anomaly_class.ipynb

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    "colab": {
      "name": "anomaly_class.ipynb",
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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/Sankarlalr23/Automotive_Telematics_Analytics/blob/main/anomaly_class.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "hTv6C29fpl0S"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "from sklearn import preprocessing\n",
        "plt.rcParams.update({'font.size': 14})\n",
        "\n",
        "from collections import Counter"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iMh1HdZapl0Y",
        "outputId": "ede9cc01-52bb-43e5-ec6d-d79d5f56d1dd"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive/')\n",
        " \n",
        "train_path='/content/drive/My Drive/Xen.AI'\n",
        "\n",
        "data_all = pd.read_csv('/content/drive/My Drive/Xen.AI/telematics_data.csv')   # data importing"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 398
        },
        "id": "BDLFYQ5Erafj",
        "outputId": "0fb76698-49f1-4296-ff3b-e45a23e43c47"
      },
      "source": [
        "data_all.head()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>day</th>\n",
              "      <th>vin</th>\n",
              "      <th>wmi</th>\n",
              "      <th>engine_cd</th>\n",
              "      <th>device_type</th>\n",
              "      <th>msg_ts</th>\n",
              "      <th>vin_utc_key</th>\n",
              "      <th>packet_id</th>\n",
              "      <th>reason_code</th>\n",
              "      <th>script_version</th>\n",
              "      <th>deviceid</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>driveridcode1</th>\n",
              "      <th>obdvin</th>\n",
              "      <th>vehiclebattvolts</th>\n",
              "      <th>fuel_total</th>\n",
              "      <th>engine_rpm</th>\n",
              "      <th>coolant_tmp</th>\n",
              "      <th>fuel_lvl_pct</th>\n",
              "      <th>driving_tme_sec</th>\n",
              "      <th>cruise_tme_sec</th>\n",
              "      <th>idle_tme_sec</th>\n",
              "      <th>idle_fuel_total</th>\n",
              "      <th>pto_tme_total</th>\n",
              "      <th>pto_fuel_total</th>\n",
              "      <th>gear</th>\n",
              "      <th>fuel_tmp</th>\n",
              "      <th>oil_tmpl</th>\n",
              "      <th>throt_pos</th>\n",
              "      <th>mpg</th>\n",
              "      <th>acel_pos</th>\n",
              "      <th>engine_pct_load</th>\n",
              "      <th>torque</th>\n",
              "      <th>oil_lvl</th>\n",
              "      <th>oil_psi</th>\n",
              "      <th>coolant_psi</th>\n",
              "      <th>intak_air_tmp</th>\n",
              "      <th>manifold_tmp</th>\n",
              "      <th>vehiclespeed</th>\n",
              "      <th>heading</th>\n",
              "      <th>odometerinmeters</th>\n",
              "      <th>odometer</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020</td>\n",
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              "      <td>Xirgo</td>\n",
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              "      <td>2020</td>\n",
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              "      <td>41.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>25.0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>2709.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020</td>\n",
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              "      <td>TZ</td>\n",
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              "      <td>2020-05-05 06:17:36</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>504.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>88.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>65.0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>320.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1090.0</td>\n",
              "      <td>458.0</td>\n",
              "      <td>107975440.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   year  month  day  ... heading odometerinmeters  odometer\n",
              "0  2020      4   18  ...   960.0      380663000.0  380663.0\n",
              "1  2020      5   20  ...   700.0       88781990.0       NaN\n",
              "2  2020      4    1  ...  2709.0      416436440.0       NaN\n",
              "3  2020      5    5  ...   838.0      426460520.0       NaN\n",
              "4  2020      4    7  ...   458.0      107975440.0       NaN\n",
              "\n",
