TRIST_AnomalyDetection(with_0_02_fraction).ipynb

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      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/carolsworld/ICS-test-bed-PoC/blob/anomalydetection/TRIST_AnomalyDetection(with_0_02_fraction).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "source": [
        "# **Demonstration of using datasets generated from TRIST for developing Anomaly Detection Model**"
      ],
      "metadata": {
        "id": "ac0Xf-fr9MyU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Getting Started\n",
        "Anomaly detection is a common technique used for identifying abnormal or rare observations that significantly different from the majority of the data in a dataset. This technique is applicable to detection of anomalous events happened on Internet of Things (IoT) and many other real-life problems.\n",
        "\n",
        "The purpose of this demonstration is to **validate whether an anomaly detection model trained based on datasets of normal operations could effectively identify anomalies on datasets generated during attacks**."
      ],
      "metadata": {
        "id": "9cd4DQLJ9QKb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Types of Anomaly Detection Models\n",
        " **Unsupervised model** is used in this demonstration, meaning that we do not have any predefined labels on the dataset for model training.\n",
        "\n",
        "The dataset generated from TRIST representing the **normal** operations of the water supply and storage is used for model training. In other words, the model is assuming that the majority of the instances are normal. This assumption is reasonable as we are generating data from python script without interference from physical environment.\n",
        "\n",
        "The datasets generated from TRIST that representing the attack simulations are used for prediction on unseen data. In this way, we could achieve our purpose by validating whether the trained model could effectively identify anomalies on datasets generated during attacks. If it is effective, the model could be deployed to the production environment.\n",
        "\n",
        "After data preparation, the following PyCaret workflow is used in our machine learning model development:\n",
        "* **1. Setup**\n",
        "* **2. Create Model**\n",
        "* **3. Assign Labels**\n",
        "* **4. Analyse Model**\n",
        "* **5. Save Trained Model**\n",
        "* **6. Load Trained Model**\n",
        "* **7. Predict Unseen Data**\n",
        "\n",
        "There are other types of anomaly detection model, one is \"supervised\", another one is \"semi-supervised\".\n",
        "* *Supervised:* Supervised model uses dataset that specifies which data records are normal, and which data records are anomaly. Thus, it requires collection of sufficient abnormal data to train a supervised anomaly detector.\n",
        "\n",
        "* *Semi-supervised:* Semi-supervised model uses only normal data during the training process. It predicts whether new data point is normal or abnormal based on the distribution of the data in the trained model.\n",
        "\n",
        "These aforesaid two types of model fall out of the scope of our demonstration."
      ],
      "metadata": {
        "id": "rAhoYwmR9UVN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Anomaly Detection Algorithm\n",
        "Data scientists have developed many anomaly detection algorithm over the past decades. For simplicity, we have used PyCaret, an open-source low-code machine learning library, to perform end-to-end machine learning model management."
      ],
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        "id": "qfAHajhY9gOF"
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m19.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m119.0/119.0 kB\u001b[0m \u001b[31m14.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for pyod (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "lida 0.0.10 requires fastapi, which is not installed.\n",
            "lida 0.0.10 requires python-multipart, which is not installed.\n",
            "lida 0.0.10 requires uvicorn, which is not installed.\u001b[0m\u001b[31m\n",
            "\u001b[0m  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for package-name (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "3.2.0\n"
          ]
        }
      ],
      "source": [
        "# Install the pyCaret library and check the version\n",
        "!pip install -q pycaret && pip install -q package-name\n",
        "from pycaret.utils import version\n",
        "print(version())"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Step 1: Data Preparation"
      ],
      "metadata": {
        "id": "yHPQ25d-N1qr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Upload a file from local machine to the Google Colab environment\n",
        "from google.colab import files\n",
        "import io\n",
        "import pandas as pd\n",
        "\n",
        "uploaded = files.upload()\n",
        "\n",
        "# Assuming you know the filename or if there's only one file uploaded\n",
        "filename = next(iter(uploaded))\n",
        "data = pd.read_csv(io.BytesIO(uploaded[filename]), parse_dates=['time'])\n",
        "data['time'] = data['time'].dt.strftime('%Y-%m-%d %H:%M:%S')\n"
      ],
      "metadata": {
        "id": "5PpoUsA-OijI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "outputId": "43b3c756-6c49-442f-d453-04a05c8e5a7c"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-c8aa97f6-66fa-4711-94f6-79a51b767210\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-c8aa97f6-66fa-4711-94f6-79a51b767210\">\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>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving normal_1hr.csv to normal_1hr.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the number of rows and columns in the dataframe\n",
        "print(\"The number of rows and columns: \", data.shape)"
      ],
      "metadata": {
        "id": "x6yNp7A5UF0w",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "07d54343-ed48-49dc-9041-c972ae5dc1c1"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The number of rows and columns:  (3595, 7)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Print the first 5 rows of the dataframe\n",
        "print(\"The first 5 rows of the dataset: \\n\", data.head())"
      ],
      "metadata": {
        "id": "MmLYaCksSS_e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "33a7c576-1454-4587-c029-8e7993d9ddb1"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The first 5 rows of the dataset: \n",
            "                   time  FIT101  MV101    LIT101  P101  FIT201    LIT301\n",
            "0  2023-07-17 11:00:00    2.55      1  0.597593     0     0.0  0.951852\n",
            "1  2023-07-17 11:00:01    2.55      1  0.644815     0     0.0  0.924074\n",
            "2  2023-07-17 11:00:02    2.55      1  0.692037     0     0.0  0.896296\n",
            "3  2023-07-17 11:00:03    2.55      1  0.739259     0     0.0  0.868519\n",
            "4  2023-07-17 11:00:04    2.55      1  0.786481     0     0.0  0.840741\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the time span of the data\n",
        "data['time'] = pd.to_datetime(data['time'])\n",
        "time_span = data.time.max() - data.time.min()\n",
        "print(\"Time span of the dataset:\", time_span)"
      ],
      "metadata": {
        "id": "4mXHzKYCSc4l",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d938dfcf-d839-4c66-c845-daae545c2b58"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time span of the dataset: 0 days 00:59:59\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot a graph to see how data looks like\n",
        "import plotly.express as px\n",
        "fig = px.line(data, x=\"time\", y=['FIT101', 'MV101', 'LIT101', 'P101', 'FIT201', 'LIT301'], title='TRIST normal dataset', template = 'plotly_dark')\n",
        "fig.show()"
      ],
      "metadata": {
        "id": "9-v8L-8MDLtF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "d47ef10f-b2c2-4b49-fc5b-51b248eede51"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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    {
      "cell_type": "markdown",
      "source": [
        "Explanation for the above graph:\n",
        "* FIT101 = Sensor 101 measuring water flow rate\n",
        "* MV101 = Motorised valve 101\n",
        "* LIT101 = Sensor 101 measuring water level at Tank 101\n",
        "* P101 = Pump 101\n",
        "* FIT201 = Sensor 201 measuring water flow rate\n",
        "* LIT301 = Sensor 301 measuring water level at Tank 301\n",
        "\n",
        "Please refer to [SWat Testbed description](https://minicps.readthedocs.io/en/latest/swat-tutorial.html?highlight=plc1#supply-and-storage-control) for details of Supply and Storage control in water treatment plant."
      ],
      "metadata": {
        "id": "8EUeddZzvMXQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Plotting all the records do not give us a clear picture of what is the data pattern of the simulation. Thus, the time feature is extracted for more accurate analysis."
      ],
      "metadata": {
        "id": "8KAYp1lPq4GK"
      }
    },
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      "cell_type": "code",
      "source": [
        "# Algorithms cannot directly consume date or timestamp data, thus we will extract the features from the timestamp\n",
        "# and will drop the actual timestamp column before training models.\n",
        "# Set the timestamp column as the index of the dataframe\n",
        "data.set_index('time', drop=True, inplace=True)"
      ],
      "metadata": {
        "id": "LVqtcrRsUyFH"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Extract features from timestamp\n",
        "data['hours'] = [i.hour for i in data.index]\n",
        "data['minutes'] = [i.minute for i in data.index]\n",
        "data['seconds']= [i.second for i in data.index]\n",
        "\n",
        "print(\"\\nThe first 5 rows of the dataset: \\n\", data.head())\n",
        "\n",
        "print(\"\\nThe last 5 rows of the dataset: \\n\", data.tail())"
      ],
      "metadata": {
        "id": "W_Si2IU0IAeo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d4fd3f09-435c-446d-b9e8-368300223b1f"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "The first 5 rows of the dataset: \n",
            "                      FIT101  MV101    LIT101  P101  FIT201    LIT301  hours  \\\n",
            "time                                                                          \n",
            "2023-07-17 11:00:00    2.55      1  0.597593     0     0.0  0.951852     11   \n",
            "2023-07-17 11:00:01    2.55      1  0.644815     0     0.0  0.924074     11   \n",
            "2023-07-17 11:00:02    2.55      1  0.692037     0     0.0  0.896296     11   \n",
            "2023-07-17 11:00:03    2.55      1  0.739259     0     0.0  0.868519     11   \n",
            "2023-07-17 11:00:04    2.55      1  0.786481     0     0.0  0.840741     11   \n",
            "\n",
            "                     minutes  seconds  \n",
            "time                                   \n",
            "2023-07-17 11:00:00        0        0  \n",
            "2023-07-17 11:00:01        0        1  \n",
            "2023-07-17 11:00:02        0        2  \n",
            "2023-07-17 11:00:03        0        3  \n",
            "2023-07-17 11:00:04        0        4  \n",
            "\n",
            "The last 5 rows of the dataset: \n",
            "                      FIT101  MV101    LIT101  P101  FIT201    LIT301  hours  \\\n",
            "time                                                                          \n",
            "2023-07-17 11:59:55    0.00      0  0.574444     1    2.45  0.873333     11   \n",
            "2023-07-17 11:59:56    0.00      0  0.529074     1    2.45  0.887407     11   \n",
            "2023-07-17 11:59:57    2.55      1  0.493148     1    2.45  0.905000     11   \n",
            "2023-07-17 11:59:58    2.55      1  0.495000     1    2.45  0.922593     11   \n",
            "2023-07-17 11:59:59    2.55      1  0.496852     1    2.45  0.940185     11   \n",
            "\n",
            "                     minutes  seconds  \n",
            "time                                   \n",
            "2023-07-17 11:59:55       59       55  \n",
            "2023-07-17 11:59:56       59       56  \n",
            "2023-07-17 11:59:57       59       57  \n",
            "2023-07-17 11:59:58       59       58  \n",
            "2023-07-17 11:59:59       59       59  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Simply listing the data record could not provide any insight. The following plots are used to analyse the data pattern of the TRIST dataset."
