Upload Baseline_XGBoost_Resource_Estimation.ipynb

Baseline_XGBoost_Resource_Estimation.ipynb

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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "e5e0f994"
+      },
+      "source": [
+        "# 🚀 Baseline XGBoost for Resource Estimation of CNNs (Keras Applications)\n",
+        "This notebook demonstrates how to use XGBoost for predicting resource usage (like fit time) of CNN models based on dataset features."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "275c013b"
+      },
+      "source": [
+        "## 1️⃣ Setup and Installation\n",
+        "Ensure required libraries are installed."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "DPbLUZKvRtwx",
+        "outputId": "d65bcfd7-a615-4b74-feb6-757456f42581"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Found existing installation: scikit-learn 1.6.1\n",
+            "Uninstalling scikit-learn-1.6.1:\n",
+            "  Successfully uninstalled scikit-learn-1.6.1\n",
+            "Collecting scikit-learn==1.5.2\n",
+            "  Downloading scikit_learn-1.5.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (13 kB)\n",
+            "Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.11/dist-packages (from scikit-learn==1.5.2) (2.0.2)\n",
+            "Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn==1.5.2) (1.14.1)\n",
+            "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn==1.5.2) (1.4.2)\n",
+            "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn==1.5.2) (3.6.0)\n",
+            "Downloading scikit_learn-1.5.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (13.3 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.3/13.3 MB\u001b[0m \u001b[31m34.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hInstalling collected packages: scikit-learn\n",
+            "Successfully installed scikit-learn-1.5.2\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip uninstall -y scikit-learn\n",
+        "!pip install scikit-learn==1.5.2"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "48b0b5f0"
+      },
+      "source": [
+        "## 2️⃣ Import Libraries\n",
+        "Import all necessary Python libraries for data handling, modeling, and visualization."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "V23vhp8o9YHM"
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.metrics import mean_squared_error\n",
+        "from xgboost import XGBRegressor\n",
+        "import joblib"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "107733d4"
+      },
+      "source": [
+        "## 3️⃣ Data Loading & Preprocessing\n",
+        "Load the dataset and perform basic preprocessing to prepare for modeling."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "UoYmjX7NGVVD"
+      },
+      "outputs": [],
+      "source": [
+        "def calculate_mspe_rmspe(y_true, y_pred):\n",
+        "    mape = np.mean(np.abs((y_true - y_pred) / (y_true)), axis=0) * 100\n",
+        "    mspe = np.mean(((y_true - y_pred) / y_true) ** 2, axis=0) * 100  # MSPE for each column\n",
+        "    rmspe = np.sqrt(mspe)  # RMSPE for each column\n",
+        "    return mape, mspe, rmspe\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "CmmE7SNz-KXJ",
+        "outputId": "dc55b8bf-2000-4954-b231-664d715851de"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Index(['name', 'samples', 'input_dim_w', 'input_dim_h', 'input_dim_c',\n",
+              "       'output_dim', 'optimizer', 'epochs', 'batch', 'learn_rate',\n",
+              "       'tf_version', 'cuda_version', 'batch_time', 'epoch_time', 'fit_time',\n",
+              "       'npz_path', 'gpu_make', 'gpu_name', 'gpu_arch', 'gpu_cc',\n",
+              "       'gpu_core_count', 'gpu_sm_count', 'gpu_memory_size', 'gpu_memory_type',\n",
+              "       'gpu_memory_bw', 'gpu_tensor_core_count', 'max_memory_util',\n",
+              "       'avg_memory_util', 'max_gpu_util', 'avg_gpu_util', 'max_gpu_temp',\n",
+              "       'avg_gpu_temp'],\n",
+              "      dtype='object')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 7
+        }
+      ],
+      "source": [
+        "# Load data\n",
+        "# Assuming the data is in a CSV file with the target column 'fit_time_in_TF'\n",
+        "data_path = 'dataset-new.csv'  # Replace with the actual path to your dataset\n",
+        "df = pd.read_csv(data_path)\n",
+        "df.columns"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "962d5030"
+      },
+      "source": [
+        "## 4️⃣ Feature Engineering\n",
+        "Extract relevant features and clean the dataset."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "OGzq5lrIHh2R",
+        "outputId": "9ee9eabd-7363-451a-b379-013b1aa7688d"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                                                name  unit_name\n",
+            "0  MobileNet_architecture_optadam_s1_ipd224x224x3...  MobileNet\n",
+            "1  MobileNet_architecture_optadam_s1_ipd224x224x3...  MobileNet\n",
+            "2  MobileNet_architecture_optadam_s1_ipd224x224x3...  MobileNet\n",