              "[5 rows x 45 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9qeeWkytsZxP",
        "outputId": "0f2111d6-addc-4df4-cf46-495e14d023ae"
      },
      "source": [
        "data_all['month'].value_counts()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4    299339\n",
              "5    215655\n",
              "Name: month, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JkkiGTgAr_tI"
      },
      "source": [
        "data_all['utctime'] = data_all['msg_ts']"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eQHun8nppl0a"
      },
      "source": [
        "## Anomaly class"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KAFtEeXuroyo",
        "outputId": "eae30aef-fca4-42d0-b334-8ded8b12f091"
      },
      "source": [
        "!pip install pyod\n",
        "\n",
        "!pip install --upgrade pyod  # to make sure that the latest version is installed!"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting pyod\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/81/dd/1cef53031ad8926df628e78d7cc9d57bb2aee62bc2f66215b2bef4deae64/pyod-0.8.9.tar.gz (104kB)\n",
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            "\u001b[?25hRequirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from pyod) (1.0.1)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from pyod) (3.2.2)\n",
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            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->pyod) (0.10.0)\n",
            "Requirement already satisfied: llvmlite<0.35,>=0.34.0.dev0 in /usr/local/lib/python3.7/dist-packages (from numba>=0.35->pyod) (0.34.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from numba>=0.35->pyod) (57.0.0)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.25->pyod) (2018.9)\n",
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            "Building wheels for collected packages: pyod\n",
            "  Building wheel for pyod (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyod: filename=pyod-0.8.9-cp37-none-any.whl size=121413 sha256=a8e888befdd72826c1d355324f4c03450f36074120bcada628705830400e0757\n",
            "  Stored in directory: /root/.cache/pip/wheels/9b/31/18/f12932b880f1ce4c181cbe51454ddaaa02665ad7cfc663f639\n",
            "Successfully built pyod\n",
            "Installing collected packages: pyod\n",
            "Successfully installed pyod-0.8.9\n",
            "Requirement already up-to-date: pyod in /usr/local/lib/python3.7/dist-packages (0.8.9)\n",
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            "Requirement already satisfied, skipping upgrade: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->pyod) (2.8.1)\n",
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            "Requirement already satisfied, skipping upgrade: llvmlite<0.35,>=0.34.0.dev0 in /usr/local/lib/python3.7/dist-packages (from numba>=0.35->pyod) (0.34.0)\n",
            "Requirement already satisfied, skipping upgrade: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.25->pyod) (2018.9)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4aW-cj2Ypl0b"
      },
      "source": [
        "from pyod.models.knn import KNN\n",
        "from pyod.models.iforest import IForest\n",
        "from pyod.models.loda import LODA\n",
        "\n",
        "def get_threshold(scores, n_std=2):\n",
        "    return np.mean(scores) + n_std*np.std(scores)\n",
        "    \n",
        "class Anomaly(object):\n",
        "    def __init__(self, variables, data):\n",
        "        self.variables = variables\n",
        "        self.data_train = data\n",
        "        \n",
        "        #Initialize scaler\n",
        "        self.scaler = preprocessing.MinMaxScaler()\n",
        "        \n",
        "        #Algorithm initializations\n",
        "        self.knn = KNN(n_neighbors=10) \n",
        "        self.iforest = IForest()\n",
        "        self.loda = LODA(n_bins=20, n_random_cuts=200)\n",
        "        \n",
        "        #Anomaly thresholds\n",
        "        self.knn_thr = -1000.\n",
        "        self.iforest_thr = -1000.\n",
        "        self.loda_thr = -1000.\n",
        "        \n",
        "    def preprocess(self, data, train=False):\n",
        "        # drop NANs\n",
        "        dataset = data.drop(['vin','utctime'], axis=1)[self.variables].dropna()\n",
        "        #dataset = data.dropna(subset=[self.variables])\n",
        "        #vins = dataset.vin.values\n",
        "        # scale data\n",
        "        if train:\n",
        "            data_scaled = self.scaler.fit_transform(dataset[self.variables])\n",