      ],
      "metadata": {
        "id": "_tL_3mMXrSUv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot a graph to see how the first 10 minutes of data looks like\n",
        "first_10_minutes = data.first('10T') # 'T' stands for minutes\n",
        "fig = px.line(first_10_minutes, y=['FIT101', 'MV101', 'LIT101', 'P101', 'FIT201', 'LIT301'], title='TRIST data', template = 'plotly_dark')\n",
        "fig.show()"
      ],
      "metadata": {
        "id": "hfK4_ERbp5wo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "61176b0b-750b-48c7-9917-3122b2ada220"
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      "execution_count": 9,
      "outputs": [
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      "cell_type": "markdown",
      "source": [
        "Explanation for the above and the following graph:\n",
        "* FIT101 = Sensor 101 measuring water flow rate\n",
        "* MV101 = Motorised valve 101\n",
        "* LIT101 = Sensor 101 measuring water level at Tank 101\n",
        "* P101 = Pump 101\n",
        "* FIT201 = Sensor 201 measuring water flow rate\n",
        "* LIT301 = Sensor 301 measuring water level at Tank 301\n",
        "\n",
        "Please refer to [SWat Testbed description](https://minicps.readthedocs.io/en/latest/swat-tutorial.html?highlight=plc1#supply-and-storage-control) for details of Supply and Storage control in water treatment plant."
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        "# Plot a graph to see how the first 1 minutes of data looks like\n",
        "first_1_minutes = data.first('1T') # 'T' stands for minutes\n",
        "fig = px.line(first_1_minutes, y=['FIT101', 'MV101', 'LIT101', 'P101', 'FIT201', 'LIT301'], title='TRIST data - first 1 minute of normal operation', template = 'plotly_dark')\n",
        "fig.show()"
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          "height": 542
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        "outputId": "cbad678d-70ca-42b7-e22c-825d3ba49e98"
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      "execution_count": 10,
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              "                            \n",
              "var gd = document.getElementById('624d1801-c498-4fa5-9454-8da1f162a8bb');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
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              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
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              "</body>\n",
              "</html>"
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Step 2: PyCaret Workflow"
      ],
      "metadata": {
        "id": "rQQ3gIzvIZ4a"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**1. Setup**"
      ],
      "metadata": {
        "id": "qLd6H20sa10F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Setup PyCaret\n",
        "# Session ID is added to provide a reproducible results.\n",
        "from pycaret.anomaly import *\n",
        "s = setup(data, session_id = 123)"
      ],
      "metadata": {
        "id": "Luokli7kI-my",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "outputId": "1f72be9e-4030-47fb-d856-5a5c4b46dbc8"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7d8fb7a32a40>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_6669c_row4_col1 {\n",
              "  background-color: lightgreen;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_6669c\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_6669c_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
              "      <th id=\"T_6669c_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_6669c_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_6669c_row0_col0\" class=\"data row0 col0\" >Session id</td>\n",
              "      <td id=\"T_6669c_row0_col1\" class=\"data row0 col1\" >123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_6669c_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_6669c_row1_col0\" class=\"data row1 col0\" >Original data shape</td>\n",
              "      <td id=\"T_6669c_row1_col1\" class=\"data row1 col1\" >(3595, 9)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_6669c_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_6669c_row2_col0\" class=\"data row2 col0\" >Transformed data shape</td>\n",
              "      <td id=\"T_6669c_row2_col1\" class=\"data row2 col1\" >(3595, 9)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_6669c_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_6669c_row3_col0\" class=\"data row3 col0\" >Numeric features</td>\n",
              "      <td id=\"T_6669c_row3_col1\" class=\"data row3 col1\" >9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_6669c_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_6669c_row4_col0\" class=\"data row4 col0\" >Preprocess</td>\n",
              "      <td id=\"T_6669c_row4_col1\" class=\"data row4 col1\" >True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_6669c_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_6669c_row5_col0\" class=\"data row5 col0\" >Imputation type</td>\n",
              "      <td id=\"T_6669c_row5_col1\" class=\"data row5 col1\" >simple</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_6669c_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_6669c_row6_col0\" class=\"data row6 col0\" >Numeric imputation</td>\n",
              "      <td id=\"T_6669c_row6_col1\" class=\"data row6 col1\" >mean</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_6669c_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_6669c_row7_col0\" class=\"data row7 col0\" >Categorical imputation</td>\n",
              "      <td id=\"T_6669c_row7_col1\" class=\"data row7 col1\" >mode</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_6669c_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_6669c_row8_col0\" class=\"data row8 col0\" >CPU Jobs</td>\n",
              "      <td id=\"T_6669c_row8_col1\" class=\"data row8 col1\" >-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_6669c_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_6669c_row9_col0\" class=\"data row9 col0\" >Use GPU</td>\n",
              "      <td id=\"T_6669c_row9_col1\" class=\"data row9 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_6669c_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
              "      <td id=\"T_6669c_row10_col0\" class=\"data row10 col0\" >Log Experiment</td>\n",
              "      <td id=\"T_6669c_row10_col1\" class=\"data row10 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_6669c_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
              "      <td id=\"T_6669c_row11_col0\" class=\"data row11 col0\" >Experiment Name</td>\n",
              "      <td id=\"T_6669c_row11_col1\" class=\"data row11 col1\" >anomaly-default-name</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_6669c_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
              "      <td id=\"T_6669c_row12_col0\" class=\"data row12 col0\" >USI</td>\n",
              "      <td id=\"T_6669c_row12_col1\" class=\"data row12 col1\" >37eb</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Find out the models available in the anomaly detection module of PyCaret\n",
        "models()"
      ],
      "metadata": {
        "id": "Sk8srtdOJmTt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "outputId": "d6cc9160-f426-4b4f-a529-d041c97b65af"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                        Name  \\\n",
              "ID                                             \n",
              "abod            Angle-base Outlier Detection   \n",
              "cluster       Clustering-Based Local Outlier   \n",
              "cof         Connectivity-Based Local Outlier   \n",
              "iforest                     Isolation Forest   \n",
              "histogram  Histogram-based Outlier Detection   \n",
              "knn             K-Nearest Neighbors Detector   \n",
              "lof                     Local Outlier Factor   \n",
              "svm                   One-class SVM detector   \n",
              "pca             Principal Component Analysis   \n",
              "mcd           Minimum Covariance Determinant   \n",
              "sod               Subspace Outlier Detection   \n",
              "sos             Stochastic Outlier Selection   \n",
              "\n",
              "                                                  Reference  \n",
              "ID                                                           \n",
              "abod                                  pyod.models.abod.ABOD  \n",
              "cluster    pycaret.internal.patches.pyod.CBLOFForceToDouble  \n",
              "cof                                     pyod.models.cof.COF  \n",
              "iforest                         pyod.models.iforest.IForest  \n",
              "histogram                             pyod.models.hbos.HBOS  \n",
              "knn                                     pyod.models.knn.KNN  \n",
              "lof                                     pyod.models.lof.LOF  \n",
              "svm                                 pyod.models.ocsvm.OCSVM  \n",
              "pca                                     pyod.models.pca.PCA  \n",
              "mcd                                     pyod.models.mcd.MCD  \n",
              "sod                                     pyod.models.sod.SOD  \n",
              "sos                                     pyod.models.sos.SOS  "
            ],
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              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Reference</th>\n",
              "    </tr>\n",
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              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>abod</th>\n",
              "      <td>Angle-base Outlier Detection</td>\n",
              "      <td>pyod.models.abod.ABOD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cluster</th>\n",
              "      <td>Clustering-Based Local Outlier</td>\n",
              "      <td>pycaret.internal.patches.pyod.CBLOFForceToDouble</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cof</th>\n",
              "      <td>Connectivity-Based Local Outlier</td>\n",
              "      <td>pyod.models.cof.COF</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>iforest</th>\n",
              "      <td>Isolation Forest</td>\n",
              "      <td>pyod.models.iforest.IForest</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>histogram</th>\n",
              "      <td>Histogram-based Outlier Detection</td>\n",
              "      <td>pyod.models.hbos.HBOS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>knn</th>\n",
              "      <td>K-Nearest Neighbors Detector</td>\n",
              "      <td>pyod.models.knn.KNN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lof</th>\n",
              "      <td>Local Outlier Factor</td>\n",
              "      <td>pyod.models.lof.LOF</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>svm</th>\n",
              "      <td>One-class SVM detector</td>\n",
              "      <td>pyod.models.ocsvm.OCSVM</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pca</th>\n",
              "      <td>Principal Component Analysis</td>\n",
              "      <td>pyod.models.pca.PCA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mcd</th>\n",
              "      <td>Minimum Covariance Determinant</td>\n",
              "      <td>pyod.models.mcd.MCD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sod</th>\n",
              "      <td>Subspace Outlier Detection</td>\n",
              "      <td>pyod.models.sod.SOD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sos</th>\n",
              "      <td>Stochastic Outlier Selection</td>\n",
              "      <td>pyod.models.sos.SOS</td>\n",
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              "\n",
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              "  .colab-df-spinner {\n",
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              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "    60% {\n",