+            "3  MobileNet_architecture_optadam_s1_ipd224x224x3...  MobileNet\n",
+            "4  MobileNet_architecture_optadam_s1_ipd224x224x3...  MobileNet\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Extract substring before the first underscore\n",
+        "df['unit_name'] = df['name'].str.split('_').str[0]\n",
+        "\n",
+        "# Display the updated DataFrame\n",
+        "print(df[['name', 'unit_name']].head())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "mw1fY7vM-fCw",
+        "outputId": "a8903a63-83f3-4b6f-b061-4a4bb900cd1d"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Label Mapping: {'DenseNet121': 0, 'DenseNet169': 1, 'DenseNet201': 2, 'EfficientNetB0': 3, 'EfficientNetB1': 4, 'EfficientNetB7': 5, 'InceptionV3': 6, 'MobileNet': 7, 'MobileNetV2': 8, 'NASNetLarge': 9, 'NASNetMobile': 10, 'ResNet101': 11, 'ResNet152': 12, 'ResNet50': 13, 'VGG16': 14, 'VGG19': 15, 'Xception': 16}\n"
+          ]
+        }
+      ],
+      "source": [
+        "df = df.dropna()  # Dropping rows with missing values (you can customize this)\n",
+        "\n",
+        "from sklearn.preprocessing import LabelEncoder\n",
+        "label_encoder = LabelEncoder()\n",
+        "# Transform the categorical column\n",
+        "df['unit_name_encoded'] = label_encoder.fit_transform(df['unit_name'])\n",
+        "# Optional: Mapping of encoded labels to original categories\n",
+        "mapping = dict(zip(label_encoder.classes_, range(len(label_encoder.classes_))))\n",
+        "print(\"\\nLabel Mapping:\", mapping)\n",
+        "\n",
+        "df = df.drop(columns=['name', 'npz_path', 'unit_name'])\n",
+        "# Convert categorical features to numeric (if any)\n",
+        "df = pd.get_dummies(df, drop_first=True)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 290
+        },
+        "id": "04XJeqln-g4n",
+        "outputId": "f63e3507-6770-41be-dc9c-b03a3cb232a6"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   samples  input_dim_w  input_dim_h  input_dim_c  output_dim  epochs  batch  \\\n",
+              "0        1          224          224            3          10       1      1   \n",
+              "1        1          224          224            3          10       1      1   \n",
+              "2        1          224          224            3          10       1      1   \n",
+              "3        1          224          224            3          10       2      1   \n",
+              "4        1          224          224            3          10       2      1   \n",
+              "\n",
+              "   learn_rate  cuda_version  batch_time  ...  max_gpu_util  avg_gpu_util  \\\n",
+              "0      0.0100          12.2       22.07  ...          13.0          0.51   \n",
+              "1      0.0010          12.2       18.44  ...         100.0          2.92   \n",
+              "2      0.0001          12.2       18.78  ...          26.0          0.86   \n",
+              "3      0.0100          12.2        9.38  ...          28.0          1.78   \n",
+              "4      0.0010          12.2        9.30  ...         100.0          3.41   \n",
+              "\n",
+              "   max_gpu_temp  avg_gpu_temp  unit_name_encoded  optimizer_sgd  \\\n",
+              "0          25.0         25.00                  7          False   \n",
+              "1          26.0         25.84                  7          False   \n",
+              "2          26.0         26.00                  7          False   \n",
+              "3          27.0         26.04                  7          False   \n",
+              "4          27.0         26.55                  7          False   \n",
+              "\n",
+              "   gpu_name_Tesla P100-PCIE-16GB  gpu_name_Tesla V100S-PCIE-32GB  \\\n",
+              "0                           True                           False   \n",
+              "1                           True                           False   \n",
+              "2                           True                           False   \n",
+              "3                           True                           False   \n",
+              "4                           True                           False   \n",
+              "\n",
+              "   gpu_arch_Tesla  gpu_memory_type_hbm2e  \n",
+              "0            True                  False  \n",
+              "1            True                  False  \n",
+              "2            True                  False  \n",
+              "3            True                  False  \n",
+              "4            True                  False  \n",
+              "\n",
+              "[5 rows x 30 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-c1d454f0-1110-4414-b0fa-c9f3510b4a83\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
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+              "    .dataframe thead th {\n",
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+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>samples</th>\n",
+              "      <th>input_dim_w</th>\n",
