decision_function(X)  # outlier scores\n",
        "    return y_test_pred, y_test_scores\n"
      ],
      "metadata": {
        "id": "kS4FjrOhM1FG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#model_path = 'grid_outlier_detection.joblib'\n",
        "twr_df = maxar_twr_df[0:500]\n",
        "grid_outlier_inference(model_path, twr_df)"
      ],
      "metadata": {
        "id": "u7Y-3RX6N94-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clf = load(model_path)"
      ],
      "metadata": {
        "id": "MjPli4v-2jnW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "twr_df = maxar_twr_df[500:1000]\n",
        "X = return_nparr_from_df(twr_df)\n",
        "y_test_pred = clf.predict(X)  # outlier labels (0 or 1)\n",
        "y_test_scores = clf.decision_function(X)  # outlier scores\n",
        "print(y_test_pred)\n",
        "print(y_test_scores)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tg6t2Dz12Cou",
        "outputId": "3d0e443b-e8fb-47bc-bd91-6a3ce283e623"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            " 1.26553658 0.97806061 1.01648758 1.00747184 1.07024115 0.97482615\n",
            " 0.98804786 0.96692183 0.99952572 1.01602676 1.01227713 1.01925442\n",
            " 1.08870348 0.98806731 0.9797054  0.9863409  0.99912542 1.01594719\n",
            " 0.98299709 0.99634557 0.96978223 0.97229158 0.98023873 0.98727781\n",
            " 0.98911956 0.99392137 0.96505442 0.97210632 0.98639876 1.03447462\n",
            " 1.0178044  1.00528359 0.99027897 0.99729134 0.97360504 1.0245901\n",
            " 1.00260758 0.97857038 0.97737357 1.0303607  1.01679608 1.00221394\n",
            " 0.99818215 1.04237509 1.03549648 0.97858486 0.99536834 0.97636465\n",
            " 1.02481784 0.98043106 1.0019055  1.03145957 0.98536353 0.98868979\n",
            " 0.99803684 1.09894365 1.01140405 0.97265914 1.01636271 0.99120569\n",
            " 1.02024103 1.01109157 1.02472506 0.99981185 0.9654671  0.98143016\n",
            " 0.98033005 0.98543416 0.99856336 0.95213823 1.0086889  1.01805016\n",
            " 1.20833502 1.0132391  1.0101865  0.99019073 0.98960291 0.9657398\n",
            " 1.05513582 0.98013589 1.21041239 1.03726647 1.00895316 1.02951191\n",
            " 1.21843776 0.9754145  0.998901   1.01787692 1.17676165 1.06840507\n",
            " 0.96101665 0.98486063 0.98631788 0.99996192 1.00413709 0.97486606\n",
            " 0.98863022 0.99503037 1.02174792 1.00468184 0.97167669 1.00558547\n",
            " 1.01679168 1.04592434 0.99531245 0.95476309 0.99981802 1.00018406\n",
            " 0.97827061 0.99025704 1.04288778 1.22485013 0.94244637 1.00301021\n",
            " 0.98621339 0.98181541 1.00513914 1.01800487 1.0236794  1.03512417\n",
            " 1.02772521 1.00171133 0.99743007 1.00382742 0.98239132 1.01040852\n",
            " 1.04290585 0.99578209 1.0249218  0.99004501 1.16898019 1.00748378\n",
            " 1.08926134 0.97787033 1.00815232 0.97816507 0.99562014 1.01767232\n",
            " 0.98263505 1.00152762 1.06064537 1.00748095 1.05481448 0.98785305\n",
            " 1.00621374 1.00278422 0.99266268 1.0128643  1.02142246 0.98535532\n",
            " 0.97271013 0.99892635 1.03120781 1.00055003 1.01342701 0.97442656\n",
            " 0.99938635 1.03269909 1.03228142 1.03031124 1.02138989 1.00635618\n",
            " 0.99507817 0.98479305 1.1835968  1.03633818 0.99999428 0.98618556\n",
            " 0.9777833  1.02839549 1.03385438 0.96765109 1.28062204 0.99401061\n",
            " 1.04910021 1.0115003  0.94490289 0.97928133 0.98916506 1.00646983\n",
            " 1.00916782 0.98296471 0.98250588 1.01401421 0.99736441 0.97077581\n",
            " 1.03293877 1.03915155 1.02058638 1.05791321 1.01061729 1.01736821\n",
            " 1.03485149 1.03061786 0.96853243 1.00865167 0.97669821 1.01826073\n",
            " 1.01645492 0.96742615 1.00336465 0.98733484 1.0071006  0.97564559\n",
            " 0.97950741 1.01560156 0.99385795 1.0146322  1.1794955  1.00765665\n",
            " 1.19844404 1.02080708 1.01201979 1.0114688  1.04048783 1.02102662\n",
            " 1.00357522 1.03922859 1.01223113 1.01366289 0.99995805 1.03228349\n",
            " 1.02824854 0.99563516 0.99384893 0.99889452 1.00590503 1.02611517\n",
            " 1.01155011 0.98852922 1.01313628 1.04848514 1.01023176 0.9775511\n",
            " 1.07126508 1.01226023 1.00579588 0.98271085 0.98889282 1.24419034\n",
            " 0.97442656 1.02201781 1.01720759 0.99707058 1.07753865 0.95212318\n",
            " 1.05394151 1.0371577  0.99685437 1.02957742 1.02471916 0.98808767\n",
            " 1.05514149 0.9797054  0.97880224 1.13244496 0.96652884 1.00578803\n",
            " 0.98003663 0.99429089 1.03189395 0.98914114 0.99021113 1.01018877\n",
            " 1.04597482 1.01670185 0.9832324  1.01899061 0.97732494 0.9939963\n",
            " 0.99070296 1.0150162  1.03581274 1.00818455 0.97221486 0.98245117\n",
            " 1.02301477 0.99876504 1.03167928 1.02025841 1.04755885 1.01214132\n",
            " 1.19505264 1.00715297 1.00031057 0.96350785 1.07420913 0.98137396\n",
            " 0.97647255 1.03964012 1.04206405 0.99277642 1.00856257 1.01746665\n",
            " 1.07021145 0.98838295 0.98652171 1.03502906 0.99959639 0.981211\n",
            " 1.01789747 0.97811975 1.05210167 1.04983783 1.00513931 1.11784814\n",
            " 0.99425728 0.98250588 0.95394338 0.98241856 0.98789231 1.04374661\n",
            " 1.04029446 0.98019227 0.97752922 1.305071   0.98894629 1.00050159\n",
            " 1.03285742 0.97046956 1.00714199 1.03542882 1.06561936 0.97662672\n",
            " 1.01912619 1.0303055  0.98640787 0.98781098 0.99233551 0.97767336\n",
            " 0.97959464 1.00467727 0.99197083 1.02177942 1.01804999 1.0053172\n",
            " 1.0290376  1.02026683 1.00234812 0.99474512 1.01284107 0.95203377\n",
            " 1.03781616 0.99010783 1.0336455  0.97136817 1.17076832 0.98944843\n",
            " 0.99353919 1.01491881 1.06615493 1.10806581 0.97146995 1.01930302\n",
            " 1.01652995 1.00998305]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/geopandas/geodataframe.py:1351: 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",
            "  super().__setitem__(key, value)\n",
            "/usr/local/lib/python3.7/dist-packages/geopandas/geodataframe.py:1351: 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",
            "  super().__setitem__(key, value)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#compare all model options"
      ],
      "metadata": {
        "id": "efRvRZ0oQphp"
      }
    }
  ]
}