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              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"models()\",\n  \"rows\": 12,\n  \"fields\": [\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [\n          \"Subspace Outlier Detection\",\n          \"Minimum Covariance Determinant\",\n          \"Angle-base Outlier Detection\"\n        ],\n        \"num_unique_values\": 12,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Reference\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [\n          \"pyod.models.sod.SOD\",\n          \"pyod.models.mcd.MCD\",\n          \"pyod.models.abod.ABOD\"\n        ],\n        \"num_unique_values\": 12,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**2. Create Model**"
      ],
      "metadata": {
        "id": "QcfBwK-kbG6X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Train model with Isolation Forest as an example for demonstration\n",
        "# Fraction = 0.02 (i.e. 2% of the population, 2 out of 100 records will be regarded as anomaly)\n",
        "# When the value of fraction increases, the number of anomaly records will be increased in proportion.\n",
        "# Therefore, this value shall be fine-tuned by the specific use case and the level of risk acceptance.\n",
        "iforest = create_model('iforest', fraction = 0.02)"
      ],
      "metadata": {
        "id": "DM7ls7rbKpTi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
            "4d3f636fd2434d36a85ffe1f96eb4d84",
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            "9d4f7c403cf549f68c9289778115e02b",
            "47a2582a1fad461fb7b5c6b69fa344cf",
            "6f12515b12cc4255b7e1e4f65465f03e",
            "046b39d303934636a6b4155013100618",
            "5461f485a5034d82b1f659ffd61cffe4",
            "c87956b57c2f41b69266d465fc3693fa",
            "c965bf2cd8f745ef997fd206788a6dae",
            "b127dc272bee4cf3ba6809bb0ffca8e9"
          ]
        },
        "outputId": "89dcfed7-f5c3-4c86-e112-6d295d1b8494"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Processing:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4d3f636fd2434d36a85ffe1f96eb4d84"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
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              }
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          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
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            "text/html": []
          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Find out more about the configurations of iforest\n",
        "# Containmination is same as the 'fraction' defined in the previous code\n",
        "print(iforest)"
      ],
      "metadata": {
        "id": "mNfzU0gLTWgr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "99fba250-3e4e-4be4-fbc1-227ba17ec9cd"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "IForest(behaviour='new', bootstrap=False, contamination=0.02,\n",
            "    max_features=1.0, max_samples='auto', n_estimators=100, n_jobs=-1,\n",
            "    random_state=123, verbose=0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**3. Assign Labels**"
      ],
      "metadata": {
        "id": "X3M0KnYibYBu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Two new columns are appended in the table:\n",
        "# Column \"Anomaly\" with value 1 refers to anomaly, value 0 refers to normal\n",
        "# Column \"Anomaly_Score\" provides the score calculated by the algorithm.\n",
        "iforest_results = assign_model(iforest)\n",
        "iforest_results.head()"
      ],
      "metadata": {
        "id": "B4Xlpe8ObPVU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "outputId": "8d0f334c-05c9-46a3-9ed6-5b8c9d4be850"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     FIT101  MV101    LIT101  P101  FIT201    LIT301  hours  \\\n",
              "time                                                                          \n",
              "2023-07-17 11:00:00    2.55      1  0.597593     0     0.0  0.951852     11   \n",
              "2023-07-17 11:00:01    2.55      1  0.644815     0     0.0  0.924074     11   \n",
              "2023-07-17 11:00:02    2.55      1  0.692037     0     0.0  0.896296     11   \n",
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              "                     minutes  seconds  Anomaly  Anomaly_Score  \n",
              "time                                                           \n",
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              "summary": "{\n  \"name\": \"iforest_results\",\n  \"rows\": 3595,\n  \"fields\": [\n    {\n      \"column\": \"FIT101\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"samples\": [\n          0.0,\n          2.549999952316284\n        ],\n        \"num_unique_values\": 2,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MV101\",\n      \"properties\": {\n        \"dtype\": \"int8\",\n        \"samples\": [\n          0,\n          1\n        ],\n        \"num_unique_values\": 2,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LIT101\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"samples\": [\n          0.5027777552604675,\n          0.5857407450675964\n        ],\n        \"num_unique_values\": 1457,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P101\",\n      \"properties\": {\n        \"dtype\": \"int8\",\n        \"samples\": [\n          1,\n          0\n        ],\n        \"num_unique_values\": 2,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FIT201\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"samples\": [\n          2.450000047683716,\n          0.0\n        ],\n        \"num_unique_values\": 2,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LIT301\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"samples\": [\n          0.8627777695655823,\n          0.7996296286582947\n        ],\n        \"num_unique_values\": 1231,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hours\",\n      \"properties\": {\n        \"dtype\": \"int8\",\n        \"samples\": [\n          11\n        ],\n        \"num_unique_values\": 1,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"minutes\",\n      \"properties\": {\n        \"dtype\": \"int8\",\n        \"samples\": [\n          0\n        ],\n        \"num_unique_values\": 60,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"seconds\",\n      \"properties\": {\n        \"dtype\": \"int8\",\n        \"samples\": [\n          0\n        ],\n        \"num_unique_values\": 60,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Anomaly\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"samples\": [\n          1\n        ],\n        \"num_unique_values\": 2,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Anomaly_Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03490997289664309,\n        \"min\": -0.1545646682292755,\n        \"max\": 0.04418948077977647,\n        \"samples\": [\n          -0.09906398867309585\n        ],\n        \"num_unique_values\": 3595,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Print the anomalies\n",
        "print(\"Anomalies: \\n\", iforest_results[iforest_results['Anomaly'] == 1])"
      ],
      "metadata": {
        "id": "8HPK6UVeOkuP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8ec0ef85-73fd-4853-8956-7d7f84e44f75"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Anomalies: \n",
            "                      FIT101  MV101    LIT101  P101  FIT201    LIT301  hours  \\\n",
            "time                                                                          \n",
            "2023-07-17 11:00:05    0.00      0  0.824259     0     0.0  0.812963     11   \n",
            "2023-07-17 11:00:06    0.00      0  0.824259     0     0.0  0.785185     11   \n",
            "2023-07-17 11:00:28    0.00      0  0.802963     0     0.0  0.825926     11   \n",
            "2023-07-17 11:00:29    0.00      0  0.802963     0     0.0  0.798148     11   \n",
            "2023-07-17 11:00:50    0.00      0  0.808889     0     0.0  0.833889     11   \n",
            "...                     ...    ...       ...   ...     ...       ...    ...   \n",
            "2023-07-17 11:58:56    2.55      1  0.505370     0     0.0  1.012407     11   \n",
            "2023-07-17 11:59:03    2.55      0  0.826481     0     0.0  0.823519     11   \n",
            "2023-07-17 11:59:04    0.00      0  0.826481     0     0.0  0.795741     11   \n",
            "2023-07-17 11:59:27    0.00      0  0.822963     1     0.0  0.781482     11   \n",
            "2023-07-17 11:59:49    0.00      0  0.819444     0     0.0  0.795000     11   \n",
            "\n",
            "                     minutes  seconds  Anomaly  Anomaly_Score  \n",
            "time                                                           \n",
            "2023-07-17 11:00:05        0        5        1       0.017038  \n",
            "2023-07-17 11:00:06        0        6        1       0.033997  \n",
            "2023-07-17 11:00:28        0       28        1       0.006093  \n",
            "2023-07-17 11:00:29        0       29        1       0.001727  \n",
            "2023-07-17 11:00:50        0       50        1       0.019959  \n",
            "...                      ...      ...      ...            ...  \n",
            "2023-07-17 11:58:56       58       56        1       0.006017  \n",
            "2023-07-17 11:59:03       59        3        1       0.013985  \n",
            "2023-07-17 11:59:04       59        4        1       0.036218  \n",
            "2023-07-17 11:59:27       59       27        1       0.026672  \n",
            "2023-07-17 11:59:49       59       49        1       0.006416  \n",
            "\n",
            "[72 rows x 11 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Sort records by anomaly score\n",
        "sorted_results = iforest_results.sort_values(by='Anomaly_Score', ascending=False)\n",
        "\n",
        "# Display the sorted anomalies results\n",
        "print(\"Anomalies: \\n\", sorted_results[sorted_results['Anomaly'] == 1])"
      ],
      "metadata": {
        "id": "BrUZ0jfswqEe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ccecf7a3-e0fa-48a5-9ef2-a28ac3ade4e4"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Anomalies: \n",
            "                      FIT101  MV101    LIT101  P101  FIT201    LIT301  hours  \\\n",
            "time                                                                          \n",
            "2023-07-17 11:55:02     0.0      0  0.803889     1    0.00  0.778148     11   \n",
            "2023-07-17 11:02:00     0.0      0  0.827778     0    0.00  0.785000     11   \n",
            "2023-07-17 11:59:04     0.0      0  0.826481     0    0.00  0.795741     11   \n",
            "2023-07-17 11:00:06     0.0      0  0.824259     0    0.00  0.785185     11   \n",
            "2023-07-17 11:00:52     0.0      0  0.808889     0    0.00  0.778333     11   \n",
            "...                     ...    ...       ...   ...     ...       ...    ...   \n",
            "2023-07-17 11:09:46     0.0      0  0.823519     0    0.00  0.790370     11   \n",
            "2023-07-17 11:51:59     0.0      0  0.803704     0    0.00  0.842222     11   \n",
            "2023-07-17 11:49:07     0.0      1  0.474630     1    2.45  0.897222     11   \n",
            "2023-07-17 11:57:58     0.0      0  0.810926     0    0.00  0.806111     11   \n",
            "2023-07-17 11:09:23     0.0      0  0.816481     0    0.00  0.782963     11   \n",
            "\n",
            "                     minutes  seconds  Anomaly  Anomaly_Score  \n",
            "time                                                           \n",
            "2023-07-17 11:55:02       55        2        1       0.044189  \n",
            "2023-07-17 11:02:00        2        0        1       0.041613  \n",
            "2023-07-17 11:59:04       59        4        1       0.036218  \n",
            "2023-07-17 11:00:06        0        6        1       0.033997  \n",
            "2023-07-17 11:00:52        0       52        1       0.032536  \n",
            "...                      ...      ...      ...            ...  \n",
            "2023-07-17 11:09:46        9       46        1       0.000558  \n",
            "2023-07-17 11:51:59       51       59        1       0.000537  \n",
            "2023-07-17 11:49:07       49        7        1       0.000444  \n",
            "2023-07-17 11:57:58       57       58        1       0.000399  \n",
            "2023-07-17 11:09:23        9       23        1       0.000107  \n",
            "\n",
            "[72 rows x 11 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The highest anomaly score is 0.044189 and the lowest anomaly score is 0.000107."
