FactoryIO_TSC_sktime_CNNClassifier.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/carolsworld/FactoryIO_TSC/blob/main/FactoryIO_TSC_sktime_CNNClassifier.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Demonstration of using datasets generated multivariate time series data from Factory I/O 3D simulation for developing multi-class Time Series Classification Model for *Threat Detection* using *a simple CNN* in sktime (deep learning) library**"
      ],
      "metadata": {
        "id": "DYpe7-R6G2pk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Background"
      ],
      "metadata": {
        "id": "cmYyiv_YE2fk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We have identified 10 different scenarios in the Factory I/O scene. It could be a normal scenarios or threat scenarios.\n",
        "\n",
        "For each scenario, we have carried out 10 simulation runs (i.e. 10 instances). Therefore, there are tototally 100 simulation instances in the csv dataset. Each simulation run has 60 second of simulation.\n",
        "\n",
        "During the simulation run, we used the Web API of Factory I/O to collect data. Each instance contain 119 timepoints (timepoints from value '0' to value '118'). Therefore, the number of row in the csv dataset is 119 * 100 = 11,900 rows."
      ],
      "metadata": {
        "id": "EQpJDggkEgtM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "The scenario (being the label (y) to be predicted) include:\n",
        "\n",
        "*   Label #1 - 'Sensors_NormalSpeed': use potentiometer with normal conveyor speed.\n",
        "*   Label #2 - 'Sensors_TooSlowOrStop':use potentiometer with conveyor speed too slow or stopped.\n",
        "*   Label #3 - 'Sensors_Oscillate': use potentiometer with conveyor speed up and down illogically.\n",
        "*   Label #4 - 'Sensors_WrongDirection': use potentiometer with conveyor going the reverse direction.\n",
        "*   Label #5 - 'ForcedActuator_Normal': force actuator value with normal conveyor speed.\n",
        "*   Label #6 - 'ForcedActuator_TooSlowOrStop': force actuator value with conveyor speed too slow or stopped.\n",
        "*   Label #7 - 'ForcedActuator_TooFast': force actuator value with conveyor speed too fast.\n",
        "*   Label #8 - 'ForcedActuator_ExtremelyFast': force actuator value with conveyor speed extremely fast.\n",
        "*   Label #9 - 'ForcedActuator_Oscillate': force actuator value with conveyor speed up and down illogically.\n",
        "*   Label #10 - 'ForcedActuator_WrongDirection': force actuator value with conveyor going the reverse direction."
      ],
      "metadata": {
        "id": "EBohz0eFG06T"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "To train the deep learning model to predict the scenario, not all the data captured from the Web API of Factory I/O are used. Instead, only the following features (X) are used for deep learning model training.\n",
        "\n",
        "Multi-variables are used for model training:\n",
        "*   'var_0': Potentiometer 0 (V) - the speed input affecting the conveyor speed (collected via Web API)\n",
        "*   'var_1': Q_Move Conveyor - the speed of the conveyor (collected via Web API)\n",
        "*   'var_2': Speed Display - the LED displaying the speed (collected via Web API)\n",
        "*   'var_3': Speed Change - the change of speed comparing the previous and current timepoints, i.e. change of speed in every 0.5 second (calculated by python script during real-time simulation)\n",
        "*   'var_4': Acceleration Indicator - check if the speed is accelerating, decelerating, constant or illogically speeding up or down based on past 15 seconds in the speed history (calculated by python script during real-time simulation)\n",
        "*   'var_5': Actual Conveyor Status - based on the value of Q_Move Conveyor to design which category the simulation is falling into:\n",
        "\n",
        "> Q_Move Conveyor with -5 < speed < 0: 'conveyor wrong direction'\n",
        "\n",
        "> Q_Move Conveyor with speed == 0: 'conveyor stop'\n",
        "\n",
        "> Q_Move Conveyor with 0 < speed <= 2.30: 'conveyor too slow'\n",
        "\n",
        "> Q_Move Conveyor with 2.31 < speed <= 5.00: 'normal conveying speed'\n",
        "\n",
        "> Q_Move Conveyor with 5.01 < speed <= 7.00: 'conveyor too fast'\n",
        "\n",
        "> Q_Move Conveyor with other values: 'abnormal'\n",
        "\n"
      ],
      "metadata": {
        "id": "5zSQqxYSEnR-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Step 1 - Upload dataset (csv file)"
      ],
      "metadata": {
        "id": "c8Ib-9xlBEY7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Upload your csv file and check the number of rows and columns in the pandas dataframe\n",
        "from google.colab import files\n",
        "import io\n",
        "import pandas as pd\n",
        "\n",
        "uploaded = files.upload()\n",
        "\n",
        "filename = next(iter(uploaded))\n",
        "\n",
        "df = pd.read_csv(io.BytesIO(uploaded['factoryiodata.csv']))\n",
        "\n",
        "print(\"The number of rows and columns in the csv file: \", df.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "id": "0Yjc_GgLt0Wm",
        "outputId": "acf3b68b-bda5-43e3-9ef6-26354cff3a0b"
      },
      "execution_count": 1,
      "outputs": [
        {
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              "      Upload widget is only available when the cell has been executed in the\n",
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              "      <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",
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              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
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              "// 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",
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              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\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",
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              "  const inputElement = document.getElementById(inputId);\n",
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              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
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              "  // Wait for the user to pick the files.\n",
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              "  cancel.remove();\n",
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              "      const chunk = new Uint8Array(fileData, position, length);\n",
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              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
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              "  _uploadFiles,\n",
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              "})(self);\n",
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving factoryiodata.csv to factoryiodata.csv\n",
            "The number of rows and columns in the csv file:  (11900, 9)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "100 instances with 119 timepoints for each instance, expected number of row is 11900."
