Jyoti_Unlabeled_data_outliers.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Jyoti_Unlabeled_data_outliers.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/nrjcs/iitpbse/blob/master/Jyoti_Unlabeled_data_outliers.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-4elXIvV1aHM",
        "colab_type": "code",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "outputId": "09e09634-695d-4f30-80e5-ff4405ec03ed"
      },
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-badf248f-fea9-41ec-b7c5-1586cb7b6775\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-badf248f-fea9-41ec-b7c5-1586cb7b6775\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-84-21dc3c638f66>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mgoogle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolab\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mfiles\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0muploaded\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfiles\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/google/colab/files.py\u001b[0m in \u001b[0;36mupload\u001b[0;34m()\u001b[0m\n\u001b[1;32m     62\u001b[0m   result = _output.eval_js(\n\u001b[1;32m     63\u001b[0m       'google.colab._files._uploadFiles(\"{input_id}\", \"{output_id}\")'.format(\n\u001b[0;32m---> 64\u001b[0;31m           input_id=input_id, output_id=output_id))\n\u001b[0m\u001b[1;32m     65\u001b[0m   \u001b[0mfiles\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_collections\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdefaultdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_six\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbinary_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m   \u001b[0;31m# Mapping from original filename to filename as saved locally.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/google/colab/output/_js.py\u001b[0m in \u001b[0;36meval_js\u001b[0;34m(script, ignore_result)\u001b[0m\n\u001b[1;32m     37\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mignore_result\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m     \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0m_message\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_reply_from_input\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest_id\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/google/colab/_message.py\u001b[0m in \u001b[0;36mread_reply_from_input\u001b[0;34m(message_id, timeout_sec)\u001b[0m\n\u001b[1;32m     99\u001b[0m     \u001b[0mreply\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_read_next_input_message\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    100\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mreply\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_NOT_READY\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreply\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 101\u001b[0;31m       \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.025\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    102\u001b[0m       \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m     if (reply.get('type') == 'colab_reply' and\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xo1vvgGP2Isc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "#https://pandas.pydata.org/docs/reference/index.html\n",
        "\n",
        "import numpy as np\n",
        "#https://numpy.org/doc/stable/user/tutorials_index.html\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "import matplotlib.font_manager\n",
        "#https://matplotlib.org/tutorials/index.html\n",
        "\n",
        "from sklearn import preprocessing\n",
        "from sklearn.svm import OneClassSVM\n",
        "from sklearn import svm\n",
        "#https://scikit-learn.org/stable/user_guide.html\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rAKiM1JYJHE4",
        "colab_type": "text"
      },
      "source": [
        "# Read Data "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LJ_ki447I_ly",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.read_csv(\"ambient_temperature_system_failure.txt\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G7X-EO3cJgzL",
        "colab_type": "text"
      },
      "source": [
        "**#Understand data**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7ngvJvr4JcKL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 199
        },
        "outputId": "9b508107-98ed-4ebd-f8f3-5ee610b80ad6"
      },
      "source": [
        "print(df.info())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 7267 entries, 0 to 7266\n",
            "Data columns (total 2 columns):\n",
            " #   Column     Non-Null Count  Dtype  \n",
            "---  ------     --------------  -----  \n",
            " 0   timestamp  7267 non-null   object \n",
            " 1   value      7267 non-null   float64\n",
            "dtypes: float64(1), object(1)\n",
            "memory usage: 113.7+ KB\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qaZbGQZ1hVwl",
        "colab_type": "text"
      },
      "source": [
