Create_RNN_Data_Dict.ipynb

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      "cell_type": "code",
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      "source": [
        "from google.colab import drive\n",
        "from typing import Dict, Iterator, List, Sequence, Union\n",
        "import os\n",
        "import json\n",
        "import pandas as pd\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.set_option('display.max_columns', None)"
      ],
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        "id": "jwqLCIBVdzR_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 0. Load Data From Drive"
      ],
      "metadata": {
        "id": "PEED9bjid1RY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "drive.mount('/content/drive/')"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oU0ROuNkd02_",
        "outputId": "ff99f615-cd21-49c1-973a-39b9919000ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive/\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "DATA_DIR = '/content/drive/My Drive/NEU/Graduate/CS_6140/Data/'\n",
        "SAVE_DATA_DIR = '/content/drive/My Drive/NEU/Graduate/CS_6140/Data/features_data/'\n",
        "PREPROCESSED_DATA_F = 'preprocessed_data_variable_hour_window_cols.csv'\n",
        "PREPROCESSED_DATA_LOC = os.path.join(DATA_DIR, PREPROCESSED_DATA_F)"
      ],
      "metadata": {
        "id": "2XW4pLe7d05I"
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      "execution_count": null,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\n",
        "    PREPROCESSED_DATA_LOC,\n",
        ")\n",
        "print(f\"- {format(len(df), ',')} total rows in df.\")\n",
        "df.head(3)"
      ],
      "metadata": {
        "colab": {
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          "height": 285
        },
        "id": "huJ7DUc8d07N",
        "outputId": "29d1fd5a-3395-44db-be34-57ac8eca2101"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "- 876,100 total rows in df.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              datetime  machineID        volt      rotate    pressure  \\\n",
              "0  2015-01-01 06:00:00          1  176.217853  418.504078  113.077935   \n",
              "1  2015-01-01 07:00:00          1  162.879223  402.747490   95.460525   \n",
              "2  2015-01-01 08:00:00          1  170.989902  527.349825   75.237905   \n",
              "\n",
              "   vibration  12_machineID_time_window  12_order_in_time_window  \\\n",
              "0  45.087686                         1                        1   \n",
              "1  43.413973                         1                        2   \n",
              "2  34.178847                         1                        3   \n",
              "\n",
              "  12_machine_example_ID       12_step_ID  24_machineID_time_window  \\\n",
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              "2            (001, 001)  (001, 001, 003)                         1   \n",
              "\n",
              "   24_order_in_time_window 24_machine_example_ID       24_step_ID  \\\n",
              "0                        1            (001, 001)  (001, 001, 001)   \n",
              "1                        2            (001, 001)  (001, 001, 002)   \n",
              "2                        3            (001, 001)  (001, 001, 003)   \n",
              "\n",
              "   48_machineID_time_window  48_order_in_time_window 48_machine_example_ID  \\\n",
              "0                         1                        1            (001, 001)   \n",
              "1                         1                        2            (001, 001)   \n",
              "2                         1                        3            (001, 001)   \n",
              "\n",
              "        48_step_ID  72_machineID_time_window  72_order_in_time_window  \\\n",
              "0  (001, 001, 001)                         1                        1   \n",
              "1  (001, 001, 002)                         1                        2   \n",
              "2  (001, 001, 003)                         1                        3   \n",
              "\n",
              "  72_machine_example_ID       72_step_ID  comp1_maint  comp2_maint  \\\n",
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              "\n",