      ],
      "metadata": {
        "id": "xYki4ju92cXf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Display the first few rows of the dataframe to check file format\n",
        "print(df.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F2hcKIGHulSO",
        "outputId": "1723cf46-3bbe-4bc5-ec08-702e20a3ff56"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   instances  timepoints  var_0  var_1  var_2  var_3  var_4  var_5  label\n",
            "0          1           0    0.0   10.0    0.0   10.0      2      6      8\n",
            "1          1           1    0.0   10.0    0.0    0.0      1      6      8\n",
            "2          1           2    0.0   10.0    0.0    0.0      1      6      8\n",
            "3          1           3    0.0   10.0    0.0    0.0      1      6      8\n",
            "4          1           4    0.0   10.0    0.0    0.0      1      6      8\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are 9 columns as follows.\n",
        "*   'instance' is instance identifier. There are 100 instance in this dataset.\n",
        "*   'timepoints' is time series index for each instance. Every instance has 119 timepoints (from value '0' to value '118').\n",
        "*   'var_0' to ''var_5' are the raw data to learn\n",
        "*   'label' contains the labels of the instance\n"
      ],
      "metadata": {
        "id": "2oWAF7HUwgV0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Step 2 - Dataset preprocessing"
      ],
      "metadata": {
        "id": "CdBpJXBdBINR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "# Set the index for multi-indexing\n",
        "df = df.set_index(['instances', 'timepoints'])\n",
        "\n",
        "# Sort the dataframe if not already sorted\n",
        "df.sort_index(inplace=True)"
      ],
      "metadata": {
        "id": "vXMGBGGAt_eg"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the number of instances and timepoints\n",
        "num_instances = df.index.get_level_values(0).nunique()\n",
        "num_timepoints = df.index.get_level_values(1).nunique()"
      ],
      "metadata": {
        "id": "h02AiASkzBG7"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Number of instances:\", num_instances)\n",
        "print(\"Number of timepoints:\", num_timepoints)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hgyz8h9YSd2W",
        "outputId": "68662753-55a1-472c-92e6-22ab70bdfed7"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of instances: 100\n",
            "Number of timepoints: 119\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Drop the 'label' column to get the features\n",
        "X_features = df.drop(columns='label')"
      ],
      "metadata": {
        "id": "RrdUA3NAy61q"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_features"
      ],
      "metadata": {
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          "height": 455
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        "id": "XmBEQniny8nk",
        "outputId": "acfd5040-28fe-4a59-f640-693e35abd2a4"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "                      var_0  var_1  var_2  var_3  var_4  var_5\n",
              "instances timepoints                                          \n",
              "1         0             0.0   10.0    0.0   10.0      2      6\n",
              "          1             0.0   10.0    0.0    0.0      1      6\n",
              "          2             0.0   10.0    0.0    0.0      1      6\n",
              "          3             0.0   10.0    0.0    0.0      1      6\n",
              "          4             0.0   10.0    0.0    0.0      1      6\n",
              "...                     ...    ...    ...    ...    ...    ...\n",
              "100       114          -5.0   -5.0   -5.0    0.0      1      6\n",
              "          115          -5.0   -5.0   -5.0    0.0      1      6\n",
              "          116          -5.0   -5.0   -5.0    0.0      1      6\n",
              "          117          -5.0   -5.0   -5.0    0.0      1      6\n",
              "          118          -5.0   -5.0   -5.0    0.0      1      6\n",
              "\n",
              "[11900 rows x 6 columns]"
            ],
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              "      <th>114</th>\n",
              "      <td>-5.0</td>\n",
              "      <td>-5.0</td>\n",