        "        else:\n",
        "            data_scaled = self.scaler.transform(dataset[self.variables])\n",
        "        X = pd.DataFrame(data_scaled, columns=dataset.columns,index=dataset.index)\n",
        "        \n",
        "        return X\n",
        "        \n",
        "    def train(self):\n",
        "        self.X_train = self.preprocess(self.data_train, True)\n",
        "        self.knn.fit(self.X_train)\n",
        "        self.iforest.fit(self.X_train)\n",
        "        self.loda.fit(self.X_train)\n",
        "\n",
        "    def calc_scores(self, data_test, train=False):\n",
        "        \n",
        "        if train:\n",
        "            X_test = self.X_train\n",
        "        else:\n",
        "            X_test = self.preprocess(data_test, train)\n",
        "        \n",
        "        #calculate algorithm scores:\n",
        "        knn_scores = self.knn.decision_function(X_test)\n",
        "        iforest_scores = self.iforest.decision_function(X_test)\n",
        "        loda_scores = self.loda.decision_function(X_test)\n",
        "        \n",
        "        #calculate threshold\n",
        "        if train:\n",
        "            self.knn_thr = get_threshold(knn_scores)\n",
        "            self.iforest_thr = get_threshold(iforest_scores)\n",
        "            self.loda_thr = get_threshold(loda_scores)\n",
        "    \n",
        "        #determine anomaly\n",
        "        if self.knn_thr < -999:\n",
        "            print('\\nWarning: KNN anomaly score is not determined yet!\\n')\n",
        "        \n",
        "        X_test.loc[:,'as_knn'] = knn_scores\n",
        "        X_test.loc[:,'as_iforest'] = iforest_scores\n",
        "        X_test.loc[:,'as_loda'] = loda_scores\n",
        "        X_test['anomaly_knn'] = X_test['as_knn'] > self.knn_thr\n",
        "        X_test['anomaly_iforest'] = X_test['as_iforest'] > self.iforest_thr\n",
        "        X_test['anomaly_loda'] = X_test['as_loda'] > self.loda_thr\n",
        "        \n",
        "        X_test.loc[:,'vin'] = data_test.vin.values\n",
        "        \n",
        "        return X_test\n",
        "    \n",
        "    def get_anomaly_frac(self, df, daily=True, plot=False, nmax=20):\n",
        "        df['day']  = [str(x)[:10] for x in df.index]\n",
        "        df['hour'] = [str(x)[:13] for x in df.index]\n",
        "\n",
        "        agg = 'day'\n",
        "        if not daily:\n",
        "            agg = 'hour'\n",
        "            \n",
        "        def get_f(algo, anomaly):\n",
        "            t = df.groupby(['vin',agg]).apply(lambda tmp: len(tmp.query(anomaly)) / len(tmp))\n",
        "            return t.to_frame().reset_index().rename(columns={0:f'frac_{algo}'})\n",
        "        t1 = get_f('KNN','anomaly_knn')\n",
        "        t2 = get_f('IForest','anomaly_iforest')\n",
        "        t3 = get_f('LODA','anomaly_loda')\n",
        "        t = pd.merge(t1,t2, on=['vin',agg])\n",
        "        t = pd.merge(t,t3, on=['vin',agg])\n",
        "        \n",
        "        return t\n"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YbRyMgD-pl0e"
      },
      "source": [
        "v = ['fuel_total', 'engine_rpm', 'coolant_tmp', 'oil_tmpl','throt_pos', 'torque', 'oil_psi', 'manifold_tmp', 'vehiclespeed',\n",
        "       'odometerinmeters']\n",
        "\n",
        "data_all.index = pd.to_datetime(data_all.utctime, format='%Y-%m-%d %H:%M:%S')\n",
        "data_all = data_all.sort_index()\n",
        "\n",
        "data_all_sel = data_all.dropna(subset=v)\n",
        "\n",
        "#data_train = data_all['2021-02-01 00:00:00':'2021-02-25 00:00:01']\n",
        "\n",
        "data_train = data_all_sel['2020-04-01 00:00:00':'2020-05-20 00:00:01']\n",
        "data_test = data_all_sel['2020-05-20 00:00:01':]"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 429
        },
        "id": "Gry_I7w_pl0f",
        "outputId": "34d005ed-4c12-46b3-fe5a-106222947d95"
      },
      "source": [
        "data_train.head()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>day</th>\n",
              "      <th>vin</th>\n",
              "      <th>wmi</th>\n",
              "      <th>engine_cd</th>\n",
              "      <th>device_type</th>\n",
              "      <th>msg_ts</th>\n",
              "      <th>vin_utc_key</th>\n",
              "      <th>packet_id</th>\n",
              "      <th>reason_code</th>\n",
              "      <th>script_version</th>\n",
              "      <th>deviceid</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>driveridcode1</th>\n",