        "**# First 10 rows of data.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hh3SePkJJfV5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "5df9f92c-6385-4927-e814-215ee01230f2"
      },
      "source": [
        "print(df.head(10))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "             timestamp      value\n",
            "0  2013-07-04 00:00:00  69.880835\n",
            "1  2013-07-04 01:00:00  71.220227\n",
            "2  2013-07-04 02:00:00  70.877805\n",
            "3  2013-07-04 03:00:00  68.959400\n",
            "4  2013-07-04 04:00:00  69.283551\n",
            "5  2013-07-04 05:00:00  70.060966\n",
            "6  2013-07-04 06:00:00  69.279765\n",
            "7  2013-07-04 07:00:00  69.369608\n",
            "8  2013-07-04 08:00:00  69.166714\n",
            "9  2013-07-04 09:00:00  68.986083\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zBuu300NKRM1",
        "colab_type": "text"
      },
      "source": [
        " - Change the type of timestamp\n",
        " - Convert the temperature value (Fahrenheit to Celsius) "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ILr2CXssJwoh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "outputId": "9d6e1542-a6db-4608-9071-3597fe1a5b87"
      },
      "source": [
        "df['timestamp'] = pd.to_datetime(df['timestamp'])\n",
        "df['value'] = (df['value'] - 32) * 5/9\n",
        "df.plot(x='timestamp', y='value')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8a6549a4a8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PTrYNkeVMQB8",
        "colab_type": "text"
      },
      "source": [
        "# Extract useful features\n",
        "\n",
        "Extracting the following useful features :\n",
        "\n",
        "**1. Hours** - 24 hours (0-23) \\\n",
        "\n",
        "**2. Daytime** - \\\n",
        " Daytime = 1 , if hour is in 7:00 AM to 10:00 PM \\\n",
        " Daytime = 0 , otherwise (Night_time) \\\n",
        "\n",
        "**3. DayOfTheWeek** -  ( Monday - 0 )  to (Sunday - 6) \\\n",
        "\n",
        "**4. WeekDay** - \\\n",
        "WeekDay = 0 , if Day of week < 5 \\\n",
        "WeekDay = 1 , Otherwise (saturday and sunday)\n",
        "\n",
        "**5. categories** - \\\n",
        "0 - Week end night \\\n",
        "1 - Week end daytime \\\n",
        "2 - Week day night \\\n",
        "3 - Week day day time \\\n",
        "\n",
        "                                   \n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U5eYlHtBMNw1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['hours'] = df['timestamp'].dt.hour\n",
        "df['daylight'] = ((df['hours'] >= 7) & (df['hours'] <= 22)).astype(int)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lAsYmV1sMPB8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['DayOfTheWeek'] = df['timestamp'].dt.dayofweek\n",
        "df['WeekDay'] = (df['DayOfTheWeek'] < 5).astype(int)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TJfYYfndMwjL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['time_epoch'] = (df['timestamp'].astype(np.int64)/100000000000).astype(np.int64)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9lMcGAujNlMh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['categories'] = df['WeekDay']*2 + df['daylight']\n",
        "\n",
        "a = df.loc[df['categories'] == 0, 'value']\n",
        "b = df.loc[df['categories'] == 1, 'value']\n",
        "c = df.loc[df['categories'] == 2, 'value']\n",
        "d = df.loc[df['categories'] == 3, 'value']\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "un5EI5XCPWzc",
        "colab_type": "text"
      },
      "source": [
        "# Histogram representation of time series data:\n",
        "\n",
        " - Histogram is an accurate graphical representation of the distribution of numerical data. It takes one numerical variable as input and cut it into several bins.\n",
        "\n",
        " -**X axis** - Bins \\\n",
        " - **y axis** - frequency \\\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KsNRdiQUOV1a",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "0bf26b5b-150e-49db-f532-c1a18f0860bd"
      },
      "source": [
        "fig, ax = plt.subplots()\n",
        "a_heights, a_bins = np.histogram(a)\n",
        "b_heights, b_bins = np.histogram(b, bins=a_bins)\n",
        "c_heights, c_bins = np.histogram(c, bins=a_bins)\n",
        "d_heights, d_bins = np.histogram(d, bins=a_bins)\n",
        "\n",
        "width = (a_bins[1] - a_bins[0])/6   # width = renge/ #of intervals\n",
        "\n",
        "ax.bar(a_bins[:-1], a_heights*100/a.count(), width=width, facecolor='blue', label='WeekEnd_Night')\n",
        "ax.bar(b_bins[:-1]+width, (b_heights*100/b.count()), width=width, facecolor='green', label ='WeekEnd_Day')\n",
        "ax.bar(c_bins[:-1]+width*2, (c_heights*100/c.count()), width=width, facecolor='red', label ='WeekDay_Night')\n",
        "ax.bar(d_bins[:-1]+width*3, (d_heights*100/d.count()), width=width, facecolor='black', label ='WeekDay_Day')\n",
        "\n",
        "plt.legend()\n",
