{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q_kZgUNPuBum",
        "outputId": "403e73d2-86af-44f0-ba60-d88bf62f9881"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Wed Mar 27 05:09:34 2024       \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\n",
            "|-----------------------------------------+----------------------+----------------------+\n",
            "| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                                         |                      |               MIG M. |\n",
            "|=========================================+======================+======================|\n",
            "|   0  Tesla T4                       Off | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   47C    P8              12W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
            "|                                         |                      |                  N/A |\n",
            "+-----------------------------------------+----------------------+----------------------+\n",
            "                                                                                         \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| Processes:                                                                            |\n",
            "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
            "|        ID   ID                                                             Usage      |\n",
            "|=======================================================================================|\n",
            "|  No running processes found                                                           |\n",
            "+---------------------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nG8mHmHHuBke"
      },
      "outputs": [],
      "source": [
        "!pip install pytorch-lightning"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d_yLgGq8C8lD",
        "outputId": "0aa8fbde-18d6-4d46-8806-60b1206f0769"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Name: pytorch-lightning\n",
            "Version: 2.2.1\n",
            "Summary: PyTorch Lightning is the lightweight PyTorch wrapper for ML researchers. Scale your models. Write less boilerplate.\n",
            "Home-page: https://github.com/Lightning-AI/lightning\n",
            "Author: Lightning AI et al.\n",
            "Author-email: pytorch@lightning.ai\n",
            "License: Apache-2.0\n",
            "Location: /usr/local/lib/python3.10/dist-packages\n",
            "Requires: fsspec, lightning-utilities, numpy, packaging, PyYAML, torch, torchmetrics, tqdm, typing-extensions\n",
            "Required-by: \n"
          ]
        }
      ],
      "source": [
        "!pip show pytorch-lightning"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eNLxwfcnEXPy"
      },
      "outputs": [],
      "source": [
        "#!pip install --upgrade pytorch-lightning"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XJpR1TmO5oJw"
      },
      "outputs": [],
      "source": [
        "import pytorch_lightning as pl"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "D5rxWgsWdV7e"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from tqdm.auto import tqdm\n",
        "\n",
        "import torch\n",
        "import torch.autograd as autograd\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gNhlzY_iTIR6"
      },
      "outputs": [],
      "source": [
        "\n",
        "import pytorch_lightning as pl\n",
        "import seaborn as sns\n",
        "from pylab import rcParams\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib import rc\n",
        "from matplotlib.ticker import MaxNLocator\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "from multiprocessing import cpu_count\n",
        "from pytorch_lightning.callbacks import ModelCheckpoint, EarlyStopping\n",
        "from pytorch_lightning.loggers import TensorBoardLogger\n",
        "# from pytorch_lightning.metrics.functional import accuracy\n",
        "from torchmetrics.functional import accuracy\n",
        "from torchmetrics import Accuracy\n",
        "from sklearn.metrics import classification_report,confusion_matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "L8-Tb6N-ecbH"
      },
      "outputs": [],
      "source": [
        "X_train=pd.read_csv(\"X_train.csv\")\n",
        "y_train=pd.read_csv(\"y_train.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "rqSmcqhfnWaL",
        "outputId": "a4f520bc-0c20-428c-b1d6-f5905eb3f2d5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "   angular_velocity_Z  linear_acceleration_X  linear_acceleration_Y  \\\n",
              "0            0.000767               -0.74857                 2.1030   \n",
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              "2           -0.005978               -0.26429                 1.5922   \n",
              "3           -0.008974                0.42684                 1.0993   \n",
              "4            0.005245               -0.50969                 1.4689   \n",
              "\n",
              "   linear_acceleration_Z  \n",
              "0                -9.7532  \n",
              "1                -9.4128  \n",
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              "  <style>\n",
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              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-dbb4a3c8-180c-49a3-8e50-d4813d86dafe 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-dbb4a3c8-180c-49a3-8e50-d4813d86dafe');\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",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        element.appendChild(docLink);\n",
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              "\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
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              "    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",
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              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-top-color: var(--fill-color);\n",
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              "    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-fe3d7d03-dd0e-45c0-86cc-102d3cb18234 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X_train"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "X_train.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "k3qxgSw2nXlI",