              "   comp3_maint  comp4_maint  error1  error2  error3  error4  error5  \\\n",
              "0            0            0       0       0       0       0       0   \n",
              "1            0            0       0       0       0       0       0   \n",
              "2            0            0       0       0       0       0       0   \n",
              "\n",
              "   comp1_failure  comp2_failure  comp3_failure  comp4_failure  age  model2  \\\n",
              "0              0              0              0              0   18   False   \n",
              "1              0              0              0              0   18   False   \n",
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              "\n",
              "   model3  model4  \n",
              "0    True   False  \n",
              "1    True   False  \n",
              "2    True   False  "
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              "      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-a69a1e1e-513c-44cf-baf0-9cae38486e3a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df[\n",
        "    [\n",
        "        'datetime', 'machineID',\n",
        "        '12_machineID_time_window', '12_order_in_time_window', '12_machine_example_ID', '12_step_ID',\n",
        "        '24_machineID_time_window', '24_order_in_time_window', '24_machine_example_ID', '24_step_ID',\n",
        "        '48_machineID_time_window', '48_order_in_time_window', '48_machine_example_ID', '48_step_ID',\n",
        "        '72_machineID_time_window', '72_order_in_time_window', '72_machine_example_ID', '72_step_ID',\n",
        "        'age',\n",
        "        'model2', 'model3', 'model4',\n",
        "        'volt', 'rotate', 'pressure', 'vibration',\n",
        "        'comp1_maint', 'comp2_maint', 'comp3_maint', 'comp4_maint',\n",
        "        'error1', 'error2', 'error3', 'error4', 'error5',\n",
        "        'comp1_failure', 'comp2_failure', 'comp3_failure', 'comp4_failure',\n",
        "    ]\n",
        "]"
      ],
      "metadata": {
        "id": "GnepiZQIdzUG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['datetime'] = pd.to_datetime(df['datetime'])\n",
        "df['machineID'] = df['machineID'].astype(int)\n",
        "\n",
        "df['12_machineID_time_window'] = df['12_machineID_time_window'].astype(int)\n",
        "df['12_order_in_time_window'] = df['12_order_in_time_window'].astype(int)\n",
        "df['12_machine_example_ID'] = df['12_machine_example_ID'].astype(str)\n",
        "df['12_step_ID'] = df['12_step_ID'].astype(str)\n",
        "df['24_machineID_time_window'] = df['24_machineID_time_window'].astype(int)\n",
        "df['24_order_in_time_window'] = df['24_order_in_time_window'].astype(int)\n",
        "df['24_machine_example_ID'] = df['24_machine_example_ID'].astype(str)\n",
        "df['24_step_ID'] = df['24_step_ID'].astype(str)\n",
        "df['48_machineID_time_window'] = df['48_machineID_time_window'].astype(int)\n",
        "df['48_order_in_time_window'] = df['48_order_in_time_window'].astype(int)\n",
        "df['48_machine_example_ID'] = df['48_machine_example_ID'].astype(str)\n",
        "df['48_step_ID'] = df['48_step_ID'].astype(str)\n",
        "df['72_machineID_time_window'] = df['72_machineID_time_window'].astype(int)\n",
        "df['72_order_in_time_window'] = df['72_order_in_time_window'].astype(int)\n",
        "df['72_machine_example_ID'] = df['72_machine_example_ID'].astype(str)\n",
        "df['72_step_ID'] = df['72_step_ID'].astype(str)\n",
        "\n",
        "df['age'] = df['age'].astype(int)\n",
        "df['model2'] = df['model2'].astype(int)\n",
        "df['model3'] = df['model3'].astype(int)\n",
        "df['model4'] = df['model4'].astype(int)\n",
        "\n",
        "df['volt'] = df['volt'].astype(float)\n",
        "df['rotate'] = df['rotate'].astype(float)\n",
        "df['pressure'] = df['pressure'].astype(float)\n",
        "df['vibration'] = df['vibration'].astype(float)\n",
        "df['comp1_maint'] = df['comp1_maint'].astype(int)\n",
        "df['comp2_maint'] = df['comp2_maint'].astype(int)\n",
        "df['comp3_maint'] = df['comp3_maint'].astype(int)\n",
        "df['comp4_maint'] = df['comp4_maint'].astype(int)\n",
        "df['error1'] = df['error1'].astype(int)\n",
        "df['error2'] = df['error2'].astype(int)\n",
        "df['error3'] = df['error3'].astype(int)\n",