      ],
      "metadata": {
        "id": "ekmG6VnSjey-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Count the number of anomalies\n",
        "number_of_anomalies = iforest_results['Anomaly'].sum()\n",
        "\n",
        "print(\"Number of anomalies detected:\", number_of_anomalies)"
      ],
      "metadata": {
        "id": "6SDXkYjMyVA7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f05dfc68-e6f6-4dfe-d66e-e31afdced9e6"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of anomalies detected: 72\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Recall the number of rows and columns in the dataset\n",
        "print(\"The number of rows and columns: \", data.shape)"
      ],
      "metadata": {
        "id": "sAikYQcpy0lw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e966e622-66ce-484d-dc5c-aac4a3d7573a"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The number of rows and columns:  (3595, 9)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The number of anomalies detected is 72, representing 2% of the 3595 records in the datasets.\n",
        "\n",
        "This 2% is the fraction parameter we have defined at 'Step 2.2. Create Model' of the PyCaret Workflow.\n",
        "\n",
        "The number of anomalies assigned by the model will increase in proportion of the percentage of fraction parameter we define.  "
      ],
      "metadata": {
        "id": "HtPXX8c8y2Z_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**4. Analyse Model**"
      ],
      "metadata": {
        "id": "UsMGlEifb6EB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot anomalies\n",
        "import plotly.graph_objects as go\n",
        "\n",
        "# Plot value on y-axis and date on x-axis\n",
        "fig = px.line(iforest_results, x=iforest_results.index, y=['FIT101', 'MV101', 'LIT101', 'P101', 'FIT201', 'LIT301'], title='TRIST Anomaly Detection - Plot all the anomalies identified', template = 'plotly_dark')\n",
        "\n",
        "# Create list of outliers\n",
        "outliers = iforest_results[iforest_results['Anomaly'] == 1].index\n",
        "\n",
        "# obtain y value of anomalies to plot\n",
        "y_values1 = [iforest_results.loc[i]['FIT101'] for i in outliers]\n",
        "\n",
        "fig.add_trace(go.Scatter(x=outliers, y=y_values1, mode = 'markers',\n",
        "                name = 'FIT101 Anomalies',\n",
        "                marker=dict(color='yellow',size=10)))\n",
        "\n",
        "# obtain y value of anomalies to plot\n",
        "y_values2 = [iforest_results.loc[i]['MV101'] for i in outliers]\n",
        "\n",
        "fig.add_trace(go.Scatter(x=outliers, y=y_values2, mode = 'markers',\n",
        "                name = 'MV101 Anomalies',\n",
        "                marker=dict(color='white',size=10)))\n",
        "\n",
        "# obtain y value of anomalies to plot\n",
        "y_values3 = [iforest_results.loc[i]['LIT101'] for i in outliers]\n",
        "\n",
        "fig.add_trace(go.Scatter(x=outliers, y=y_values3, mode = 'markers',\n",
        "                name = 'LIT101 Anomalies',\n",
        "                marker=dict(color='pink',size=10)))\n",
        "\n",
        "# obtain y value of anomalies to plot\n",
        "y_values4 = [iforest_results.loc[i]['P101'] for i in outliers]\n",
        "\n",
        "fig.add_trace(go.Scatter(x=outliers, y=y_values4, mode = 'markers',\n",
        "                name = 'P101 Anomalies',\n",
        "                marker=dict(color='lightcyan',size=10)))\n",
        "\n",
        "# obtain y value of anomalies to plot\n",
        "y_values5 = [iforest_results.loc[i]['FIT201'] for i in outliers]\n",
        "\n",
        "fig.add_trace(go.Scatter(x=outliers, y=y_values5, mode = 'markers',\n",
        "                name = 'FIT201 Anomalies',\n",
        "                marker=dict(color='lightgoldenrodyellow',size=10)))\n",
        "\n",
        "# obtain y value of anomalies to plot\n",
        "y_values6 = [iforest_results.loc[i]['LIT301'] for i in outliers]\n",
        "\n",
        "fig.add_trace(go.Scatter(x=outliers, y=y_values6, mode = 'markers',\n",
        "                name = 'LIT301 Anomalies',\n",
        "                marker=dict(color='lightyellow',size=10)))\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "id": "ZiRTNmLWO-iI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "d6f94fe4-9934-49a6-e021-9556c520349d"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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    {
      "cell_type": "markdown",
      "source": [
        "Explanation for the above graph:\n",
        "* FIT101 = Sensor 101 measuring water flow rate\n",
        "* MV101 = Motorised valve 101\n",
        "* LIT101 = Sensor 101 measuring water level at Tank 101\n",
        "* P101 = Pump 101\n",
        "* FIT201 = Sensor 201 measuring water flow rate\n",
        "* LIT301 = Sensor 301 measuring water level at Tank 301\n",
        "\n",
        "Please refer to [SWat Testbed description](https://minicps.readthedocs.io/en/latest/swat-tutorial.html?highlight=plc1#supply-and-storage-control) for details of Supply and Storage control in water treatment plant."
      ],
      "metadata": {
        "id": "75oH08F8wzQm"
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    },
    {
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      "source": [
        "# Plot anomalies in 3D with t-SNE\n",
        "plot_model(iforest, plot = 'tsne')"
      ],
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        "id": "RLPjQWpjO9Gz",
        "colab": {
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      "execution_count": 21,
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TSNE Plot for Outliers\"},\"height\":800,\"width\":900},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('93c675dc-5328-431f-808d-a08c9b29a9c7');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**5. Save Trained Model**"
      ],
      "metadata": {
        "id": "Zcuj2TtXcA0A"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Save pipeline into pickle file\n",
        "# Download the pickle file from Google Colab's \"Files\" section on the left-hand side bar if you want to use the model later.\n",
        "save_model(iforest, 'iforest_0.02_anomalous')"
      ],
      "metadata": {
        "id": "CAGnoZ95cDI1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dec604c0-d4a5-456b-e8ff-b483f592a614"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Transformation Pipeline and Model Successfully Saved\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(Pipeline(memory=Memory(location=None),\n",
              "          steps=[('numerical_imputer',\n",
              "                  TransformerWrapper(include=['FIT101', 'MV101', 'LIT101',\n",
              "                                              'P101', 'FIT201', 'LIT301',\n",
              "                                              'hours', 'minutes', 'seconds'],\n",
              "                                     transformer=SimpleImputer())),\n",
              "                 ('categorical_imputer',\n",
              "                  TransformerWrapper(include=[],\n",
              "                                     transformer=SimpleImputer(strategy='most_frequent'))),\n",
              "                 ('trained_model',\n",
              "                  IForest(behaviour='new', bootstrap=False, contamination=0.02,\n",
              "     max_features=1.0, max_samples='auto', n_estimators=100, n_jobs=-1,\n",
              "     random_state=123, verbose=0))]),\n",
              " 'iforest_0.02_anomalous.pkl')"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**6. Load Trained Model**"
      ],
      "metadata": {
        "id": "-itmxonkTxTp"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "40ed5152",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "outputId": "aed02cc2-ad6a-448c-a510-686a8cd72131"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Transformation Pipeline and Model Successfully Loaded\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(memory=FastMemory(location=/tmp/joblib),\n",
              "         steps=[('numerical_imputer',\n",
              "                 TransformerWrapper(include=['FIT101', 'MV101', 'LIT101',\n",
              "                                             'P101', 'FIT201', 'LIT301',\n",
              "                                             'hours', 'minutes', 'seconds'],\n",
              "                                    transformer=SimpleImputer())),\n",
              "                ('categorical_imputer',\n",
              "                 TransformerWrapper(include=[],\n",
              "                                    transformer=SimpleImputer(strategy='most_frequent'))),\n",
              "                ('trained_model',\n",
              "                 IForest(behaviour='new', bootstrap=False, contamination=0.02,\n",
              "    max_features=1.0, max_samples='auto', n_estimators=100, n_jobs=-1,\n",
              "    random_state=123, verbose=0))])"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(memory=FastMemory(location=/tmp/joblib),\n",
              "         steps=[(&#x27;numerical_imputer&#x27;,\n",
              "                 TransformerWrapper(include=[&#x27;FIT101&#x27;, &#x27;MV101&#x27;, &#x27;LIT101&#x27;,\n",
              "                                             &#x27;P101&#x27;, &#x27;FIT201&#x27;, &#x27;LIT301&#x27;,\n",
              "                                             &#x27;hours&#x27;, &#x27;minutes&#x27;, &#x27;seconds&#x27;],\n",
              "                                    transformer=SimpleImputer())),\n",
              "                (&#x27;categorical_imputer&#x27;,\n",
              "                 TransformerWrapper(include=[],\n",
              "                                    transformer=SimpleImputer(strategy=&#x27;most_frequent&#x27;))),\n",
              "                (&#x27;trained_model&#x27;,\n",
              "                 IForest(behaviour=&#x27;new&#x27;, bootstrap=False, contamination=0.02,\n",
              "    max_features=1.0, max_samples=&#x27;auto&#x27;, n_estimators=100, n_jobs=-1,\n",
              "    random_state=123, verbose=0))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(memory=FastMemory(location=/tmp/joblib),\n",
              "         steps=[(&#x27;numerical_imputer&#x27;,\n",
              "                 TransformerWrapper(include=[&#x27;FIT101&#x27;, &#x27;MV101&#x27;, &#x27;LIT101&#x27;,\n",
              "                                             &#x27;P101&#x27;, &#x27;FIT201&#x27;, &#x27;LIT301&#x27;,\n",
              "                                             &#x27;hours&#x27;, &#x27;minutes&#x27;, &#x27;seconds&#x27;],\n",
              "                                    transformer=SimpleImputer())),\n",
              "                (&#x27;categorical_imputer&#x27;,\n",
              "                 TransformerWrapper(include=[],\n",
              "                                    transformer=SimpleImputer(strategy=&#x27;most_frequent&#x27;))),\n",
              "                (&#x27;trained_model&#x27;,\n",
              "                 IForest(behaviour=&#x27;new&#x27;, bootstrap=False, contamination=0.02,\n",
              "    max_features=1.0, max_samples=&#x27;auto&#x27;, n_estimators=100, n_jobs=-1,\n",
              "    random_state=123, verbose=0))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">numerical_imputer: TransformerWrapper</label><div class=\"sk-toggleable__content\"><pre>TransformerWrapper(include=[&#x27;FIT101&#x27;, &#x27;MV101&#x27;, &#x27;LIT101&#x27;, &#x27;P101&#x27;, &#x27;FIT201&#x27;,\n",
              "                            &#x27;LIT301&#x27;, &#x27;hours&#x27;, &#x27;minutes&#x27;, &#x27;seconds&#x27;],\n",
              "                   transformer=SimpleImputer())</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">transformer: SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer()</pre></div></div></div></div></div></div></div></div><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">categorical_imputer: TransformerWrapper</label><div class=\"sk-toggleable__content\"><pre>TransformerWrapper(include=[],\n",
              "                   transformer=SimpleImputer(strategy=&#x27;most_frequent&#x27;))</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">transformer: SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">IForest</label><div class=\"sk-toggleable__content\"><pre>IForest(behaviour=&#x27;new&#x27;, bootstrap=False, contamination=0.02,\n",
              "    max_features=1.0, max_samples=&#x27;auto&#x27;, n_estimators=100, n_jobs=-1,\n",
              "    random_state=123, verbose=0)</pre></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "# Load the trained model (i.e. the pickle file created in previous step)\n",
        "# Or upload the pickle file (iforest_X_anomalous.pkl) from your local machine if you have stopped the training process previously.\n",