              "      <td>-5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>115</th>\n",
              "      <td>-5.0</td>\n",
              "      <td>-5.0</td>\n",
              "      <td>-5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>116</th>\n",
              "      <td>-5.0</td>\n",
              "      <td>-5.0</td>\n",
              "      <td>-5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117</th>\n",
              "      <td>-5.0</td>\n",
              "      <td>-5.0</td>\n",
              "      <td>-5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>118</th>\n",
              "      <td>-5.0</td>\n",
              "      <td>-5.0</td>\n",
              "      <td>-5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>11900 rows × 6 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-01e9f251-681c-40d2-8f79-0354f77a97b3')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-01e9f251-681c-40d2-8f79-0354f77a97b3 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-01e9f251-681c-40d2-8f79-0354f77a97b3');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-b83d9cfc-9fa5-4562-9fd0-a0e2d0c57853\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b83d9cfc-9fa5-4562-9fd0-a0e2d0c57853')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      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",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      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",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b83d9cfc-9fa5-4562-9fd0-a0e2d0c57853 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X_features",
              "summary": "{\n  \"name\": \"X_features\",\n  \"rows\": 11900,\n  \"fields\": [\n    {\n      \"column\": \"var_0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.8515572620515386,\n        \"min\": -5.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 273,\n        \"samples\": [\n          -4.53,\n          0.57,\n          1.01\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"var_1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.3002126561861616,\n        \"min\": -10.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 334,\n        \"samples\": [\n          -7.81,\n          3.95,\n          -1.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"var_2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.846072377032869,\n        \"min\": -5.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 277,\n        \"samples\": [\n          -1.19,\n          -2.06,\n          2.84\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"var_3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.62168892712602,\n        \"min\": -10.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 338,\n        \"samples\": [\n          5.02,\n          -3.0,\n          -1.76\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"var_4\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1,\n          4,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"var_5\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 6,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          6,\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Print the shapes\n",
        "print(\"Shape before reshaping:\", X_features.shape)\n",
        "print(\"Total expected size:\", num_instances * 6 * num_timepoints)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-SDscXubzEo-",
        "outputId": "d921324f-3daa-42fd-b981-d5203dd210a4"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape before reshaping: (11900, 6)\n",
            "Total expected size: 71400\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the data into features (X) and labels (y):\n",
        "# For this demonstration, there are 6 variables from var_0 to var_5.\n",
        "X = df.drop(columns='label').to_numpy().reshape(-1, 6, df.index.levels[1].size)\n",
        "y = df['label'].to_numpy()"
      ],
      "metadata": {
        "id": "DpEHwQ8hxeqr"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Ensure labels are unique per instance and not per time point\n",
        "# Reshape features (X) into a 3D numpy array\n",
        "_, idx = np.unique(df.index.get_level_values(0), return_index=True)\n",
        "y = y[idx]"
      ],
      "metadata": {
        "id": "6T50m8oEyFn7"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B3qw3BA4yUZP",
        "outputId": "e2a41bd7-ed35-4cbd-f461-261199093839"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[ 0.  , 10.  ,  0.  , ...,  0.  ,  0.  ,  1.  ],\n",