        "outputId": "7ce5fa55-f71e-4e64-ef2b-a1b015ec49bf"
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              "   series_id  group_id        surface\n",
              "0          0        13  fine_concrete\n",
              "1          1        31       concrete\n",
              "2          2        20       concrete\n",
              "3          3        31       concrete\n",
              "4          4        22     soft_tiles"
            ],
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "y_train",
              "summary": "{\n  \"name\": \"y_train\",\n  \"rows\": 3810,\n  \"fields\": [\n    {\n      \"column\": \"series_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1099,\n        \"min\": 0,\n        \"max\": 3809,\n        \"num_unique_values\": 3810,\n        \"samples\": [\n          1011,\n          3185,\n          3698\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"group_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20,\n        \"min\": 0,\n        \"max\": 72,\n        \"num_unique_values\": 73,\n        \"samples\": [\n          1,\n          66,\n          35\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"surface\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"hard_tiles\",\n          \"concrete\",\n          \"hard_tiles_large_space\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "\n",
        "y_train.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BaJdijJUXUwr",
        "outputId": "0a033385-e4eb-4530-9411-f18f26525ecd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 487680 entries, 0 to 487679\n",
            "Data columns (total 13 columns):\n",
            " #   Column                 Non-Null Count   Dtype  \n",
            "---  ------                 --------------   -----  \n",
            " 0   row_id                 487680 non-null  object \n",
            " 1   series_id              487680 non-null  int64  \n",
            " 2   measurement_number     487680 non-null  int64  \n",
            " 3   orientation_X          487680 non-null  float64\n",
            " 4   orientation_Y          487680 non-null  float64\n",
            " 5   orientation_Z          487680 non-null  float64\n",
            " 6   orientation_W          487680 non-null  float64\n",
            " 7   angular_velocity_X     487680 non-null  float64\n",
            " 8   angular_velocity_Y     487680 non-null  float64\n",
            " 9   angular_velocity_Z     487680 non-null  float64\n",
            " 10  linear_acceleration_X  487680 non-null  float64\n",
            " 11  linear_acceleration_Y  487680 non-null  float64\n",
            " 12  linear_acceleration_Z  487680 non-null  float64\n",
            "dtypes: float64(10), int64(2), object(1)\n",
            "memory usage: 48.4+ MB\n"
          ]
        }
      ],
      "source": [
        "X_train.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 537
        },
        "id": "RQzr17jET_tX",
        "outputId": "02d43e8f-71d0-41cc-c9ed-349c84455941"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "y_train.surface.value_counts().plot(kind=\"bar\")\n",
        "plt.xticks(rotation=45)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "I7IbUsA9ktj0"
      },
      "outputs": [],
      "source": [
        "label_encoder=LabelEncoder()\n",
        "encoded_labels=label_encoder.fit_transform(y_train.surface)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bwsGLdmfm5hP",
        "outputId": "53dd15d4-6ac7-45f3-8c81-8e6594c5cf37"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 1, 1, 1, 6, 7, 5, 1, 4, 7])"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "encoded_labels[:10]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c3WF9v4ynH27",
        "outputId": "20f6e926-5e33-4aaf-c3b4-23c6f41808ea"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['carpet', 'concrete', 'fine_concrete', 'hard_tiles',\n",
              "       'hard_tiles_large_space', 'soft_pvc', 'soft_tiles', 'tiled',\n",
              "       'wood'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "label_encoder.classes_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QmhzTg6onQ12"
      },
      "outputs": [],
      "source": [
        "y_train['label']=encoded_labels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "u5REAlhSnkPi",
        "outputId": "3e5926bc-b79d-4d54-da98-b03dfe446650"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   series_id  group_id        surface  label\n",
              "0          0        13  fine_concrete      2\n",
              "1          1        31       concrete      1\n",
              "2          2        20       concrete      1\n",
              "3          3        31       concrete      1\n",
              "4          4        22     soft_tiles      6"
            ],
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              "        document.querySelector('#df-c9aafca0-000e-404c-a27b-ad9a83f0decb button.colab-df-convert');\n",
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              "        const element = document.querySelector('#df-c9aafca0-000e-404c-a27b-ad9a83f0decb');\n",
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              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
              "\n",
              "<div id=\"df-072fa7de-dd91-4e8c-ade0-1ee267a59ab1\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-072fa7de-dd91-4e8c-ade0-1ee267a59ab1')\"\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-072fa7de-dd91-4e8c-ade0-1ee267a59ab1 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": "y_train",
              "summary": "{\n  \"name\": \"y_train\",\n  \"rows\": 3810,\n  \"fields\": [\n    {\n      \"column\": \"series_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1099,\n        \"min\": 0,\n        \"max\": 3809,\n        \"num_unique_values\": 3810,\n        \"samples\": [\n          1011,\n          3185,\n          3698\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"group_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20,\n        \"min\": 0,\n        \"max\": 72,\n        \"num_unique_values\": 73,\n        \"samples\": [\n          1,\n          66,\n          35\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"surface\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"hard_tiles\",\n          \"concrete\",\n          \"hard_tiles_large_space\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          3,\n          1,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "y_train.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "m-Ly0n8KnnMd",