        "loaded_iforest_pipeline = load_model('iforest_0.02_anomalous')\n",
        "loaded_iforest_pipeline"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**7. Predict Unseen Data**"
      ],
      "metadata": {
        "id": "v4z_zzY9b-sC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### A. Unseen Attack 1\n",
        "\n",
        "* Method: Keep MV101 open until tank T101 overflow\n",
        "\n",
        "* Result: tank T101 overflow"
      ],
      "metadata": {
        "id": "PN9mWsE1ylwM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 1.Data Preparation"
      ],
      "metadata": {
        "id": "O3rHeizOlJaf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Upload a file from local machine to the Google Colab environment\n",
        "from google.colab import files\n",
        "import io\n",
        "import pandas as pd\n",
        "\n",
        "uploaded = files.upload()\n",
        "\n",
        "# Assuming you know the filename or if there's only one file uploaded\n",
        "filename = next(iter(uploaded))\n",
        "unseen_data1 = pd.read_csv(io.BytesIO(uploaded[filename]), parse_dates=['time'])\n",
        "unseen_data1['time'] = unseen_data1['time'].dt.strftime('%Y-%m-%d %H:%M:%S')\n"
      ],
      "metadata": {
        "id": "DTQke5LAlZ3J",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "outputId": "d066a0c2-08d8-46e5-8f39-13d0590be72b"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-5b61ab5c-3f2b-4011-9247-b693c7fc8bcd\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-5b61ab5c-3f2b-4011-9247-b693c7fc8bcd\">\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>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving attack1.csv to attack1.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the number of rows and columns in the dataframe\n",
        "print(\"The number of rows and columns: \", unseen_data1.shape)"
      ],
      "metadata": {
        "id": "bLmM4W_Klnud",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4c8b3464-14c8-408d-aefb-4fab3adf27af"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The number of rows and columns:  (75, 7)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Print the first 5 rows of the dataframe\n",
        "print(\"The first 5 rows of the dataset: \\n\", unseen_data1.head())"
      ],
      "metadata": {
        "id": "JwfqDTJWlqrM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "73df200b-1563-44e5-8179-539ada35ad35"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The first 5 rows of the dataset: \n",
            "                   time  FIT101  MV101    LIT101  P101  FIT201    LIT301\n",
            "0  2023-07-31 16:45:06    2.55      1  0.500741     1    2.45  0.507037\n",
            "1  2023-07-31 16:45:07    2.55      1  0.502593     1    2.45  0.524630\n",
            "2  2023-07-31 16:45:08    2.55      1  0.504444     1    2.45  0.542222\n",
            "3  2023-07-31 16:45:09    2.55      1  0.505926     1    2.45  0.556296\n",
            "4  2023-07-31 16:45:10    2.55      1  0.507778     1    2.45  0.573889\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the time span of the unseen data\n",
        "unseen_data1['time'] = pd.to_datetime(unseen_data1['time'])\n",
        "time_span = unseen_data1.time.max() - unseen_data1.time.min()\n",
        "print(\"Time span of the dataset:\", time_span)"
      ],
      "metadata": {
        "id": "szTh-I6cltkr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d7e96ae6-998b-4bbe-f832-a1960f92df19"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time span of the dataset: 0 days 00:01:14\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot a graph to see how data looks like\n",
        "import plotly.express as px\n",
        "fig = px.line(unseen_data1, x=\"time\", y=['FIT101', 'MV101', 'LIT101', 'P101', 'FIT201', 'LIT301'], title='TRIST unseen data - attack 1 (Keep MV101 open until tank T101 overflow)', template = 'plotly_dark')\n",
        "fig.show()"
      ],
      "metadata": {
        "id": "lcJuCvSPlymy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "08b0b2bd-22a9-40c0-a183-63b242a58b2a"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
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    {
      "cell_type": "code",
      "source": [
        "# Algorithms cannot directly consume date or timestamp data, thus we will extract the features from the timestamp\n",
        "# and will drop the actual timestamp column before training models.\n",
        "# Set the timestamp column as the index of the dataframe\n",
        "unseen_data1.set_index('time', drop=True, inplace=True)"
      ],
      "metadata": {
        "id": "M5yv_tCKmNox"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Extract features from timestamp\n",
        "unseen_data1['hours'] = [i.hour for i in unseen_data1.index]\n",
        "unseen_data1['minutes'] = [i.minute for i in unseen_data1.index]\n",
        "unseen_data1['seconds']= [i.second for i in unseen_data1.index]\n",
        "\n",
        "print(\"\\nThe first 5 rows of the dataset: \\n\", unseen_data1.head())\n",
        "\n",
        "print(\"\\nThe last 5 rows of the dataset: \\n\", unseen_data1.tail())"
      ],
      "metadata": {
        "id": "-mYDusHlmQnx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fd219dfe-4af1-442a-e7ef-14fc32415245"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "The first 5 rows of the dataset: \n",
            "                      FIT101  MV101    LIT101  P101  FIT201    LIT301  hours  \\\n",
            "time                                                                          \n",
            "2023-07-31 16:45:06    2.55      1  0.500741     1    2.45  0.507037     16   \n",
            "2023-07-31 16:45:07    2.55      1  0.502593     1    2.45  0.524630     16   \n",
            "2023-07-31 16:45:08    2.55      1  0.504444     1    2.45  0.542222     16   \n",
            "2023-07-31 16:45:09    2.55      1  0.505926     1    2.45  0.556296     16   \n",
            "2023-07-31 16:45:10    2.55      1  0.507778     1    2.45  0.573889     16   \n",
            "\n",
            "                     minutes  seconds  \n",
            "time                                   \n",
            "2023-07-31 16:45:06       45        6  \n",
            "2023-07-31 16:45:07       45        7  \n",
            "2023-07-31 16:45:08       45        8  \n",
            "2023-07-31 16:45:09       45        9  \n",
            "2023-07-31 16:45:10       45       10  \n",
            "\n",
            "The last 5 rows of the dataset: \n",
            "                      FIT101  MV101    LIT101  P101  FIT201    LIT301  hours  \\\n",
            "time                                                                          \n",
            "2023-07-31 16:46:16    2.55      1  1.202222     1     0.0  0.536296     16   \n",
            "2023-07-31 16:46:17    2.55      1  1.202222     1     0.0  0.508519     16   \n",
            "2023-07-31 16:46:18    2.55      1  1.202222     1     0.0  0.480741     16   \n",
            "2023-07-31 16:46:19    2.55      1  1.202222     1     0.0  0.452963     16   \n",
            "2023-07-31 16:46:20    2.55      1  1.202222     1     0.0  0.425185     16   \n",
            "\n",
            "                     minutes  seconds  \n",
            "time                                   \n",
            "2023-07-31 16:46:16       46       16  \n",
            "2023-07-31 16:46:17       46       17  \n",
            "2023-07-31 16:46:18       46       18  \n",
            "2023-07-31 16:46:19       46       19  \n",
            "2023-07-31 16:46:20       46       20  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 2.Prediction"
      ],
      "metadata": {
        "id": "sROyh3D-l-td"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Predict on test set\n",
        "# The predict_model function returns Anomaly and Anomaly_Score label as a new column in the input dataframe.\n",
        "# The predict_model is only useful when you want to obtain labels on unseen data (i.e. data that was not used during training the model).\n",
        "iforest_pred1 = predict_model(iforest, data=unseen_data1)\n",
        "iforest_pred1"
      ],
      "metadata": {
        "id": "oSvoEU9EY00r",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 802
        },
        "outputId": "c386ad9c-a385-4c7b-8d17-fc9d6f711ddf"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     FIT101  MV101    LIT101  P101  FIT201    LIT301  hours  \\\n",
              "time                                                                          \n",
              "2023-07-31 16:45:06    2.55    1.0  0.500741   1.0    2.45  0.507037   16.0   \n",
              "2023-07-31 16:45:07    2.55    1.0  0.502593   1.0    2.45  0.524630   16.0   \n",
              "2023-07-31 16:45:08    2.55    1.0  0.504444   1.0    2.45  0.542222   16.0   \n",
              "2023-07-31 16:45:09    2.55    1.0  0.505926   1.0    2.45  0.556296   16.0   \n",
              "2023-07-31 16:45:10    2.55    1.0  0.507778   1.0    2.45  0.573889   16.0   \n",
              "...                     ...    ...       ...   ...     ...       ...    ...   \n",
              "2023-07-31 16:46:16    2.55    1.0  1.202222   1.0    0.00  0.536296   16.0   \n",
              "2023-07-31 16:46:17    2.55    1.0  1.202222   1.0    0.00  0.508519   16.0   \n",
              "2023-07-31 16:46:18    2.55    1.0  1.202222   1.0    0.00  0.480741   16.0   \n",
              "2023-07-31 16:46:19    2.55    1.0  1.202222   1.0    0.00  0.452963   16.0   \n",
              "2023-07-31 16:46:20    2.55    1.0  1.202222   1.0    0.00  0.425185   16.0   \n",
              "\n",
              "                     minutes  seconds  Anomaly  Anomaly_Score  \n",
              "time                                                           \n",
              "2023-07-31 16:45:06     45.0      6.0        0      -0.026827  \n",
              "2023-07-31 16:45:07     45.0      7.0        0      -0.027619  \n",
              "2023-07-31 16:45:08     45.0      8.0        0      -0.031736  \n",
              "2023-07-31 16:45:09     45.0      9.0        0      -0.027762  \n",
              "2023-07-31 16:45:10     45.0     10.0        0      -0.023524  \n",
              "...                      ...      ...      ...            ...  \n",
              "2023-07-31 16:46:16     46.0     16.0        1       0.000469  \n",
              "2023-07-31 16:46:17     46.0     17.0        1       0.000194  \n",
              "2023-07-31 16:46:18     46.0     18.0        1       0.000110  \n",
              "2023-07-31 16:46:19     46.0     19.0        0      -0.000418  \n",
              "2023-07-31 16:46:20     46.0     20.0        0      -0.002143  \n",
              "\n",
              "[75 rows x 11 columns]"
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>FIT101</th>\n",
              "      <th>MV101</th>\n",
              "      <th>LIT101</th>\n",
              "      <th>P101</th>\n",
              "      <th>FIT201</th>\n",
              "      <th>LIT301</th>\n",
              "      <th>hours</th>\n",
              "      <th>minutes</th>\n",
              "      <th>seconds</th>\n",
              "      <th>Anomaly</th>\n",
              "      <th>Anomaly_Score</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>time</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2023-07-31 16:45:06</th>\n",
              "      <td>2.55</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.500741</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.45</td>\n",
              "      <td>0.507037</td>\n",
              "      <td>16.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.026827</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-07-31 16:45:07</th>\n",
              "      <td>2.55</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.502593</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.45</td>\n",
              "      <td>0.524630</td>\n",
              "      <td>16.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.027619</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-07-31 16:45:08</th>\n",
              "      <td>2.55</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.504444</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.45</td>\n",
              "      <td>0.542222</td>\n",
              "      <td>16.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.031736</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-07-31 16:45:09</th>\n",
              "      <td>2.55</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.505926</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.45</td>\n",
              "      <td>0.556296</td>\n",
              "      <td>16.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.027762</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-07-31 16:45:10</th>\n",