              "        [ 6.  ,  0.  , 10.  , ..., 10.  ,  0.  ,  0.  ],\n",
              "        [ 1.  ,  6.  ,  0.  , ...,  0.  , 10.  ,  0.  ],\n",
              "        [ 0.  ,  1.  ,  6.  , ...,  6.  ,  0.  , 10.  ],\n",
              "        [ 0.  ,  0.  ,  1.  , ...,  1.  ,  6.  ,  0.  ],\n",
              "        [10.  ,  0.  ,  0.  , ...,  0.  ,  1.  ,  6.  ]],\n",
              "\n",
              "       [[ 0.  ,  7.19,  0.  , ...,  0.  ,  0.  ,  1.  ],\n",
              "        [ 6.  ,  0.  ,  7.19, ...,  7.19,  0.  ,  0.  ],\n",
              "        [ 1.  ,  6.  ,  0.  , ...,  0.  ,  7.19,  0.  ],\n",
              "        [ 0.  ,  1.  ,  6.  , ...,  6.  ,  0.  ,  7.19],\n",
              "        [ 0.  ,  0.  ,  1.  , ...,  1.  ,  6.  ,  0.  ],\n",
              "        [ 7.19,  0.  ,  0.  , ...,  0.  ,  1.  ,  6.  ]],\n",
              "\n",
              "       [[ 0.  ,  7.5 ,  0.  , ...,  0.  ,  0.  ,  1.  ],\n",
              "        [ 6.  ,  0.  ,  7.5 , ...,  7.5 ,  0.  ,  0.  ],\n",
              "        [ 1.  ,  6.  ,  0.  , ...,  0.  ,  7.5 ,  0.  ],\n",
              "        [ 0.  ,  1.  ,  6.  , ...,  6.  ,  0.  ,  7.5 ],\n",
              "        [ 0.  ,  0.  ,  1.  , ...,  1.  ,  6.  ,  0.  ],\n",
              "        [ 7.5 ,  0.  ,  0.  , ...,  0.  ,  1.  ,  6.  ]],\n",
              "\n",
              "       ...,\n",
              "\n",
              "       [[-4.31, -4.31, -4.31, ..., -4.31,  0.  ,  1.  ],\n",
              "        [ 5.  , -4.31, -4.31, ..., -4.31, -4.31,  0.  ],\n",
              "        [ 1.  ,  5.  , -4.31, ..., -4.31, -4.31, -4.31],\n",
              "        [ 0.  ,  1.  ,  5.  , ...,  5.  , -4.31, -4.31],\n",
              "        [-4.31,  0.  ,  1.  , ...,  1.  ,  5.  , -4.31],\n",
              "        [-4.31, -4.31,  0.  , ...,  0.  ,  1.  ,  5.  ]],\n",
              "\n",
              "       [[-4.58, -4.57, -4.57, ..., -4.57,  0.  ,  1.  ],\n",
              "        [ 5.  , -4.58, -4.57, ..., -4.57, -4.57,  0.  ],\n",
              "        [ 1.  ,  5.  , -4.58, ..., -4.58, -4.57, -4.57],\n",
              "        [ 0.  ,  1.  ,  5.  , ...,  5.  , -4.58, -4.57],\n",
              "        [-4.57,  0.  ,  1.  , ...,  1.  ,  5.  , -4.58],\n",
              "        [-4.57, -4.57,  0.  , ...,  0.  ,  1.  ,  5.  ]],\n",
              "\n",
              "       [[-5.  , -5.  , -5.  , ..., -5.  ,  0.  ,  1.  ],\n",
              "        [ 6.  , -5.  , -5.  , ..., -5.  , -5.  ,  0.  ],\n",
              "        [ 1.  ,  6.  , -5.  , ..., -5.  , -5.  , -5.  ],\n",
              "        [ 0.  ,  1.  ,  6.  , ...,  6.  , -5.  , -5.  ],\n",
              "        [-5.  ,  0.  ,  1.  , ...,  1.  ,  6.  , -5.  ],\n",
              "        [-5.  , -5.  ,  0.  , ...,  0.  ,  1.  ,  6.  ]]])"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S1xA0td54ols",
        "outputId": "6383aae8-d10d-49fd-82bb-7077b835f27b"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(100, 6, 119)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "X is 3D array and should have the shape [number of samples, number of variables, sequence length].\n",
        "\n",
        "Each of the 100 instances is a 2D array of 6 variables over 119 timepoints"
      ],
      "metadata": {
        "id": "gYwhDTNC3ud4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "779zN9rgySPJ",
        "outputId": "d38e4747-a76b-46c8-c082-906c907e8ba6"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 8,  8,  8,  8,  8,  8,  8,  8,  8,  8,  5,  5,  5,  5,  5,  5,  5,\n",
              "        5,  5,  5,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  7,  7,  7,  7,\n",
              "        7,  7,  7,  7,  7,  7,  6,  6,  6,  6,  6,  6,  6,  6,  6,  6, 10,\n",
              "       10, 10, 10, 10, 10, 10, 10, 10, 10,  1,  1,  1,  1,  1,  1,  1,  1,\n",
              "        1,  1,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  2,  2,  2,  2,  2,\n",
              "        2,  2,  2,  2,  2,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4])"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XV_XNuR-4rbO",
        "outputId": "7c335c77-ad23-478c-8c35-114061ec28cd"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(100,)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "y is 1D numpy array with the shape [number of samples]."