        "plt.show() "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UdDy-7YTU_yQ",
        "colab_type": "text"
      },
      "source": [
        "We can see here that the temperature is more stable during day time of week days."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mai9IZs7SE7e",
        "colab_type": "text"
      },
      "source": [
        "**Model to detect the rare events**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aAHfWGHDTrwH",
        "colab_type": "text"
      },
      "source": [
        "**1. OneClass SVM(Support vector machine)**\n",
        "\n",
        "\n",
        "- SVM use a hyperplane to separates the two classes with max margin.\n",
        "\n",
        "- One class SVM Unsupervised Outlier Detection method\n",
        "\n",
        "- One Class SVM is used for anomaly detection. The idea of anomaly detection is to detect the events that happen rarely.\n",
        "\n",
        "- One class SVM finds a hyperball in higher dimensional space, that contains only normal points."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AOo164DM_m_n",
        "colab_type": "text"
      },
      "source": [
        "**Example :**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1X4JaEcd72ku",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "1516f071-8854-43f6-9a5d-1beb4fcc8a78"
      },
      "source": [
        "xx, yy = np.meshgrid(np.linspace(-5, 5, 500), np.linspace(-5, 5, 500))\n",
        "# Generate train data\n",
        "X = 0.3 * np.random.randn(100, 2)\n",
        "X_train = np.r_[X + 2, X - 2]\n",
        "# Generate some abnormal observations\n",
        "X_outliers = np.random.uniform(low=-4, high=4, size=(20, 2))\n",
        "\n",
        "# fit the model\n",
        "clf = svm.OneClassSVM(nu=0.1)\n",
        "clf.fit(X_train)\n",
        "y_pred_train = clf.predict(X_train)\n",
        "y_pred_outliers = clf.predict(X_outliers)\n",
        "\n",
        "# plot the line, the points, and the nearest vectors to the plane\n",
        "Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])\n",
        "Z = Z.reshape(xx.shape)\n",
        "\n",
        "plt.title(\"Anomaly Detection\")\n",
        "plt.contourf(xx, yy, Z, levels=np.linspace(Z.min(), 0, 7), cmap=plt.cm.PuBu)\n",
        "a = plt.contour(xx, yy, Z, levels=[0], linewidths=2, colors='darkred')\n",
        "plt.contourf(xx, yy, Z, levels=[0, Z.max()], colors='palevioletred')\n",
        "\n",
        "s = 40\n",
        "b1 = plt.scatter(X_train[:, 0], X_train[:, 1], c='white', s=s, edgecolors='k')\n",
        "c = plt.scatter(X_outliers[:, 0], X_outliers[:, 1], c='gold', s=s,\n",
        "                edgecolors='k')\n",
        "plt.axis('tight')\n",
        "plt.xlim((-5, 5))\n",
        "plt.ylim((-5, 5))\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nbucIJSUOXDj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "3ef4c767-b78b-43b5-e997-e13214f87ada"
      },
      "source": [
        "outliers_fraction = 0.01\n",
        "data = df[['value', 'hours', 'daylight', 'DayOfTheWeek', 'WeekDay']]\n",
        "min_max_scaler = preprocessing.StandardScaler()\n",
        "np_scaled = min_max_scaler.fit_transform(data)    # Standardize features by removing the mean and scaling to unit variance\n",
        "# train one class SVM \n",
        "model =  OneClassSVM(nu=0.95 * outliers_fraction)\n",
        "data = pd.DataFrame(np_scaled)\n",
        "model.fit(data)\n",
        "# add the data to the main  \n",
        "df['anomaly'] = pd.Series(model.predict(data))\n",
        "df['anomaly'] = df['anomaly'].map( {1: 0, -1: 1} )\n",
        "print(df['anomaly'].value_counts())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0    7198\n",
            "1      69\n",
            "Name: anomaly, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZSGC5YszVbXv",
        "colab_type": "text"
      },
      "source": [
        "**visualize anomaly**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gW4f7dfpUhkN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "outputId": "a3f1eb0e-ed35-4141-84e4-174c88e7a56d"
      },
      "source": [
        "fig, ax = plt.subplots()\n",
        "\n",
        "a = df.loc[df['anomaly'] == 1, ['time_epoch', 'value']] #anomaly\n",
        "\n",
        "ax.plot(df['time_epoch'], df['value'], color='blue')\n",
        "ax.scatter(a['time_epoch'],a['value'], color='red')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wawWHhChVzP3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "02c6810c-e23a-4845-cddb-4123f9041e9d"
      },
      "source": [
        "a = df.loc[df['anomaly'] == 0, 'value']\n",
        "b = df.loc[df['anomaly'] == 1, 'value']\n",
        "\n",
        "fig, axs = plt.subplots()\n",
        "axs.hist([a,b], bins=32, stacked=True, color=['blue', 'red'], label=['normal', 'anomaly'])\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SfYY_LJz2NyD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.to_csv('data1.csv', index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RMobvOtqumWd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}