+              "      <th>input_dim_h</th>\n",
+              "      <th>input_dim_c</th>\n",
+              "      <th>output_dim</th>\n",
+              "      <th>epochs</th>\n",
+              "      <th>batch</th>\n",
+              "      <th>learn_rate</th>\n",
+              "      <th>cuda_version</th>\n",
+              "      <th>batch_time</th>\n",
+              "      <th>...</th>\n",
+              "      <th>max_gpu_util</th>\n",
+              "      <th>avg_gpu_util</th>\n",
+              "      <th>max_gpu_temp</th>\n",
+              "      <th>avg_gpu_temp</th>\n",
+              "      <th>unit_name_encoded</th>\n",
+              "      <th>optimizer_sgd</th>\n",
+              "      <th>gpu_name_Tesla P100-PCIE-16GB</th>\n",
+              "      <th>gpu_name_Tesla V100S-PCIE-32GB</th>\n",
+              "      <th>gpu_arch_Tesla</th>\n",
+              "      <th>gpu_memory_type_hbm2e</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>224</td>\n",
+              "      <td>224</td>\n",
+              "      <td>3</td>\n",
+              "      <td>10</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0.0100</td>\n",
+              "      <td>12.2</td>\n",
+              "      <td>22.07</td>\n",
+              "      <td>...</td>\n",
+              "      <td>13.0</td>\n",
+              "      <td>0.51</td>\n",
+              "      <td>25.0</td>\n",
+              "      <td>25.00</td>\n",
+              "      <td>7</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1</td>\n",
+              "      <td>224</td>\n",
+              "      <td>224</td>\n",
+              "      <td>3</td>\n",
+              "      <td>10</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0.0010</td>\n",
+              "      <td>12.2</td>\n",
+              "      <td>18.44</td>\n",
+              "      <td>...</td>\n",
+              "      <td>100.0</td>\n",
+              "      <td>2.92</td>\n",
+              "      <td>26.0</td>\n",
+              "      <td>25.84</td>\n",
+              "      <td>7</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>1</td>\n",
+              "      <td>224</td>\n",
+              "      <td>224</td>\n",
+              "      <td>3</td>\n",
+              "      <td>10</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0.0001</td>\n",
+              "      <td>12.2</td>\n",
+              "      <td>18.78</td>\n",
+              "      <td>...</td>\n",
+              "      <td>26.0</td>\n",
+              "      <td>0.86</td>\n",
+              "      <td>26.0</td>\n",
+              "      <td>26.00</td>\n",
+              "      <td>7</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1</td>\n",
+              "      <td>224</td>\n",
+              "      <td>224</td>\n",
+              "      <td>3</td>\n",
+              "      <td>10</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0.0100</td>\n",
+              "      <td>12.2</td>\n",
+              "      <td>9.38</td>\n",
+              "      <td>...</td>\n",
+              "      <td>28.0</td>\n",
+              "      <td>1.78</td>\n",
+              "      <td>27.0</td>\n",
+              "      <td>26.04</td>\n",
+              "      <td>7</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>1</td>\n",
+              "      <td>224</td>\n",
+              "      <td>224</td>\n",
+              "      <td>3</td>\n",
+              "      <td>10</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0.0010</td>\n",
+              "      <td>12.2</td>\n",
+              "      <td>9.30</td>\n",
+              "      <td>...</td>\n",
+              "      <td>100.0</td>\n",
+              "      <td>3.41</td>\n",
+              "      <td>27.0</td>\n",
+              "      <td>26.55</td>\n",
+              "      <td>7</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
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+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-c1d454f0-1110-4414-b0fa-c9f3510b4a83');\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-7fca458e-936c-4a6a-a0af-2f6ef67c3e8e\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7fca458e-936c-4a6a-a0af-2f6ef67c3e8e')\"\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-7fca458e-936c-4a6a-a0af-2f6ef67c3e8e 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": 10
+        }
+      ],
+      "source": [
+        "df.head()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "83f8b988"
+      },
+      "source": [
+        "## 5️⃣ Train-Test Split\n",
+        "Split the dataset into training and testing sets."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "id": "m8xtdVgq_ZBt"
+      },
+      "outputs": [],
+      "source": [
+        "# Example: Split based on a numerical condition\n",
+        "train_data = df[df['unit_name_encoded'] >= 6]\n",
+        "test_data = df[df['unit_name_encoded'] < 6]\n",
+        "\n",
+        "train_data = train_data.sample(frac=0.2, random_state=42)\n",
+        "\n",
+        "# Separate features and target\n",
+        "\n",