      ],
      "metadata": {
        "id": "V9kWfwbQ4BPr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the data into a training set and a test set\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=3, stratify=y)\n",
        "\n"
      ],
      "metadata": {
        "id": "wPCuaiZk8tzZ"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dzMb3n9b81x4",
        "outputId": "f03e36cc-0dd1-496a-d9e1-ebc31101cfd3"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[ 0.  ,  0.  ,  0.  , ...,  1.75, -0.86,  4.  ],\n",
              "        [ 3.  ,  1.75,  1.75, ...,  2.08,  2.08,  0.  ],\n",
              "        [ 1.  ,  3.  ,  2.09, ...,  2.09,  2.08,  2.08],\n",
              "        [ 0.  ,  1.  ,  3.  , ...,  3.  ,  2.09,  2.08],\n",
              "        [ 2.08,  0.  ,  1.  , ...,  1.  ,  3.  ,  2.09],\n",
              "        [ 2.08,  2.08,  0.  , ...,  0.  ,  1.  ,  3.  ]],\n",
              "\n",
              "       [[ 0.  ,  5.63,  0.  , ...,  0.  ,  0.  ,  1.  ],\n",
              "        [ 2.  ,  0.  ,  5.63, ...,  5.63,  0.  ,  0.  ],\n",
              "        [ 1.  ,  2.  ,  0.  , ...,  0.  ,  5.63,  0.  ],\n",
              "        [ 0.  ,  1.  ,  2.  , ...,  2.  ,  0.  ,  5.63],\n",
              "        [ 0.  ,  0.  ,  1.  , ...,  1.  ,  2.  ,  0.  ],\n",
              "        [ 5.63,  0.  ,  0.  , ...,  0.  ,  1.  ,  2.  ]],\n",
              "\n",
              "       [[ 4.42,  4.41,  4.41, ...,  4.41,  0.  ,  1.  ],\n",
              "        [ 1.  ,  4.42,  4.41, ...,  4.41,  4.41,  0.  ],\n",
              "        [ 1.  ,  1.  ,  4.42, ...,  4.42,  4.41,  4.41],\n",
              "        [ 0.  ,  1.  ,  1.  , ...,  1.  ,  4.42,  4.41],\n",
              "        [ 4.41,  0.  ,  1.  , ...,  1.  ,  1.  ,  4.42],\n",
              "        [ 4.41,  4.41,  0.  , ...,  0.  ,  1.  ,  1.  ]],\n",
              "\n",
              "       ...,\n",
              "\n",
              "       [[-2.58, -2.57, -2.57, ..., -2.57,  0.  ,  1.  ],\n",
              "        [ 5.  , -2.58, -2.57, ..., -2.57, -2.57,  0.  ],\n",
              "        [ 1.  ,  5.  , -2.58, ..., -2.58, -2.57, -2.57],\n",
              "        [ 0.  ,  1.  ,  5.  , ...,  5.  , -2.58, -2.57],\n",
              "        [-2.57,  0.  ,  1.  , ...,  1.  ,  5.  , -2.58],\n",
              "        [-2.57, -2.57,  0.  , ...,  0.  ,  1.  ,  5.  ]],\n",
              "\n",
              "       [[ 0.  ,  8.75,  0.  , ...,  0.  ,  0.  ,  1.  ],\n",
              "        [ 6.  ,  0.  ,  8.75, ...,  8.75,  0.  ,  0.  ],\n",
              "        [ 1.  ,  6.  ,  0.  , ...,  0.  ,  8.75,  0.  ],\n",
              "        [ 0.  ,  1.  ,  6.  , ...,  6.  ,  0.  ,  8.75],\n",
              "        [ 0.  ,  0.  ,  1.  , ...,  1.  ,  6.  ,  0.  ],\n",
              "        [ 8.75,  0.  ,  0.  , ...,  0.  ,  1.  ,  6.  ]],\n",
              "\n",
              "       [[ 0.  ,  0.  ,  0.  , ...,  0.  ,  0.  ,  1.  ],\n",
              "        [ 1.  ,  0.  ,  2.19, ...,  5.62,  0.  ,  0.  ],\n",
              "        [ 4.  ,  2.  ,  0.  , ...,  0.  ,  7.81,  0.  ],\n",
              "        [ 0.  ,  4.  ,  6.  , ...,  6.  ,  0.  ,  7.5 ],\n",
              "        [ 0.  ,  0.  ,  4.  , ...,  4.  ,  6.  ,  0.  ],\n",
              "        [ 7.19,  0.  ,  0.  , ...,  0.  ,  4.  ,  3.  ]]])"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "91qL0FxF834a",
        "outputId": "82f88ef6-bd26-48e8-cd85-32ae7d79563c"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[  4.59,   4.59,   4.59, ...,   4.59,   0.  ,   1.  ],\n",
              "        [  1.  ,   4.59,   4.59, ...,   4.59,   4.59,   0.  ],\n",
              "        [  1.  ,   1.  ,   4.59, ...,   4.59,   4.59,   4.59],\n",
              "        [  0.  ,   1.  ,   1.  , ...,   1.  ,   4.59,   4.59],\n",
              "        [  4.59,   0.  ,   1.  , ...,   1.  ,   1.  ,   4.59],\n",
              "        [  4.59,   4.59,   0.  , ...,   0.  ,   1.  ,   1.  ]],\n",
              "\n",
              "       [[  1.4 ,   1.39,   1.39, ...,   1.39,   0.  ,   1.  ],\n",
              "        [  3.  ,   1.4 ,   1.39, ...,   1.39,   1.39,   0.  ],\n",
              "        [  1.  ,   3.  ,   1.4 , ...,   1.4 ,   1.39,   1.39],\n",
              "        [  0.  ,   1.  ,   3.  , ...,   3.  ,   1.4 ,   1.39],\n",
              "        [  1.39,   0.  ,   1.  , ...,   1.  ,   3.  ,   1.4 ],\n",
              "        [  1.39,   1.39,   0.  , ...,   0.  ,   1.  ,   3.  ]],\n",
              "\n",
              "       [[  0.  ,  -2.5 ,   0.  , ...,   0.  ,   0.  ,   1.  ],\n",
              "        [  5.  ,   0.  ,  -2.5 , ...,  -2.5 ,   0.  ,   0.  ],\n",
              "        [  1.  ,   5.  ,   0.  , ...,   0.  ,  -2.5 ,   0.  ],\n",
              "        [  0.  ,   1.  ,   5.  , ...,   5.  ,   0.  ,  -2.5 ],\n",
              "        [  0.  ,   0.  ,   1.  , ...,   1.  ,   5.  ,   0.  ],\n",