              "      <th>obdvin</th>\n",
              "      <th>vehiclebattvolts</th>\n",
              "      <th>fuel_total</th>\n",
              "      <th>engine_rpm</th>\n",
              "      <th>coolant_tmp</th>\n",
              "      <th>fuel_lvl_pct</th>\n",
              "      <th>driving_tme_sec</th>\n",
              "      <th>cruise_tme_sec</th>\n",
              "      <th>idle_tme_sec</th>\n",
              "      <th>idle_fuel_total</th>\n",
              "      <th>pto_tme_total</th>\n",
              "      <th>pto_fuel_total</th>\n",
              "      <th>gear</th>\n",
              "      <th>fuel_tmp</th>\n",
              "      <th>oil_tmpl</th>\n",
              "      <th>throt_pos</th>\n",
              "      <th>mpg</th>\n",
              "      <th>acel_pos</th>\n",
              "      <th>engine_pct_load</th>\n",
              "      <th>torque</th>\n",
              "      <th>oil_lvl</th>\n",
              "      <th>oil_psi</th>\n",
              "      <th>coolant_psi</th>\n",
              "      <th>intak_air_tmp</th>\n",
              "      <th>manifold_tmp</th>\n",
              "      <th>vehiclespeed</th>\n",
              "      <th>heading</th>\n",
              "      <th>odometerinmeters</th>\n",
              "      <th>odometer</th>\n",
              "      <th>utctime</th>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "    <tr>\n",
              "      <th>2020-04-01 00:00:07</th>\n",
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              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>83.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>388.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>1094.0</td>\n",
              "      <td>1834.0</td>\n",
              "      <td>284931580.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2020-04-01 00:01:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-01 00:02:21</th>\n",
              "      <td>2020</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>3HSDZTZR5LN134290</td>\n",
              "      <td>3HS</td>\n",
              "      <td>TZ</td>\n",
              "      <td>Xirgo</td>\n",
              "      <td>2020-04-01 00:02:21</td>\n",
              "      <td>3HSDZTZR5LN1342902020-04-01 00:02:21</td>\n",
              "      <td>36</td>\n",
              "      <td>9</td>\n",
              "      <td>204.0</td>\n",
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              "      <td>-114.781823</td>\n",
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              "      <td>380.0</td>\n",
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              "      <td>1038.0</td>\n",
              "      <td>894.0</td>\n",
              "      <td>265636940.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2020-04-01 00:02:21</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     year  month  ...  odometer              utctime\n",
              "utctime                           ...                               \n",
              "2020-04-01 00:00:07  2020      4  ...       NaN  2020-04-01 00:00:07\n",
              "2020-04-01 00:00:19  2020      4  ...       NaN  2020-04-01 00:00:19\n",
              "2020-04-01 00:00:32  2020      4  ...       NaN  2020-04-01 00:00:32\n",
              "2020-04-01 00:01:06  2020      4  ...       NaN  2020-04-01 00:01:06\n",
              "2020-04-01 00:02:21  2020      4  ...       NaN  2020-04-01 00:02:21\n",
              "\n",
              "[5 rows x 46 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D0ZA5iiOpl0h"
      },
      "source": [
        "#instantiate\n",
        "a = Anomaly(v, data_train)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "9nzBEvrmpl0h"
      },
      "source": [
        "#train algos\n",
        "a.train()"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_wOBLq-Hpl0i"
      },
      "source": [
        "#calc scores for train\n",
        "X_train = a.calc_scores(data_train, True)"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 429
        },
        "id": "v4vzlvtNpl0j",
        "outputId": "5154c75a-50de-42bb-d64a-0ddf5f325a6b"
      },
      "source": [
        "X_train.head()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>fuel_total</th>\n",
              "      <th>engine_rpm</th>\n",
              "      <th>coolant_tmp</th>\n",
              "      <th>oil_tmpl</th>\n",
              "      <th>throt_pos</th>\n",
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              "      <th>as_knn</th>\n",
              "      <th>as_iforest</th>\n",