+        "X_train = train_data.drop(columns=['max_memory_util',\t'avg_memory_util',\t'max_gpu_util',\t'avg_gpu_util',\t'max_gpu_temp',\t'avg_gpu_temp', 'epoch_time',\t'fit_time'])\n",
+        "y_train = train_data[['epoch_time', 'fit_time', 'max_memory_util', 'max_gpu_util']]\n",
+        "X_test = test_data.drop(columns=['max_memory_util',\t'avg_memory_util',\t'max_gpu_util',\t'avg_gpu_util',\t'max_gpu_temp',\t'avg_gpu_temp', 'epoch_time',\t'fit_time'])  # Replace 'fit_time_in_TF' with your target column\n",
+        "y_test = test_data[['epoch_time', 'fit_time', 'max_memory_util', 'max_gpu_util']]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "0B124aBf-i7l",
+        "outputId": "5de51f6b-fbc7-4bbe-8849-89b9667f3218"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "(1553, 27)"
+            ]
+          },
+          "execution_count": 10,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "train_data.shape"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3250bbd6"
+      },
+      "source": [
+        "## 6️⃣ Model Building with XGBoost\n",
+        "Define, train, and predict using the XGBoost Regressor."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 253
+        },
+        "id": "yc6bxIBq_ZuP",
+        "outputId": "83fb3bd1-d5fa-48aa-dc23-5248667f2974"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+              "             colsample_bylevel=None, colsample_bynode=None,\n",
+              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
+              "             gamma=None, grow_policy=None, importance_type=None,\n",
+              "             interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
+              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
+              "             max_delta_step=None, max_depth=6, max_leaves=None,\n",
+              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+              "             multi_strategy=None, n_estimators=100, n_jobs=None,\n",
+              "             num_parallel_tree=None, random_state=42, ...)"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-1 {\n",
+              "  /* Definition of color scheme common for light and dark mode */\n",
+              "  --sklearn-color-text: black;\n",
+              "  --sklearn-color-line: gray;\n",
+              "  /* Definition of color scheme for unfitted estimators */\n",
+              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+              "  --sklearn-color-unfitted-level-3: chocolate;\n",
+              "  /* Definition of color scheme for fitted estimators */\n",
+              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+              "\n",
+              "  /* Specific color for light theme */\n",
+              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-icon: #696969;\n",
+              "\n",
+              "  @media (prefers-color-scheme: dark) {\n",
+              "    /* Redefinition of color scheme for dark theme */\n",
+              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-icon: #878787;\n",
+              "  }\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-hidden--visually {\n",
+              "  border: 0;\n",
+              "  clip: rect(1px 1px 1px 1px);\n",
+              "  clip: rect(1px, 1px, 1px, 1px);\n",
+              "  height: 1px;\n",
+              "  margin: -1px;\n",
+              "  overflow: hidden;\n",
+              "  padding: 0;\n",
+              "  position: absolute;\n",
+              "  width: 1px;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+              "  border: 1px dashed var(--sklearn-color-line);\n",
+              "  margin: 0 0.4em 0.5em 0.4em;\n",
+              "  box-sizing: border-box;\n",
+              "  padding-bottom: 0.4em;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-container {\n",
+              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+              "     so we also need the `!important` here to be able to override the\n",
+              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+              "  display: inline-block !important;\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-parallel-item,\n",
+              "div.sk-serial,\n",
+              "div.sk-item {\n",
+              "  /* draw centered vertical line to link estimators */\n",
+              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+              "  background-size: 2px 100%;\n",
+              "  background-repeat: no-repeat;\n",
+              "  background-position: center center;\n",
+              "}\n",
+              "\n",
+              "/* Parallel-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item::after {\n",
+              "  content: \"\";\n",
+              "  width: 100%;\n",
+              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+              "  flex-grow: 1;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel {\n",
+              "  display: flex;\n",
+              "  align-items: stretch;\n",
+              "  justify-content: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-serial {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "  align-items: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  padding-right: 1em;\n",