              "        [ -2.5 ,   0.  ,   0.  , ...,   0.  ,   1.  ,   5.  ]],\n",
              "\n",
              "       ...,\n",
              "\n",
              "       [[  0.  ,   0.  ,   0.  , ...,   1.64,  -0.45,   3.  ],\n",
              "        [  3.  ,   1.52,   1.51, ...,   3.3 ,   3.3 ,   0.  ],\n",
              "        [  1.  ,   1.  ,   1.9 , ...,   1.91,   1.9 ,   1.9 ],\n",
              "        [  0.72,   2.  ,   3.  , ...,   3.  ,   1.31,   1.31],\n",
              "        [  1.31,   0.  ,   1.  , ...,   4.  ,   1.  ,   5.  ],\n",
              "        [  5.  ,   5.  ,   0.  , ...,   0.  ,   4.  ,   3.  ]],\n",
              "\n",
              "       [[ -0.73,  -0.73,  -0.73, ...,  -0.73,   0.  ,   1.  ],\n",
              "        [  5.  ,  -0.73,  -0.73, ...,  -0.73,  -0.73,   0.  ],\n",
              "        [  1.  ,   5.  ,  -0.73, ...,  -0.73,  -0.73,  -0.73],\n",
              "        [  0.  ,   1.  ,   5.  , ...,   5.  ,  -0.73,  -0.73],\n",
              "        [ -0.73,   0.  ,   1.  , ...,   1.  ,   5.  ,  -0.73],\n",
              "        [ -0.73,  -0.73,   0.  , ...,   0.  ,   1.  ,   5.  ]],\n",
              "\n",
              "       [[  0.  , -10.  ,   0.  , ...,   0.  ,   0.  ,   1.  ],\n",
              "        [  6.  ,   0.  , -10.  , ..., -10.  ,   0.  ,   0.  ],\n",
              "        [  1.  ,   6.  ,   0.  , ...,   0.  , -10.  ,   0.  ],\n",
              "        [  0.  ,   1.  ,   6.  , ...,   6.  ,   0.  , -10.  ],\n",
              "        [  0.  ,   0.  ,   1.  , ...,   1.  ,   6.  ,   0.  ],\n",
              "        [-10.  ,   0.  ,   0.  , ...,   0.  ,   1.  ,   6.  ]]])"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HkvWFSbW86PI",
        "outputId": "a8c037fe-499a-4309-8bbe-03a5306fb605"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 3,  7,  1,  1,  6,  2,  4,  9, 10, 10,  5, 10,  1,  7,  6,  8,  9,\n",
              "        8,  8,  3,  9,  6,  2,  4, 10,  7,  1,  5,  6,  4,  2, 10,  5,  4,\n",
              "        3, 10,  6,  4,  1,  3,  7,  6,  1, 10,  5,  8,  3,  8,  7,  1,  2,\n",
              "        3,  9,  2,  3,  2,  9,  4,  8,  7,  5,  2,  7,  5,  5,  6,  9,  4,\n",
              "        8,  9])"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ryggyviH889f",
        "outputId": "6dfa6483-74f9-4b85-ab84-b65a65b002d3"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 1,  2, 10, 10,  7,  5,  5,  6,  2,  7,  2,  8,  4,  3,  4,  8,  6,\n",
              "        1,  8,  3,  1,  9,  6,  5,  7,  9,  9,  3,  4, 10])"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Step 3 - Train the model"
      ],
      "metadata": {
        "id": "kpIDoyqlBZwS"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "vPgM4MZ6HTrW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "db0b1b0f-5ff5-46e5-d0c9-fbe71b2ddb87"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting sktime\n",
            "  Downloading sktime-0.27.0-py3-none-any.whl (21.9 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.9/21.9 MB\u001b[0m \u001b[31m7.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy<1.27,>=1.21 in /usr/local/lib/python3.10/dist-packages (from sktime) (1.25.2)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from sktime) (24.0)\n",
            "Requirement already satisfied: pandas<2.2.0,>=1.1 in /usr/local/lib/python3.10/dist-packages (from sktime) (1.5.3)\n",
            "Collecting scikit-base<0.8.0 (from sktime)\n",
            "  Downloading scikit_base-0.7.5-py3-none-any.whl (128 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m128.8/128.8 kB\u001b[0m \u001b[31m5.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: scikit-learn<1.5.0,>=0.24 in /usr/local/lib/python3.10/dist-packages (from sktime) (1.2.2)\n",
            "Requirement already satisfied: scipy<2.0.0,>=1.2 in /usr/local/lib/python3.10/dist-packages (from sktime) (1.11.4)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas<2.2.0,>=1.1->sktime) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas<2.2.0,>=1.1->sktime) (2023.4)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn<1.5.0,>=0.24->sktime) (1.3.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn<1.5.0,>=0.24->sktime) (3.3.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas<2.2.0,>=1.1->sktime) (1.16.0)\n",
            "Installing collected packages: scikit-base, sktime\n",
            "Successfully installed scikit-base-0.7.5 sktime-0.27.0\n"
          ]
        }
      ],
      "source": [
        "# Install the sktime library\n",
        "!pip install sktime\n",
        "import sktime"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check in memory type is correct for label y\n",
        "from sktime.datatypes import check_is_mtype\n",
        "check_is_mtype(y, mtype=\"np.ndarray\", return_metadata=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6uNDKB0MlQmM",