        "df['error4'] = df['error4'].astype(int)\n",
        "df['error5'] = df['error5'].astype(int)\n",
        "df['comp1_failure'] = df['comp1_failure'].astype(int)\n",
        "df['comp2_failure'] = df['comp2_failure'].astype(int)\n",
        "df['comp3_failure'] = df['comp3_failure'].astype(int)\n",
        "df['comp4_failure'] = df['comp4_failure'].astype(int)"
      ],
      "metadata": {
        "id": "AyU5ip6BeA0p"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1. Create One Feature File per Feature Time Window Length"
      ],
      "metadata": {
        "id": "E9IahsC4d7fB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "id": "e1LBNsEWf-EL",
        "outputId": "2f576e08-4b72-4e8a-9843-0c7d8ebfc0a8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             datetime  machineID  12_machineID_time_window  \\\n",
              "0 2015-01-01 06:00:00          1                         1   \n",
              "1 2015-01-01 07:00:00          1                         1   \n",
              "2 2015-01-01 08:00:00          1                         1   \n",
              "3 2015-01-01 09:00:00          1                         1   \n",
              "\n",
              "   12_order_in_time_window 12_machine_example_ID       12_step_ID  \\\n",
              "0                        1            (001, 001)  (001, 001, 001)   \n",
              "1                        2            (001, 001)  (001, 001, 002)   \n",
              "2                        3            (001, 001)  (001, 001, 003)   \n",
              "3                        4            (001, 001)  (001, 001, 004)   \n",
              "\n",
              "   24_machineID_time_window  24_order_in_time_window 24_machine_example_ID  \\\n",
              "0                         1                        1            (001, 001)   \n",
              "1                         1                        2            (001, 001)   \n",
              "2                         1                        3            (001, 001)   \n",
              "3                         1                        4            (001, 001)   \n",
              "\n",
              "        24_step_ID  48_machineID_time_window  48_order_in_time_window  \\\n",
              "0  (001, 001, 001)                         1                        1   \n",
              "1  (001, 001, 002)                         1                        2   \n",
              "2  (001, 001, 003)                         1                        3   \n",
              "3  (001, 001, 004)                         1                        4   \n",
              "\n",
              "  48_machine_example_ID       48_step_ID  72_machineID_time_window  \\\n",
              "0            (001, 001)  (001, 001, 001)                         1   \n",
              "1            (001, 001)  (001, 001, 002)                         1   \n",
              "2            (001, 001)  (001, 001, 003)                         1   \n",
              "3            (001, 001)  (001, 001, 004)                         1   \n",
              "\n",
              "   72_order_in_time_window 72_machine_example_ID       72_step_ID  age  \\\n",
              "0                        1            (001, 001)  (001, 001, 001)   18   \n",
              "1                        2            (001, 001)  (001, 001, 002)   18   \n",
              "2                        3            (001, 001)  (001, 001, 003)   18   \n",
              "3                        4            (001, 001)  (001, 001, 004)   18   \n",
              "\n",
              "   model2  model3  model4        volt      rotate    pressure  vibration  \\\n",
              "0       0       1       0  176.217853  418.504078  113.077935  45.087686   \n",
              "1       0       1       0  162.879223  402.747490   95.460525  43.413973   \n",
              "2       0       1       0  170.989902  527.349825   75.237905  34.178847   \n",
              "3       0       1       0  162.462833  346.149335  109.248561  41.122144   \n",
              "\n",
              "   comp1_maint  comp2_maint  comp3_maint  comp4_maint  error1  error2  error3  \\\n",
              "0            0            0            0            0       0       0       0   \n",
              "1            0            0            0            0       0       0       0   \n",