              "      <th>as_loda</th>\n",
              "      <th>anomaly_knn</th>\n",
              "      <th>anomaly_iforest</th>\n",
              "      <th>anomaly_loda</th>\n",
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              "    <tr>\n",
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              "      <th>2020-04-01 00:00:19</th>\n",
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              "    <tr>\n",
              "      <th>2020-04-01 00:00:32</th>\n",
              "      <td>0.246470</td>\n",
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              "      <th>2020-04-01 00:01:06</th>\n",
              "      <td>0.079304</td>\n",
              "      <td>0.118712</td>\n",
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            "text/plain": [
              "                     fuel_total  engine_rpm  ...  anomaly_loda                vin\n",
              "utctime                                      ...                                 \n",
              "2020-04-01 00:00:07    0.427126    0.068537  ...         False  3HSDZTZR4KN126504\n",
              "2020-04-01 00:00:19    0.333417    0.133325  ...         False  3HSDZTZR5LN134290\n",
              "2020-04-01 00:00:32    0.246470    0.018234  ...         False  3HSDWTZR7LN064378\n",
              "2020-04-01 00:01:06    0.079304    0.118712  ...         False  3HSDWTZR5LN844783\n",
              "2020-04-01 00:02:21    0.333417    0.129833  ...         False  3HSDZTZR5LN134290\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mAW9-cwEpl0k",
        "outputId": "10cf1ed2-0ea8-48f4-a19e-f5aca78b3284"
      },
      "source": [
        "a.knn_thr, a.iforest_thr, a.loda_thr"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.11722372170473133, 0.03697761977021241, 0.01638862258459863)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f-7cTgJRpl0l"
      },
      "source": [
        "#calc scores for test sample\n",
        "X_test = a.calc_scores(data_test)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1pyhS_bupl0m"
      },
      "source": [
        "#get anomaly frac on test sample\n",
        "xt_frac = a.get_anomaly_frac(X_test, True)"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 205
        },
        "id": "JCTYVQy1pl0n",
        "outputId": "f3644430-8b77-4c62-f2cf-8b53f5616cb0"
      },
      "source": [
        "xt_frac.head()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>vin</th>\n",
              "      <th>day</th>\n",
              "      <th>frac_KNN</th>\n",
              "      <th>frac_IForest</th>\n",
              "      <th>frac_LODA</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1HSERTZRXLH809470</td>\n",
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              "      <td>0.000000</td>\n",
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              "    <tr>\n",
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              "      <td>1HSERTZRXLH809470</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1HSERTZRXLH809470</td>\n",
              "      <td>2020-05-23</td>\n",
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              "      <td>0.190476</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1HSERTZRXLH809470</td>\n",
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              "      <td>0.523810</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 vin         day  frac_KNN  frac_IForest  frac_LODA\n",
              "0  1HSERTZRXLH809470  2020-05-20  0.000000      0.000000   0.000000\n",
              "1  1HSERTZRXLH809470  2020-05-21  0.000000      0.000000   0.000000\n",
              "2  1HSERTZRXLH809470  2020-05-22  0.000000      0.098361   0.098361\n",
              "3  1HSERTZRXLH809470  2020-05-23  0.119048      0.119048   0.190476\n",
              "4  1HSERTZRXLH809470  2020-05-24  0.285714      0.476190   0.523810"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "dglaEGbGpl0o",
        "outputId": "00ed3277-cc72-4701-accc-605ba04c6d8c"
      },
      "source": [
        "n = 0\n",
        "nmax = 5\n",
        "for vin, tv in xt_frac.groupby('vin'):\n",
        "    if n < nmax and len(tv)>1:\n",
        "        tv.index = tv.day\n",
        "        tv[['frac_LODA','frac_KNN','frac_IForest']].plot(subplots=False, legend=True, logy=False, figsize = (12,3), \n",
        "                                ylim=(0,1.1),title=vin)\n",
        "    n += 1"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nRpUEcVdpl0q"
      },
      "source": [
        ""
      ],
      "execution_count": 18,
      "outputs": []
    }
  ]
}