        "outputId": "90314e66-47b7-4bd7-e1df-d0ece6d60e5a"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(True,\n",
              " None,\n",
              " {'is_empty': False,\n",
              "  'is_univariate': True,\n",
              "  'n_features': 1,\n",
              "  'feature_names': [0],\n",
              "  'is_equally_spaced': True,\n",
              "  'has_nans': False,\n",
              "  'mtype': 'np.ndarray',\n",
              "  'scitype': 'Series'})"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import seaborn as sns\n",
        "\n",
        "from sktime.classification.deep_learning.cnn import CNNClassifier\n",
        "\n",
        "sns.set_style(\"whitegrid\")"
      ],
      "metadata": {
        "id": "xl-rBAr1ngy-"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize the LSTMFCNClassifier\n",
        "classifier = CNNClassifier()"
      ],
      "metadata": {
        "id": "z5z5vbaX7mxL"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fit the classifier on the training data\n",
        "classifier.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "73pvrI4K9M1Z",
        "outputId": "518210ce-fc81-47cc-bf64-17ad517e6fb9"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CNNClassifier()"
            ],
            "text/html": [
              "<style>#sk-a0126627-67b4-496c-b37f-cf442f17778a {color: black;background-color: white;}#sk-a0126627-67b4-496c-b37f-cf442f17778a pre{padding: 0;}#sk-a0126627-67b4-496c-b37f-cf442f17778a div.sk-toggleable {background-color: white;}#sk-a0126627-67b4-496c-b37f-cf442f17778a label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-a0126627-67b4-496c-b37f-cf442f17778a label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-a0126627-67b4-496c-b37f-cf442f17778a label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-a0126627-67b4-496c-b37f-cf442f17778a div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-a0126627-67b4-496c-b37f-cf442f17778a div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-a0126627-67b4-496c-b37f-cf442f17778a div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-a0126627-67b4-496c-b37f-cf442f17778a input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-a0126627-67b4-496c-b37f-cf442f17778a input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-a0126627-67b4-496c-b37f-cf442f17778a div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-a0126627-67b4-496c-b37f-cf442f17778a div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-a0126627-67b4-496c-b37f-cf442f17778a 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-a0126627-67b4-496c-b37f-cf442f17778a 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-a0126627-67b4-496c-b37f-cf442f17778a div.sk-estimator:hover {background-color: #d4ebff;}#sk-a0126627-67b4-496c-b37f-cf442f17778a div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-a0126627-67b4-496c-b37f-cf442f17778a div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-a0126627-67b4-496c-b37f-cf442f17778a div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-a0126627-67b4-496c-b37f-cf442f17778a div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-a0126627-67b4-496c-b37f-cf442f17778a div.sk-item {z-index: 1;}#sk-a0126627-67b4-496c-b37f-cf442f17778a div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-a0126627-67b4-496c-b37f-cf442f17778a div.sk-parallel::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-a0126627-67b4-496c-b37f-cf442f17778a div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-a0126627-67b4-496c-b37f-cf442f17778a div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-a0126627-67b4-496c-b37f-cf442f17778a div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-a0126627-67b4-496c-b37f-cf442f17778a div.sk-parallel-item:only-child::after {width: 0;}#sk-a0126627-67b4-496c-b37f-cf442f17778a 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;position: relative;}#sk-a0126627-67b4-496c-b37f-cf442f17778a div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-a0126627-67b4-496c-b37f-cf442f17778a div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-a0126627-67b4-496c-b37f-cf442f17778a 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-a0126627-67b4-496c-b37f-cf442f17778a div.sk-text-repr-fallback {display: none;}</style><div id='sk-a0126627-67b4-496c-b37f-cf442f17778a' class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>CNNClassifier()</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class=\"sk-container\" hidden><div class='sk-item'><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=UUID('22b306f9-97d4-410d-8583-953ed4541524') type=\"checkbox\" checked><label for=UUID('22b306f9-97d4-410d-8583-953ed4541524') class='sk-toggleable__label sk-toggleable__label-arrow'>CNNClassifier</label><div class=\"sk-toggleable__content\"><pre>CNNClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Step 4 - Prediction and Performance Analysis"
      ],
      "metadata": {