              "2            0            0            0            0       0       0       0   \n",
              "3            0            0            0            0       0       0       0   \n",
              "\n",
              "   error4  error5  comp1_failure  comp2_failure  comp3_failure  comp4_failure  \n",
              "0       0       0              0              0              0              0  \n",
              "1       0       0              0              0              0              0  \n",
              "2       0       0              0              0              0              0  \n",
              "3       0       0              0              0              0              0  "
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>datetime</th>\n",
              "      <th>machineID</th>\n",
              "      <th>12_machineID_time_window</th>\n",
              "      <th>12_order_in_time_window</th>\n",
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              "      <th>48_machineID_time_window</th>\n",
              "      <th>48_order_in_time_window</th>\n",
              "      <th>48_machine_example_ID</th>\n",
              "      <th>48_step_ID</th>\n",
              "      <th>72_machineID_time_window</th>\n",
              "      <th>72_order_in_time_window</th>\n",
              "      <th>72_machine_example_ID</th>\n",
              "      <th>72_step_ID</th>\n",
              "      <th>age</th>\n",
              "      <th>model2</th>\n",
              "      <th>model3</th>\n",
              "      <th>model4</th>\n",
              "      <th>volt</th>\n",
              "      <th>rotate</th>\n",
              "      <th>pressure</th>\n",
              "      <th>vibration</th>\n",
              "      <th>comp1_maint</th>\n",
              "      <th>comp2_maint</th>\n",
              "      <th>comp3_maint</th>\n",
              "      <th>comp4_maint</th>\n",
              "      <th>error1</th>\n",
              "      <th>error2</th>\n",
              "      <th>error3</th>\n",
              "      <th>error4</th>\n",
              "      <th>error5</th>\n",
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              "      <th>comp2_failure</th>\n",
              "      <th>comp3_failure</th>\n",
              "      <th>comp4_failure</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>2015-01-01 06:00:00</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>(001, 001)</td>\n",
              "      <td>(001, 001, 001)</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>(001, 001)</td>\n",
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              "      <td>2015-01-01 07:00:00</td>\n",
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              "        document.querySelector('#df-f745c9e5-c6e7-49b5-9241-2bd0c9c02cd8 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-f745c9e5-c6e7-49b5-9241-2bd0c9c02cd8');\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-3979bc8a-fcaf-48df-8a60-3a5a56f1bd4d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3979bc8a-fcaf-48df-8a60-3a5a56f1bd4d')\"\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",
              "    }\n",
              "    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-3979bc8a-fcaf-48df-8a60-3a5a56f1bd4d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 1.1) Define Helper Functions to Obtain Outcomes"
      ],
      "metadata": {
        "id": "m2c8oWFFe74r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def chunker(\n",
        "    seq: Sequence,\n",
        "    size: int\n",
        ") -> Iterator[Sequence]:\n",
        "    \"\"\"\n",
        "    Purpose: Splits the sequence into chunks of given size.\n",
        "    :return: An iterator over chunks of the original sequence.\n",
        "    \"\"\"\n",
        "    return (\n",
        "        seq[pos : pos+size] for pos in range(0, len(seq), size)\n",
        "    )"
      ],
      "metadata": {
        "id": "jIuhZXv8h9p_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def custom_agg(\n",
        "    sub_df: pd.DataFrame\n",
        ") -> List[int | float]:\n",
        "    \"\"\"\n",
        "    Performs custom aggregation for a sub-dataframe, ensuring output is JSON serializable.\n",
        "    \"\"\"\n",
        "    aggregated_list = [\n",
        "        float(sub_df['age'].mean()),\n",