              "      <td>2.55</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.507778</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.45</td>\n",
              "      <td>0.573889</td>\n",
              "      <td>16.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.023524</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-07-31 16:46:16</th>\n",
              "      <td>2.55</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.202222</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.536296</td>\n",
              "      <td>16.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.000469</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-07-31 16:46:17</th>\n",
              "      <td>2.55</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.202222</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.508519</td>\n",
              "      <td>16.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.000194</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-07-31 16:46:18</th>\n",
              "      <td>2.55</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.202222</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.480741</td>\n",
              "      <td>16.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.000110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-07-31 16:46:19</th>\n",
              "      <td>2.55</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.202222</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.452963</td>\n",
              "      <td>16.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.000418</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-07-31 16:46:20</th>\n",
              "      <td>2.55</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.202222</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.425185</td>\n",
              "      <td>16.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.002143</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>75 rows × 11 columns</p>\n",
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              "    async function quickchart(key) {\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "iforest_pred1",
              "summary": "{\n  \"name\": \"iforest_pred1\",\n  \"rows\": 75,\n  \"fields\": [\n    {\n      \"column\": \"FIT101\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.941594867382432e-16,\n        \"min\": 2.55,\n        \"max\": 2.55,\n        \"samples\": [\n          2.55\n        ],\n        \"num_unique_values\": 1,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MV101\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 1.0,\n        \"max\": 1.0,\n        \"samples\": [\n          1.0\n        ],\n        \"num_unique_values\": 1,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LIT101\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2848962082785005,\n        \"min\": 0.500740740740741,\n        \"max\": 1.20222222222222,\n        \"samples\": [\n          0.500740740740741\n        ],\n        \"num_unique_values\": 58,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P101\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4214945523463591,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"samples\": [\n          0.0\n        ],\n        \"num_unique_values\": 2,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FIT201\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.2145818247483409,\n        \"min\": 0.0,\n        \"max\": 2.45,\n        \"samples\": [\n          0.0\n        ],\n        \"num_unique_values\": 2,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LIT301\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.15905313584924977,\n        \"min\": 0.425185185185179,\n        \"max\": 1.01166666666666,\n        \"samples\": [\n          0.573888888888888\n        ],\n        \"num_unique_values\": 75,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hours\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 16.0,\n        \"max\": 16.0,\n        \"samples\": [\n          16.0\n        ],\n        \"num_unique_values\": 1,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"minutes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.45202248210053037,\n        \"min\": 45.0,\n        \"max\": 46.0,\n        \"samples\": [\n          46.0\n        ],\n        \"num_unique_values\": 2,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"seconds\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17.061890519755078,\n        \"min\": 0.0,\n        \"max\": 59.0,\n        \"samples\": [\n          6.0\n        ],\n        \"num_unique_values\": 60,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Anomaly\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"samples\": [\n          1\n        ],\n        \"num_unique_values\": 2,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Anomaly_Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03276392956750158,\n        \"min\": -0.11196910100165353,\n        \"max\": 0.011402663216702247,\n        \"samples\": [\n          -0.02352400465671567\n        ],\n        \"num_unique_values\": 75,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Print the predicted anomalies\n",
        "print(\"Anomalies: \\n\", iforest_pred1[iforest_pred1['Anomaly'] == 1])"
      ],
      "metadata": {
        "id": "bgJWJiNvSYaq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b675ee2e-548a-4679-d64f-dc21bb445ba2"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Anomalies: \n",
            "                      FIT101  MV101    LIT101  P101  FIT201    LIT301  hours  \\\n",
            "time                                                                          \n",
            "2023-07-31 16:45:59    2.55    1.0  1.022778   0.0     0.0  0.997407   16.0   \n",
            "2023-07-31 16:46:07    2.55    1.0  1.202222   1.0     0.0  0.780741   16.0   \n",
            "2023-07-31 16:46:08    2.55    1.0  1.202222   1.0     0.0  0.752963   16.0   \n",
            "2023-07-31 16:46:09    2.55    1.0  1.202222   1.0     0.0  0.725185   16.0   \n",
            "2023-07-31 16:46:10    2.55    1.0  1.202222   1.0     0.0  0.697407   16.0   \n",
            "2023-07-31 16:46:11    2.55    1.0  1.202222   1.0     0.0  0.669630   16.0   \n",
            "2023-07-31 16:46:12    2.55    1.0  1.202222   1.0     0.0  0.641852   16.0   \n",
            "2023-07-31 16:46:13    2.55    1.0  1.202222   1.0     0.0  0.614074   16.0   \n",
            "2023-07-31 16:46:14    2.55    1.0  1.202222   1.0     0.0  0.586296   16.0   \n",
            "2023-07-31 16:46:15    2.55    1.0  1.202222   1.0     0.0  0.564074   16.0   \n",
            "2023-07-31 16:46:16    2.55    1.0  1.202222   1.0     0.0  0.536296   16.0   \n",
            "2023-07-31 16:46:17    2.55    1.0  1.202222   1.0     0.0  0.508519   16.0   \n",
            "2023-07-31 16:46:18    2.55    1.0  1.202222   1.0     0.0  0.480741   16.0   \n",
            "\n",
            "                     minutes  seconds  Anomaly  Anomaly_Score  \n",
            "time                                                           \n",
            "2023-07-31 16:45:59     45.0     59.0        1       0.002474  \n",
            "2023-07-31 16:46:07     46.0      7.0        1       0.010223  \n",
            "2023-07-31 16:46:08     46.0      8.0        1       0.011403  \n",
            "2023-07-31 16:46:09     46.0      9.0        1       0.010950  \n",
            "2023-07-31 16:46:10     46.0     10.0        1       0.008718  \n",
            "2023-07-31 16:46:11     46.0     11.0        1       0.009546  \n",
            "2023-07-31 16:46:12     46.0     12.0        1       0.006727  \n",
            "2023-07-31 16:46:13     46.0     13.0        1       0.003517  \n",
            "2023-07-31 16:46:14     46.0     14.0        1       0.003866  \n",
            "2023-07-31 16:46:15     46.0     15.0        1       0.002514  \n",
            "2023-07-31 16:46:16     46.0     16.0        1       0.000469  \n",
            "2023-07-31 16:46:17     46.0     17.0        1       0.000194  \n",
            "2023-07-31 16:46:18     46.0     18.0        1       0.000110  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Count the number of anomalies\n",
        "number_of_anomalies = iforest_pred1['Anomaly'].sum()\n",
        "\n",
        "print(\"Number of anomalies detected in attack 1:\", number_of_anomalies)"
      ],
      "metadata": {
        "id": "ErXw9Z4Ks0-b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "495eca1a-f18b-4c28-d206-b0656d6518b8"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of anomalies detected in attack 1: 13\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Recall the number of rows and columns in the dataset\n",
        "print(\"The number of rows and columns: \", unseen_data1.shape)"
      ],
      "metadata": {
        "id": "CEdl--pKs_31",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f5a156a8-26d3-4c0e-d4ca-14424fcc0c20"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The number of rows and columns:  (75, 9)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot anomalies\n",
        "import plotly.graph_objects as go\n",
        "\n",
        "# Plot value on y-axis and date on x-axis\n",
        "fig = px.line(iforest_pred1, x=iforest_pred1.index, y=['FIT101', 'MV101', 'LIT101', 'P101', 'FIT201', 'LIT301'], title='TRIST Anomaly Detection - Prediction on Unseen Attack 1 (Keep MV101 open until tank T101 overflow)', template = 'plotly_dark')\n",
        "\n",
        "# Create list of outliers\n",
        "outliers = iforest_pred1[iforest_pred1['Anomaly'] == 1].index\n",
        "\n",
        "# obtain y value of anomalies to plot\n",
        "y_values1 = [iforest_pred1.loc[i]['FIT101'] for i in outliers]\n",
        "\n",
        "fig.add_trace(go.Scatter(x=outliers, y=y_values1, mode = 'markers',\n",
        "                name = 'FIT101 Anomalies',\n",
        "                marker=dict(color='yellow',size=10)))\n",
        "\n",
        "# obtain y value of anomalies to plot\n",
        "y_values2 = [iforest_pred1.loc[i]['MV101'] for i in outliers]\n",
        "\n",
        "fig.add_trace(go.Scatter(x=outliers, y=y_values2, mode = 'markers',\n",
        "                name = 'MV101 Anomalies',\n",
        "                marker=dict(color='white',size=10)))\n",
        "\n",
        "# obtain y value of anomalies to plot\n",
        "y_values3 = [iforest_pred1.loc[i]['LIT101'] for i in outliers]\n",
        "\n",
        "fig.add_trace(go.Scatter(x=outliers, y=y_values3, mode = 'markers',\n",
        "                name = 'LIT101 Anomalies',\n",
        "                marker=dict(color='pink',size=10)))\n",
        "\n",
        "# obtain y value of anomalies to plot\n",
        "y_values4 = [iforest_pred1.loc[i]['P101'] for i in outliers]\n",
        "\n",
        "fig.add_trace(go.Scatter(x=outliers, y=y_values4, mode = 'markers',\n",
        "                name = 'P101 Anomalies',\n",
        "                marker=dict(color='lightcyan',size=10)))\n",
        "\n",
        "# obtain y value of anomalies to plot\n",
        "y_values5 = [iforest_pred1.loc[i]['FIT201'] for i in outliers]\n",
        "\n",
        "fig.add_trace(go.Scatter(x=outliers, y=y_values5, mode = 'markers',\n",
        "                name = 'FIT201 Anomalies',\n",
        "                marker=dict(color='lightgoldenrodyellow',size=10)))\n",
        "\n",
        "# obtain y value of anomalies to plot\n",
        "y_values6 = [iforest_pred1.loc[i]['LIT301'] for i in outliers]\n",
        "\n",
        "fig.add_trace(go.Scatter(x=outliers, y=y_values6, mode = 'markers',\n",
        "                name = 'LIT301 Anomalies',\n",
        "                marker=dict(color='lightyellow',size=10)))\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "id": "vvqUaxDftgnp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "6d3e301c-4e45-4756-9193-7197aaced2f4"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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Anomaly Detection - Prediction on Unseen Attack 1 (Keep MV101 open until tank T101 overflow)\"}},                        {\"responsive\": true}                    ).then(function(){\n",
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              "\n",
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              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Explanation for the above graph:\n",
        "* FIT101 = Sensor 101 measuring water flow rate\n",
        "* MV101 = Motorised valve 101\n",
        "* LIT101 = Sensor 101 measuring water level at Tank 101\n",
        "* P101 = Pump 101\n",
        "* FIT201 = Sensor 201 measuring water flow rate\n",
        "* LIT301 = Sensor 301 measuring water level at Tank 301\n",
        "\n",
        "Please refer to [SWat Testbed description](https://minicps.readthedocs.io/en/latest/swat-tutorial.html?highlight=plc1#supply-and-storage-control) for details of Supply and Storage control in water treatment plant."