+              "  padding-left: 1em;\n",
+              "}\n",
+              "\n",
+              "\n",
+              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+              "clickable and can be expanded/collapsed.\n",
+              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+              "*/\n",
+              "\n",
+              "/* Pipeline and ColumnTransformer style (default) */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable {\n",
+              "  /* Default theme specific background. It is overwritten whether we have a\n",
+              "  specific estimator or a Pipeline/ColumnTransformer */\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable label */\n",
+              "#sk-container-id-1 label.sk-toggleable__label {\n",
+              "  cursor: pointer;\n",
+              "  display: block;\n",
+              "  width: 100%;\n",
+              "  margin-bottom: 0;\n",
+              "  padding: 0.5em;\n",
+              "  box-sizing: border-box;\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+              "  /* Arrow on the left of the label */\n",
+              "  content: \"▸\";\n",
+              "  float: left;\n",
+              "  margin-right: 0.25em;\n",
+              "  color: var(--sklearn-color-icon);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content {\n",
+              "  max-height: 0;\n",
+              "  max-width: 0;\n",
+              "  overflow: hidden;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+              "  margin: 0.2em;\n",
+              "  border-radius: 0.25em;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  max-height: 200px;\n",
+              "  max-width: 100%;\n",
+              "  overflow: auto;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+              "  content: \"▾\";\n",
+              "}\n",
+              "\n",
+              "/* Pipeline/ColumnTransformer-specific style */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific style */\n",
+              "\n",
+              "/* Colorize estimator box */\n",
+              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-1 div.sk-label label {\n",
+              "  /* The background is the default theme color */\n",
+              "  color: var(--sklearn-color-text-on-default-background);\n",
+              "}\n",
+              "\n",
+              "/* On hover, darken the color of the background */\n",
+              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Label box, darken color on hover, fitted */\n",
+              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator label */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label label {\n",
+              "  font-family: monospace;\n",
+              "  font-weight: bold;\n",
+              "  display: inline-block;\n",
+              "  line-height: 1.2em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-1 div.sk-estimator {\n",
+              "  font-family: monospace;\n",
+              "  border: 1px dotted var(--sklearn-color-border-box);\n",
+              "  border-radius: 0.25em;\n",
+              "  box-sizing: border-box;\n",
+              "  margin-bottom: 0.5em;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-1 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+              "\n",
+              "/* Common style for \"i\" and \"?\" */\n",
+              "\n",
+              ".sk-estimator-doc-link,\n",
+              "a:link.sk-estimator-doc-link,\n",
+              "a:visited.sk-estimator-doc-link {\n",
+              "  float: right;\n",
+              "  font-size: smaller;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1em;\n",
+              "  height: 1em;\n",
+              "  width: 1em;\n",
+              "  text-decoration: none !important;\n",
+              "  margin-left: 1ex;\n",
+              "  /* unfitted */\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted,\n",
+              "a:link.sk-estimator-doc-link.fitted,\n",
+              "a:visited.sk-estimator-doc-link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "/* Span, style for the box shown on hovering the info icon */\n",
+              ".sk-estimator-doc-link span {\n",
+              "  display: none;\n",
+              "  z-index: 9999;\n",
+              "  position: relative;\n",
+              "  font-weight: normal;\n",
+              "  right: .2ex;\n",
+              "  padding: .5ex;\n",
+              "  margin: .5ex;\n",
+              "  width: min-content;\n",
+              "  min-width: 20ex;\n",
+              "  max-width: 50ex;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  box-shadow: 2pt 2pt 4pt #999;\n",
+              "  /* unfitted */\n",
+              "  background: var(--sklearn-color-unfitted-level-0);\n",
+              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted span {\n",
+              "  /* fitted */\n",
+              "  background: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link:hover span {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link {\n",