        "        int(sub_df['model2'].max()),\n",
        "        int(sub_df['model3'].max()),\n",
        "        int(sub_df['model4'].max()),\n",
        "        float(sub_df['volt'].mean()),\n",
        "        float(sub_df['volt'].std(ddof = 0)),  # ddof=0 for population std, ensuring result not NaN\n",
        "        float(sub_df['rotate'].mean()),\n",
        "        float(sub_df['rotate'].std(ddof = 0)),\n",
        "        float(sub_df['pressure'].mean()),\n",
        "        float(sub_df['pressure'].std(ddof = 0)),\n",
        "        float(sub_df['vibration'].mean()),\n",
        "        float(sub_df['vibration'].std(ddof = 0)),\n",
        "        int(sub_df['comp1_maint'].sum()),\n",
        "        int(sub_df['comp2_maint'].sum()),\n",
        "        int(sub_df['comp3_maint'].sum()),\n",
        "        int(sub_df['comp4_maint'].sum()),\n",
        "        int(sub_df['error1'].sum()),\n",
        "        int(sub_df['error2'].sum()),\n",
        "        int(sub_df['error3'].sum()),\n",
        "        int(sub_df['error4'].sum()),\n",
        "        int(sub_df['error5'].sum()),\n",
        "        int(sub_df['comp1_failure'].sum()),\n",
        "        int(sub_df['comp2_failure'].sum()),\n",
        "        int(sub_df['comp3_failure'].sum()),\n",
        "        int(sub_df['comp4_failure'].sum()),\n",
        "    ]\n",
        "    # replace NaNs with zeros, assuming missing data can be treated as zero\n",
        "    aggregated_list = [\n",
        "        0 if np.isnan(x) else x for x in aggregated_list\n",
        "    ]\n",
        "    return aggregated_list"
      ],
      "metadata": {
        "id": "ioZieBcBh9yL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def generate_features_dict(\n",
        "    df: pd.DataFrame,\n",
        "    hour_feature_window: int,\n",
        "    chunk_size: int = 1\n",
        ") -> Dict[\n",
        "    str,\n",
        "    List[List[Union[int, float]]]\n",
        "]:\n",
        "    \"\"\"\n",
        "    Generates a dictionary of features from the DataFrame based on chunk size.\n",
        "    \"\"\"\n",
        "    assert hour_feature_window % chunk_size == 0, \\\n",
        "        f\"chunk_size ({chunk_size}) must evenly divide hour_feature_window ({hour_feature_window}).\"\n",
        "\n",
        "    # obtain column names to use for this hour_feature_window\n",
        "    machineID_time_window_col = f'{hour_feature_window}_machineID_time_window'\n",
        "    order_in_time_window_col = f'{hour_feature_window}_order_in_time_window'\n",
        "    machine_example_ID_col = f'{hour_feature_window}_machine_example_ID'\n",
        "    step_ID_col\t= f'{hour_feature_window}_step_ID'\n",
        "\n",
        "    # sort DataFrame\n",
        "    df.sort_values(\n",
        "        by = [machine_example_ID_col, order_in_time_window_col],\n",
        "        inplace = True\n",
        "    )\n",
        "\n",
        "    # create features_dict\n",
        "    features_dict = dict()\n",
        "    for machine_example_ID, group in df.groupby(machine_example_ID_col):\n",
        "\n",
        "        # process in chunks\n",
        "        aggregated_data_list = [\n",
        "            custom_agg(\n",
        "                sub_df = group.iloc[chunk_indices]\n",
        "            ) for chunk_indices in chunker(\n",
        "                seq = range(group.shape[0]),\n",
        "                size = chunk_size\n",
        "            )\n",
        "        ]\n",
        "        #assert len(aggregated_data_list) == hour_feature_window / chunk_size, \\\n",
        "        #    f\"len(aggregated_data_list) = {len(aggregated_data_list)}. Should be {hour_feature_window / chunk_size}! \\n {aggregated_data_list}\"\n",
        "\n",
        "\n",
        "        # store data\n",
        "        features_dict[machine_example_ID] = aggregated_data_list\n",
        "\n",
        "    return features_dict"
      ],
      "metadata": {
        "id": "rzHqpbbLd71n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 1.2) Use Helper Function in Loop of Hour Windows"
      ],
      "metadata": {
        "id": "5hh6lTzbe_RJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for hour_feature_window in [12, 24, 48, 72]:\n",
        "    print(f\"- Hour Feature Window: {hour_feature_window}\")\n",
        "\n",
        "    # obtain features_dict\n",