      ],
      "metadata": {
        "id": "u6EFJkj9w3LF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### B. Unseen Attack 2\n",
        "\n",
        "* Method: Keep MV101 open and manipulate LIT101 to a constant rate of 0.7\n",
        "\n",
        "* Result: tank T101 overflow"
      ],
      "metadata": {
        "id": "i1eDczcYzgan"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 1.Data Preparation"
      ],
      "metadata": {
        "id": "nanfBZRy0GDh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Upload a file from local machine to the Google Colab environment\n",
        "from google.colab import files\n",
        "import io\n",
        "import pandas as pd\n",
        "\n",
        "uploaded = files.upload()\n",
        "\n",
        "# Assuming you know the filename or if there's only one file uploaded\n",
        "filename = next(iter(uploaded))\n",
        "unseen_data2 = pd.read_csv(io.BytesIO(uploaded[filename]), parse_dates=['time'])\n",
        "unseen_data2['time'] = unseen_data2['time'].dt.strftime('%Y-%m-%d %H:%M:%S')\n"
      ],
      "metadata": {
        "id": "iAfCNTH20MBh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "outputId": "0518a7d8-d47c-4de3-dda2-2cf1f844b779"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
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              "     <input type=\"file\" id=\"files-92229a8b-c236-431e-aece-d9cffacf3aa9\" name=\"files[]\" multiple disabled\n",
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              "      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>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
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              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
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              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
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              "\n",
              "/**\n",
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              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
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              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
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              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
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              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
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              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
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              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
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              "      reader.readAsArrayBuffer(file);\n",
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              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving attack21_recorded.csv to attack21_recorded.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the number of rows and columns in the dataframe\n",
        "print(\"The number of rows and columns: \", unseen_data2.shape)"
      ],
      "metadata": {
        "id": "faYde8Ca0Oeh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ed6e2f11-f007-4abf-e615-0b590461a3e3"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The number of rows and columns:  (71, 7)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Print the first 5 rows of the dataframe\n",
        "print(\"The first 5 rows of the dataset: \\n\", unseen_data2.head())"
      ],
      "metadata": {
        "id": "jNTwfHLB0Qy-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e00fcefe-22f8-40fb-c159-a70c8cf16ce7"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The first 5 rows of the dataset: \n",
            "                   time  FIT101  MV101  LIT101  P101  FIT201    LIT301\n",
            "0  2023-07-31 14:49:12    2.55      1     0.7     1    2.45  0.507037\n",
            "1  2023-07-31 14:49:13    2.55      1     0.7     1    2.45  0.524630\n",
            "2  2023-07-31 14:49:14    2.55      1     0.7     1    2.45  0.542222\n",
            "3  2023-07-31 14:49:15    2.55      1     0.7     1    2.45  0.559815\n",
            "4  2023-07-31 14:49:16    2.55      1     0.7     1    2.45  0.577407\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the time span of the unseen data\n",
        "unseen_data2['time'] = pd.to_datetime(unseen_data2['time'])\n",
        "time_span = unseen_data2.time.max() - unseen_data2.time.min()\n",
        "print(\"Time span of the dataset:\", time_span)"
      ],
      "metadata": {
        "id": "zfLE33jp0TnG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d58261d7-bc14-4eca-fd70-caec2bc07e77"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time span of the dataset: 0 days 00:01:10\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot a graph to see how data looks like\n",
        "import plotly.express as px\n",
        "fig = px.line(unseen_data2, x=\"time\", y=['FIT101', 'MV101', 'LIT101', 'P101', 'FIT201', 'LIT301'], title='TRIST unseen data - attack 2 (Keep MV101 open and manipulate LIT101 to a constant rate of 0.7)', template = 'plotly_dark')\n",
        "fig.show()"
      ],
      "metadata": {
        "id": "puXVZSBp0Xp8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "d5f58543-f61d-4fb1-fbc4-9d30182ef8b0"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
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    },
    {
      "cell_type": "code",
      "source": [
        "# Algorithms cannot directly consume date or timestamp data, thus we will extract the features from the timestamp\n",
        "# and will drop the actual timestamp column before training models.\n",
        "# Set the timestamp column as the index of the dataframe\n",
        "unseen_data2.set_index('time', drop=True, inplace=True)"
      ],
      "metadata": {
        "id": "WUzT0uJg0fXJ"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Extract features from timestamp\n",
        "unseen_data2['hours'] = [i.hour for i in unseen_data2.index]\n",
        "unseen_data2['minutes'] = [i.minute for i in unseen_data2.index]\n",
        "unseen_data2['seconds']= [i.second for i in unseen_data2.index]\n",
        "\n",
        "print(\"\\nThe first 5 rows of the dataset: \\n\", unseen_data2.head())\n",
        "\n",
        "print(\"\\nThe last 5 rows of the dataset: \\n\", unseen_data2.tail())"
      ],
      "metadata": {
        "id": "41i7Sre70h7z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "84a10031-87dd-45cd-a0f2-096d7a60a4cb"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "The first 5 rows of the dataset: \n",
            "                      FIT101  MV101  LIT101  P101  FIT201    LIT301  hours  \\\n",
            "time                                                                        \n",
            "2023-07-31 14:49:12    2.55      1     0.7     1    2.45  0.507037     14   \n",
            "2023-07-31 14:49:13    2.55      1     0.7     1    2.45  0.524630     14   \n",
            "2023-07-31 14:49:14    2.55      1     0.7     1    2.45  0.542222     14   \n",
            "2023-07-31 14:49:15    2.55      1     0.7     1    2.45  0.559815     14   \n",
            "2023-07-31 14:49:16    2.55      1     0.7     1    2.45  0.577407     14   \n",
            "\n",
            "                     minutes  seconds  \n",
            "time                                   \n",
            "2023-07-31 14:49:12       49       12  \n",
            "2023-07-31 14:49:13       49       13  \n",
            "2023-07-31 14:49:14       49       14  \n",
            "2023-07-31 14:49:15       49       15  \n",
            "2023-07-31 14:49:16       49       16  \n",
            "\n",
            "The last 5 rows of the dataset: \n",
            "                      FIT101  MV101  LIT101  P101  FIT201    LIT301  hours  \\\n",
            "time                                                                        \n",
            "2023-07-31 14:50:18    2.55      1     0.7     1     0.0  0.663519     14   \n",
            "2023-07-31 14:50:19    2.55      1     0.7     1     0.0  0.635741     14   \n",
            "2023-07-31 14:50:20    2.55      1     0.7     1     0.0  0.607963     14   \n",
            "2023-07-31 14:50:21    2.55      1     0.7     1     0.0  0.585741     14   \n",
            "2023-07-31 14:50:22    2.55      1     0.7     1     0.0  0.557963     14   \n",
            "\n",
            "                     minutes  seconds  \n",
            "time                                   \n",
            "2023-07-31 14:50:18       50       18  \n",
            "2023-07-31 14:50:19       50       19  \n",
            "2023-07-31 14:50:20       50       20  \n",
            "2023-07-31 14:50:21       50       21  \n",
            "2023-07-31 14:50:22       50       22  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 2.Prediction"
      ],
      "metadata": {
        "id": "g-yOHtfP0nxt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Predict on test set\n",
        "# The predict_model function returns Anomaly and Anomaly_Score label as a new column in the input dataframe.\n",
        "# The predict_model is only useful when you want to obtain labels on unseen data (i.e. data that was not used during training the model).\n",
        "iforest_pred2 = predict_model(iforest, data=unseen_data2)\n",
        "iforest_pred2"
      ],
      "metadata": {
        "id": "accJo3nW0p3K",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        },
        "outputId": "49fdcd25-1831-41c7-8dd4-5871bec7429c"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     FIT101  MV101  LIT101  P101  FIT201    LIT301  hours  \\\n",
              "time                                                                        \n",
              "2023-07-31 14:49:12    2.55    1.0     0.7   1.0    2.45  0.507037   14.0   \n",
              "2023-07-31 14:49:13    2.55    1.0     0.7   1.0    2.45  0.524630   14.0   \n",
              "2023-07-31 14:49:14    2.55    1.0     0.7   1.0    2.45  0.542222   14.0   \n",
              "2023-07-31 14:49:15    2.55    1.0     0.7   1.0    2.45  0.559815   14.0   \n",
              "2023-07-31 14:49:16    2.55    1.0     0.7   1.0    2.45  0.577407   14.0   \n",
              "...                     ...    ...     ...   ...     ...       ...    ...   \n",
              "2023-07-31 14:50:18    2.55    1.0     0.7   1.0    0.00  0.663519   14.0   \n",
              "2023-07-31 14:50:19    2.55    1.0     0.7   1.0    0.00  0.635741   14.0   \n",
              "2023-07-31 14:50:20    2.55    1.0     0.7   1.0    0.00  0.607963   14.0   \n",
              "2023-07-31 14:50:21    2.55    1.0     0.7   1.0    0.00  0.585741   14.0   \n",
              "2023-07-31 14:50:22    2.55    1.0     0.7   1.0    0.00  0.557963   14.0   \n",
              "\n",
              "                     minutes  seconds  Anomaly  Anomaly_Score  \n",
              "time                                                           \n",
              "2023-07-31 14:49:12     49.0     12.0        0      -0.015558  \n",
              "2023-07-31 14:49:13     49.0     13.0        0      -0.017112  \n",
              "2023-07-31 14:49:14     49.0     14.0        0      -0.016775  \n",
              "2023-07-31 14:49:15     49.0     15.0        0      -0.017171  \n",
              "2023-07-31 14:49:16     49.0     16.0        0      -0.019545  \n",
              "...                      ...      ...      ...            ...  \n",
              "2023-07-31 14:50:18     50.0     18.0        0      -0.007111  \n",
              "2023-07-31 14:50:19     50.0     19.0        0      -0.006826  \n",
              "2023-07-31 14:50:20     50.0     20.0        0      -0.008675  \n",
              "2023-07-31 14:50:21     50.0     21.0        0      -0.006273  \n",
              "2023-07-31 14:50:22     50.0     22.0        0      -0.007080  \n",
              "\n",
              "[71 rows x 11 columns]"
            ],
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>FIT101</th>\n",
              "      <th>MV101</th>\n",
              "      <th>LIT101</th>\n",
              "      <th>P101</th>\n",