        "id": "miyb1TEaB1MW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Predict the labels for the test set\n",
        "y_pred = classifier.predict(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2FmxuIF49q0g",
        "outputId": "cb8894eb-b69a-4825-9996-e06edb5224b5"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2/2 [==============================] - 0s 7ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate accuracy\n",
        "from sklearn.metrics import accuracy_score\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(\"Accuracy:\", accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qef6dbel_BsF",
        "outputId": "23edb20e-61d6-421b-e2cf-e1bc68673386"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The scenario (being the label (y) to be predicted) include:\n",
        "\n",
        "*   Label #1 - 'Sensors_NormalSpeed': use potentiometer with normal conveyor speed.\n",
        "*   Label #2 - 'Sensors_TooSlowOrStop':use potentiometer with conveyor speed too slow or stopped.\n",
        "*   Label #3 - 'Sensors_Oscillate': use potentiometer with conveyor speed up and down illogically.\n",
        "*   Label #4 - 'Sensors_WrongDirection': use potentiometer with conveyor going the reverse direction.\n",
        "*   Label #5 - 'ForcedActuator_Normal': force actuator value with normal conveyor speed.\n",
        "*   Label #6 - 'ForcedActuator_TooSlowOrStop': force actuator value with conveyor speed too slow or stopped.\n",
        "*   Label #7 - 'ForcedActuator_TooFast': force actuator value with conveyor speed too fast.\n",
        "*   Label #8 - 'ForcedActuator_ExtremelyFast': force actuator value with conveyor speed extremely fast.\n",
        "*   Label #9 - 'ForcedActuator_Oscillate': force actuator value with conveyor speed up and down illogically.\n",
        "*   Label #10 - 'ForcedActuator_WrongDirection': force actuator value with conveyor going the reverse direction."
      ],
      "metadata": {
        "id": "HFJHWKzZCRI5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Check classification_report\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "report = classification_report(y_test, y_pred, output_dict=True)\n",
        "\n",
        "print(classification_report(y_test, y_pred))\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q86X6XQjsUsX",
        "outputId": "43b88fed-d103-4971-c65c-9061c831378e"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       1.00      1.00      1.00         3\n",
            "           2       0.75      1.00      0.86         3\n",
            "           3       1.00      0.67      0.80         3\n",
            "           4       0.75      1.00      0.86         3\n",
            "           5       0.75      1.00      0.86         3\n",
            "           6       1.00      1.00      1.00         3\n",
            "           7       1.00      1.00      1.00         3\n",
            "           8       1.00      1.00      1.00         3\n",
            "           9       1.00      0.33      0.50         3\n",
            "          10       1.00      1.00      1.00         3\n",
            "\n",
            "    accuracy                           0.90        30\n",
            "   macro avg       0.93      0.90      0.89        30\n",
            "weighted avg       0.93      0.90      0.89        30\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check confusion matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "conf_matrix = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "plt.figure(figsize=(10, 8))\n",
        "sns.heatmap(conf_matrix, annot=True, fmt='g', cmap=plt.cm.Blues)\n",
        "plt.xlabel('Predicted label')\n",
        "plt.ylabel('True label')\n",
        "plt.title('Confusion Matrix')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "id": "Gr5pMoJ891kh",
        "outputId": "1b38eb1d-4857-4141-8dba-be6ddeeb6d13"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Step 5 - Save and load model"
      ],
      "metadata": {
        "id": "iPpWXLR_BxAv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Save model using pickle\n",
        "import pickle\n",
        "\n",
        "model_filename = '/content/FactoryIO_CNN.pkl'\n",
        "\n",
        "with open(model_filename, 'wb') as file:\n",
        "    pickle.dump(classifier, file)"
      ],
      "metadata": {
        "id": "MQtnF6U9DEya"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Reminder: download the model from Google Colab file directory."
      ],
      "metadata": {
        "id": "ZpHwwKQYDymv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the model\n",
        "with open(model_filename, 'rb') as file:\n",
        "    loaded_model = pickle.load(file)"
      ],
      "metadata": {
        "id": "a4IlLdKdDmCn"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The End"
      ],
      "metadata": {
        "id": "8j3JApAf96cU"
      }
    }
  ]
}