        "    features_dict = generate_features_dict(\n",
        "        df = df,\n",
        "        hour_feature_window = hour_feature_window,\n",
        "        chunk_size = 1\n",
        "    )\n",
        "    print(f\"\\t- {format(len(features_dict), ',')} total rows in features_dict.\")\n",
        "\n",
        "    # remove last time interval (because there will be no corresponding outcome)\n",
        "    machineID_time_window_col = f'{hour_feature_window}_machineID_time_window'\n",
        "    max_machineID_time_window = df[machineID_time_window_col].max()\n",
        "    max_machineID_time_window = f'{max_machineID_time_window:03}'\n",
        "    features_dict = {\n",
        "        k: v for k, v in features_dict.items()\n",
        "        if str(k[6:9]) != max_machineID_time_window\n",
        "    }\n",
        "    print(f\"\\t- Removed max order in time window ({max_machineID_time_window}).\")\n",
        "\n",
        "    # print length of features_dict\n",
        "    print(f\"\\t- {format(len(features_dict), ',')} total rows in features_dict.\")\n",
        "\n",
        "    # save as JSON\n",
        "    SAVE_FILENAME = f'features_dict_{hour_feature_window}hrfeaturewindow.json'\n",
        "    SAVE_LOC = os.path.join(SAVE_DATA_DIR, SAVE_FILENAME)\n",
        "    with open(SAVE_LOC, mode = 'w') as json_f:\n",
        "      json.dump(obj = features_dict, fp = json_f, indent = 4)\n",
        "    print(f\"\\t- Saved file: {SAVE_FILENAME}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hut7rlasd73v",
        "outputId": "5d415d1f-1f2f-456c-97c2-6b62145692b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "- Hour Feature Window: 12\n",
            "\t- 73,100 total rows in features_dict.\n",
            "\t- Removed max order in time window (731).\n",
            "\t- 73,000 total rows in features_dict.\n",
            "\t- Saved file: features_dict_12hrfeaturewindow.json\n",
            "- Hour Feature Window: 24\n",
            "\t- 36,600 total rows in features_dict.\n",
            "\t- Removed max order in time window (366).\n",
            "\t- 36,500 total rows in features_dict.\n",
            "\t- Saved file: features_dict_24hrfeaturewindow.json\n",
            "- Hour Feature Window: 48\n",
            "\t- 18,300 total rows in features_dict.\n",
            "\t- Removed max order in time window (183).\n",
            "\t- 18,200 total rows in features_dict.\n",
            "\t- Saved file: features_dict_48hrfeaturewindow.json\n",
            "- Hour Feature Window: 72\n",
            "\t- 12,200 total rows in features_dict.\n",
            "\t- Removed max order in time window (122).\n",
            "\t- 12,100 total rows in features_dict.\n",
            "\t- Saved file: features_dict_72hrfeaturewindow.json\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "i = 0\n",
        "for k, v in features_dict.items():\n",
        "    i += 1\n",
        "    if i > 1:\n",
        "        break\n",
        "    print(f\"{k} -> {v}\")\n",
        "    print(len(v))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xs6N2rgUdzV_",
        "outputId": "c797d8a4-b3c0-442d-aeb3-93287ca01ff2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(001, 001) -> [[18.0, 0, 1, 0, 176.217853015625, 0.0, 418.504078221616, 0.0, 113.077935462083, 0.0, 45.0876857639276, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [18.0, 0, 1, 0, 162.87922289706, 0.0, 402.747489565395, 0.0, 95.4605253823187, 0.0, 43.4139726834815, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [18.0, 0, 1, 0, 170.989902405567, 0.0, 527.349825452291, 0.0, 75.2379048586662, 0.0, 34.1788471214451, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [18.0, 0, 1, 0, 162.462833264092, 0.0, 346.149335043074, 0.0, 109.248561276504, 0.0, 41.1221440884256, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [18.0, 0, 1, 0, 157.61002119306, 0.0, 435.376873016938, 0.0, 111.886648210168, 0.0, 25.9905109982024, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [18.0, 0, 1, 0, 172.504839196295, 0.0, 430.323362106675, 0.0, 95.9270416939636, 0.0, 35.6550173268837, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [18.0, 0, 1, 0, 156.556030606329, 0.0, 499.071623068962, 0.0, 111.755684290096, 0.0, 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            "72\n"
          ]
        }
      ]
    }
  ]
}