+              "  float: right;\n",
+              "  font-size: 1rem;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1rem;\n",
+              "  height: 1rem;\n",
+              "  width: 1rem;\n",
+              "  text-decoration: none;\n",
+              "  /* unfitted */\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+              "             colsample_bylevel=None, colsample_bynode=None,\n",
+              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
+              "             gamma=None, grow_policy=None, importance_type=None,\n",
+              "             interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
+              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
+              "             max_delta_step=None, max_depth=6, max_leaves=None,\n",
+              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+              "             multi_strategy=None, n_estimators=100, n_jobs=None,\n",
+              "             num_parallel_tree=None, random_state=42, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;XGBRegressor<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+              "             colsample_bylevel=None, colsample_bynode=None,\n",
+              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
+              "             gamma=None, grow_policy=None, importance_type=None,\n",
+              "             interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
+              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
+              "             max_delta_step=None, max_depth=6, max_leaves=None,\n",
+              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+              "             multi_strategy=None, n_estimators=100, n_jobs=None,\n",
+              "             num_parallel_tree=None, random_state=42, ...)</pre></div> </div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 12
+        }
+      ],
+      "source": [
+        "xgb_model = XGBRegressor(\n",
+        "    n_estimators=100,\n",
+        "    learning_rate=0.1,\n",
+        "    max_depth=6,\n",
+        "    random_state=42,\n",
+        "    verbosity=1\n",
+        ")\n",
+        "\n",
+        "# Train the model\n",
+        "xgb_model.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "id": "4moppOvOFnZD"
+      },
+      "outputs": [],
+      "source": [
+        "# Predict on the test set\n",
+        "y_pred = xgb_model.predict(X_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "xIwFbkEyNuby",
+        "outputId": "2f9fb71f-b828-41b6-e9bb-2a5747a18444"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[21.514431 , 20.075377 ,  5.7195935, 48.045883 ],\n",
+              "       [19.894218 , 19.883572 , 10.0262985, 69.34461  ],\n",
+              "       [20.112694 , 20.007633 , 11.493458 , 73.78841  ],\n",
+              "       [12.975633 , 18.08273  ,  7.1654763, 45.41951  ],\n",
+              "       [12.418544 , 18.560135 ,  8.162864 , 59.944546 ],\n",
+              "       [12.637019 , 18.684196 ,  8.962711 , 67.1194   ],\n",
+              "       [ 6.4134297, 20.612186 , 10.105451 , 60.0018   ],\n",
+              "       [ 5.8482485, 21.089592 , 10.060244 , 67.4093   ],\n",
+              "       [ 5.8482485, 20.968369 , 10.259554 , 74.447716 ],\n",
+              "       [ 4.295031 , 22.474957 , 10.036483 , 67.872734 ]], dtype=float32)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 15
+        }
+      ],
+      "source": [
+        "y_pred[:10]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "b996833d"
+      },
+      "source": [
+        "## 7️⃣ Evaluation Metrics\n",
+        "Calculate MAPE, MSPE, RMSPE, and standard regression metrics."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "gL5b_8Fb_lyj",
+        "outputId": "05c40163-4803-4788-cc3d-34d0e9438dea"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "<ipython-input-16-84545122e16d>:2: SettingWithCopyWarning: \n",
+            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+            "Try using .loc[row_indexer,col_indexer] = value instead\n",
+            "\n",
+            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+            "  df1['fit_time_res'] = (df1['batch_time'] / df1['batch']) * df1['samples'] * df1['epochs']\n"
+          ]
+        }
+      ],
+      "source": [
+        "def manualCalculate(df1):\n",
+        "  df1['fit_time_res'] = (df1['batch_time'] / df1['batch']) * df1['samples'] * df1['epochs']\n",
+        "  return df1[['fit_time_res']]\n",
+        "\n",
+        "y_manual = manualCalculate(X_test[['batch_time', 'batch', 'samples', 'epochs']])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "zHc7GLmw_eDu",
+        "outputId": "7240f37a-3dc2-4ae9-a3ac-08b977714fbd"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Target Column 1:\n",
+            "  MSE: 1245.5831\n",
+            "  RMSE: 35.2928\n",
+            "  MAPE: 35.2928%\n",
+            "  MSPE: 42.7299%\n",
+            "  RMSPE: 6.5368%\n",
+            "Target Column 2:\n",