              "      <th>FIT201</th>\n",
              "      <th>LIT301</th>\n",
              "      <th>hours</th>\n",
              "      <th>minutes</th>\n",
              "      <th>seconds</th>\n",
              "      <th>Anomaly</th>\n",
              "      <th>Anomaly_Score</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>time</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2023-07-31 14:49:12</th>\n",
              "      <td>2.55</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.7</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.45</td>\n",
              "      <td>0.507037</td>\n",
              "      <td>14.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.015558</td>\n",
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              "    <tr>\n",
              "      <th>2023-07-31 14:49:13</th>\n",
              "      <td>2.55</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.7</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.45</td>\n",
              "      <td>0.524630</td>\n",
              "      <td>14.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.017112</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-07-31 14:49:14</th>\n",
              "      <td>2.55</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.7</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.45</td>\n",
              "      <td>0.542222</td>\n",
              "      <td>14.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.016775</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-07-31 14:49:15</th>\n",
              "      <td>2.55</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.7</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.45</td>\n",
              "      <td>0.559815</td>\n",
              "      <td>14.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.017171</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-07-31 14:49:16</th>\n",
              "      <td>2.55</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.7</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.45</td>\n",
              "      <td>0.577407</td>\n",
              "      <td>14.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.019545</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-07-31 14:50:18</th>\n",
              "      <td>2.55</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.7</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.663519</td>\n",
              "      <td>14.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.007111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-07-31 14:50:19</th>\n",
              "      <td>2.55</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.7</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.635741</td>\n",
              "      <td>14.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.006826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-07-31 14:50:20</th>\n",
              "      <td>2.55</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.7</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.607963</td>\n",
              "      <td>14.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.008675</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-07-31 14:50:21</th>\n",
              "      <td>2.55</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.7</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.585741</td>\n",
              "      <td>14.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.006273</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-07-31 14:50:22</th>\n",
              "      <td>2.55</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.7</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.557963</td>\n",
              "      <td>14.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.007080</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>71 rows × 11 columns</p>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "iforest_pred2",
              "summary": "{\n  \"name\": \"iforest_pred2\",\n  \"rows\": 71,\n  \"fields\": [\n    {\n      \"column\": \"FIT101\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.3417500810990772e-15,\n        \"min\": 2.55,\n        \"max\": 2.55,\n        \"samples\": [\n          2.55\n        ],\n        \"num_unique_values\": 1,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MV101\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 1.0,\n        \"max\": 1.0,\n        \"samples\": [\n          1.0\n        ],\n        \"num_unique_values\": 1,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LIT101\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.354375202747693e-16,\n        \"min\": 0.7,\n        \"max\": 0.7,\n        \"samples\": [\n          0.7\n        ],\n        \"num_unique_values\": 1,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P101\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4297769764621901,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"samples\": [\n          0.0\n        ],\n        \"num_unique_values\": 2,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FIT201\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1887234246374092,\n        \"min\": 0.0,\n        \"max\": 2.45,\n        \"samples\": [\n          0.0\n        ],\n        \"num_unique_values\": 2,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LIT301\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.13837018978200283,\n        \"min\": 0.507037037037037,\n        \"max\": 1.00666666666666,\n        \"samples\": [\n          0.887037037037033\n        ],\n        \"num_unique_values\": 71,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hours\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 14.0,\n        \"max\": 14.0,\n        \"samples\": [\n          14.0\n        ],\n        \"num_unique_values\": 1,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"minutes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.471309661242674,\n        \"min\": 49.0,\n        \"max\": 50.0,\n        \"samples\": [\n          50.0\n        ],\n        \"num_unique_values\": 2,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"seconds\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16.71503377575853,\n        \"min\": 0.0,\n        \"max\": 59.0,\n        \"samples\": [\n          12.0\n        ],\n        \"num_unique_values\": 60,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Anomaly\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"samples\": [\n          0\n        ],\n        \"num_unique_values\": 1,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Anomaly_Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.02418392332082435,\n        \"min\": -0.09975587749581494,\n        \"max\": -0.0036206567197835104,\n        \"samples\": [\n          -0.06197573881665164\n        ],\n        \"num_unique_values\": 70,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Print the predicted anomalies\n",
        "print(\"Anomalies: \\n\", iforest_pred2[iforest_pred2['Anomaly'] == 1])"
      ],
      "metadata": {
        "id": "y16b8P020qyr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5a799f06-6642-48ec-b087-01eb8aeca741"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Anomalies: \n",
            " Empty DataFrame\n",
            "Columns: [FIT101, MV101, LIT101, P101, FIT201, LIT301, hours, minutes, seconds, Anomaly, Anomaly_Score]\n",
            "Index: []\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Count the number of anomalies\n",
        "number_of_anomalies = iforest_pred2['Anomaly'].sum()\n",
        "\n",
        "print(\"Number of anomalies detected in attack 2:\", number_of_anomalies)"
      ],
      "metadata": {
        "id": "_ugkAGSB0vx5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9a83ea9c-af57-4953-f787-c335e00be1d2"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of anomalies detected in attack 2: 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Recall the number of rows and columns in the dataset\n",
        "print(\"The number of rows and columns: \", unseen_data2.shape)"
      ],
      "metadata": {
        "id": "gCpCtYrs0zC_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3719ff76-01a2-43a8-f0b2-72547b1a6cdb"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The number of rows and columns:  (71, 9)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The number of anomalies detected is 0, representing 0% of the 71 records in the datasets."
      ],
      "metadata": {
        "id": "KCPVzKPQ04Oz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot anomalies\n",
        "import plotly.graph_objects as go\n",
        "\n",
        "# Plot value on y-axis and date on x-axis\n",
        "fig = px.line(iforest_pred2, x=iforest_pred2.index, y=['FIT101', 'MV101', 'LIT101', 'P101', 'FIT201', 'LIT301'], title='TRIST Anomaly Detection - Prediction on Unseen Attack 2 (Keep MV101 open and manipulate LIT101 to a constant rate of 0.7)', template = 'plotly_dark')\n",
        "\n",
        "# Create list of outliers\n",
        "outliers = iforest_pred2[iforest_pred2['Anomaly'] == 1].index\n",
        "\n",
        "# obtain y value of anomalies to plot\n",
        "y_values1 = [iforest_pred2.loc[i]['FIT101'] for i in outliers]\n",
        "\n",
        "fig.add_trace(go.Scatter(x=outliers, y=y_values1, mode = 'markers',\n",
        "                name = 'FIT101 Anomalies',\n",
        "                marker=dict(color='yellow',size=10)))\n",
        "\n",
        "# obtain y value of anomalies to plot\n",
        "y_values2 = [iforest_pred2.loc[i]['MV101'] for i in outliers]\n",
        "\n",
        "fig.add_trace(go.Scatter(x=outliers, y=y_values2, mode = 'markers',\n",
        "                name = 'MV101 Anomalies',\n",
        "                marker=dict(color='white',size=10)))\n",
        "\n",
        "# obtain y value of anomalies to plot\n",
        "y_values3 = [iforest_pred2.loc[i]['LIT101'] for i in outliers]\n",
        "\n",
        "fig.add_trace(go.Scatter(x=outliers, y=y_values3, mode = 'markers',\n",
        "                name = 'LIT101 Anomalies',\n",
        "                marker=dict(color='pink',size=10)))\n",
        "\n",
        "# obtain y value of anomalies to plot\n",
        "y_values4 = [iforest_pred2.loc[i]['P101'] for i in outliers]\n",
        "\n",
        "fig.add_trace(go.Scatter(x=outliers, y=y_values4, mode = 'markers',\n",
        "                name = 'P101 Anomalies',\n",
        "                marker=dict(color='lightcyan',size=10)))\n",
        "\n",
        "# obtain y value of anomalies to plot\n",
        "y_values5 = [iforest_pred2.loc[i]['FIT201'] for i in outliers]\n",
        "\n",
        "fig.add_trace(go.Scatter(x=outliers, y=y_values5, mode = 'markers',\n",
        "                name = 'FIT201 Anomalies',\n",
        "                marker=dict(color='lightgoldenrodyellow',size=10)))\n",
        "\n",
        "# obtain y value of anomalies to plot\n",
        "y_values6 = [iforest_pred2.loc[i]['LIT301'] for i in outliers]\n",
        "\n",
        "fig.add_trace(go.Scatter(x=outliers, y=y_values6, mode = 'markers',\n",
        "                name = 'LIT301 Anomalies',\n",
        "                marker=dict(color='lightyellow',size=10)))\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "id": "-taq34BQ06_D",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "fcf028d9-d4c8-463e-d455-738b19ce3560"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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        "Explanation for the above graph:\n",
        "* FIT101 = Sensor 101 measuring water flow rate\n",
        "* MV101 = Motorised valve 101\n",
        "* LIT101 = Sensor 101 measuring water level at Tank 101\n",
        "* P101 = Pump 101\n",
        "* FIT201 = Sensor 201 measuring water flow rate\n",
        "* LIT301 = Sensor 301 measuring water level at Tank 301\n",
        "\n",
        "Please refer to [SWat Testbed description](https://minicps.readthedocs.io/en/latest/swat-tutorial.html?highlight=plc1#supply-and-storage-control) for details of Supply and Storage control in water treatment plant."
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