+            "  MSE: 4263.1022\n",
+            "  RMSE: 65.2924\n",
+            "  MAPE: 65.2924%\n",
+            "  MSPE: 43.7782%\n",
+            "  RMSPE: 6.6165%\n",
+            "Target Column 3:\n",
+            "  MSE: 99.2517\n",
+            "  RMSE: 9.9625\n",
+            "  MAPE: 9.9625%\n",
+            "  MSPE: inf%\n",
+            "  RMSPE: inf%\n",
+            "Target Column 4:\n",
+            "  MSE: 456.3985\n",
+            "  RMSE: 21.3635\n",
+            "  MAPE: 21.3635%\n",
+            "  MSPE: inf%\n",
+            "  RMSPE: inf%\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "['xgb_model_model.pkl']"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 17
+        }
+      ],
+      "source": [
+        "\n",
+        "\n",
+        "mape__per_column, mspe_per_column, rmspe_per_column = calculate_mspe_rmspe(y_test, y_pred)\n",
+        "\n",
+        "mse_per_column = mean_squared_error(y_test, y_pred, multioutput='raw_values')  # MSE for each column\n",
+        "rmse_per_column = np.sqrt(mse_per_column)  # RMSE for each column\n",
+        "\n",
+        "# Display results\n",
+        "for i, (mse, rmse, mape, mspe, rmspe) in enumerate(zip(mse_per_column, rmse_per_column, mape__per_column, mspe_per_column, rmspe_per_column)):\n",
+        "    print(f\"Target Column {i + 1}:\")\n",
+        "    print(f\"  MSE: {mse:.4f}\")\n",
+        "    print(f\"  RMSE: {rmse:.4f}\")\n",
+        "    print(f\"  MAPE: {rmse:.4f}%\")\n",
+        "    print(f\"  MSPE: {mspe:.4f}%\")\n",
+        "    print(f\"  RMSPE: {rmspe:.4f}%\")\n",
+        "\n",
+        "# Save the model for future use\n",
+        "joblib.dump(xgb_model, 'xgb_model_model.pkl')\n",
+        "\n",
+        "# Example of loading the model\n",
+        "# loaded_model = joblib.load('random_forest_model.pkl')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "VR0GKYPTBQqu",
+        "outputId": "6ae3a663-c9d8-4c9d-a4ea-7db03208de35"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Target Column 1:\n",
+            "  MSE: 111.3580\n",
+            "  RMSE: 10.5526\n",
+            "  MAPE: 10.5526%\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "['xgb_model_model.pkl']"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 18
+        }
+      ],
+      "source": [
+        "\n",
+        "\n",
+        "mape__per_column, mspe_per_column, rmspe_per_column = calculate_mspe_rmspe(y_test[['fit_time']], y_manual)\n",
+        "\n",
+        "mse_per_column = mean_squared_error(y_test[['fit_time']], y_manual, multioutput='raw_values')  # MSE for each column\n",
+        "rmse_per_column = np.sqrt(mse_per_column)  # RMSE for each column\n",
+        "\n",
+        "# Display results\n",
+        "for i, (mse, rmse, mape, mspe, rmspe) in enumerate(zip(mse_per_column, rmse_per_column, mape__per_column, mspe_per_column, rmspe_per_column)):\n",
+        "    print(f\"Target Column {i + 1}:\")\n",
+        "    print(f\"  MSE: {mse:.4f}\")\n",
+        "    print(f\"  RMSE: {rmse:.4f}\")\n",
+        "    print(f\"  MAPE: {rmse:.4f}%\")\n",
+        "\n",
+        "# Save the model for future use\n",
+        "joblib.dump(xgb_model, 'xgb_model_model.pkl')\n",
+        "\n",
+        "# Example of loading the model\n",
+        "# loaded_model = joblib.load('random_forest_model.pkl')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {
+        "id": "c0a4ea0a"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "import numpy as np"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "4f37c35d",
+        "outputId": "6be2112f-41c1-4b0e-f367-f9ab8dd4f839"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "MAE: 24.9825\n",
+            "RMSE: 32.9778\n",
+            "R²: 0.2661\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.11/dist-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
+            "  warnings.warn(\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Standard Regression Metrics\n",
+        "mae = mean_absolute_error(y_test, y_pred)\n",
+        "rmse = mean_squared_error(y_test, y_pred, squared=False)\n",
+        "r2 = r2_score(y_test, y_pred)\n",
+        "\n",
+        "print(f'MAE: {mae:.4f}')\n",
+        "print(f'RMSE: {rmse:.4f}')\n",
+        "print(f'R²: {r2:.4f}')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "c3eb4fc3"
+      },
+      "source": [
+        "## 9️⃣ Conclusion\n",
+        "Summarize key insights from the model performance and visualizations."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "c78d5e48"
+      },
+      "source": [
+        "### ✅ In Conclusion:\n",
+        "- The XGBoost model provides a reasonable baseline for predicting CNN resource usage.\n",
+        "- Visualization highlights areas where predictions deviate.\n",
+        "- Feature importance gives insights into which factors most influence fit time.\n",
+        "\n",
+        "For future work, hyperparameter tuning and advanced models could improve accuracy."
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "gpuType": "T4",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}