        "outputId": "bc0df539-23a5-46a2-8bf3-9cb168dd10b5"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X_train"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-8b852024-51a4-486f-8019-5e92de064962\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>row_id</th>\n",
              "      <th>series_id</th>\n",
              "      <th>measurement_number</th>\n",
              "      <th>orientation_X</th>\n",
              "      <th>orientation_Y</th>\n",
              "      <th>orientation_Z</th>\n",
              "      <th>orientation_W</th>\n",
              "      <th>angular_velocity_X</th>\n",
              "      <th>angular_velocity_Y</th>\n",
              "      <th>angular_velocity_Z</th>\n",
              "      <th>linear_acceleration_X</th>\n",
              "      <th>linear_acceleration_Y</th>\n",
              "      <th>linear_acceleration_Z</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>0.107650</td>\n",
              "      <td>0.017561</td>\n",
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              "      <td>2.1030</td>\n",
              "      <td>-9.7532</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0_1</td>\n",
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              "      <td>-0.10600</td>\n",
              "      <td>0.067851</td>\n",
              "      <td>0.029939</td>\n",
              "      <td>0.003386</td>\n",
              "      <td>0.33995</td>\n",
              "      <td>1.5064</td>\n",
              "      <td>-9.4128</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0_2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>-0.75853</td>\n",
              "      <td>-0.63435</td>\n",
              "      <td>-0.10492</td>\n",
              "      <td>-0.10597</td>\n",
              "      <td>0.007275</td>\n",
              "      <td>0.028934</td>\n",
              "      <td>-0.005978</td>\n",
              "      <td>-0.26429</td>\n",
              "      <td>1.5922</td>\n",
              "      <td>-8.7267</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0_3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>-0.75852</td>\n",
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              "      <td>-0.10495</td>\n",
              "      <td>-0.10597</td>\n",
              "      <td>-0.013053</td>\n",
              "      <td>0.019448</td>\n",
              "      <td>-0.008974</td>\n",
              "      <td>0.42684</td>\n",
              "      <td>1.0993</td>\n",
              "      <td>-10.0960</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0_4</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>-0.75852</td>\n",
              "      <td>-0.63435</td>\n",
              "      <td>-0.10495</td>\n",
              "      <td>-0.10596</td>\n",
              "      <td>0.005135</td>\n",
              "      <td>0.007652</td>\n",
              "      <td>0.005245</td>\n",
              "      <td>-0.50969</td>\n",
              "      <td>1.4689</td>\n",
              "      <td>-10.4410</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8b852024-51a4-486f-8019-5e92de064962')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-8b852024-51a4-486f-8019-5e92de064962 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-8b852024-51a4-486f-8019-5e92de064962');\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-c5499577-4237-42df-945c-6c83d71a0328\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c5499577-4237-42df-945c-6c83d71a0328')\"\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-c5499577-4237-42df-945c-6c83d71a0328 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "  row_id  series_id  measurement_number  orientation_X  orientation_Y  \\\n",
              "0    0_0          0                   0       -0.75853       -0.63435   \n",
              "1    0_1          0                   1       -0.75853       -0.63434   \n",
              "2    0_2          0                   2       -0.75853       -0.63435   \n",
              "3    0_3          0                   3       -0.75852       -0.63436   \n",
              "4    0_4          0                   4       -0.75852       -0.63435   \n",
              "\n",
              "   orientation_Z  orientation_W  angular_velocity_X  angular_velocity_Y  \\\n",
              "0       -0.10488       -0.10597            0.107650            0.017561   \n",
              "1       -0.10490       -0.10600            0.067851            0.029939   \n",
              "2       -0.10492       -0.10597            0.007275            0.028934   \n",
              "3       -0.10495       -0.10597           -0.013053            0.019448   \n",
              "4       -0.10495       -0.10596            0.005135            0.007652   \n",
              "\n",
              "   angular_velocity_Z  linear_acceleration_X  linear_acceleration_Y  \\\n",
              "0            0.000767               -0.74857                 2.1030   \n",
              "1            0.003386                0.33995                 1.5064   \n",
              "2           -0.005978               -0.26429                 1.5922   \n",
              "3           -0.008974                0.42684                 1.0993   \n",
              "4            0.005245               -0.50969                 1.4689   \n",
              "\n",
              "   linear_acceleration_Z  \n",
              "0                -9.7532  \n",
              "1                -9.4128  \n",
              "2                -8.7267  \n",
              "3               -10.0960  \n",
              "4               -10.4410  "
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_train.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fkRBu96YntpB",
        "outputId": "546f8c66-7591-4e08-af74-eb41721257e2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['orientation_X',\n",
              " 'orientation_Y',\n",
              " 'orientation_Z',\n",
              " 'orientation_W',\n",
              " 'angular_velocity_X',\n",
              " 'angular_velocity_Y',\n",
              " 'angular_velocity_Z',\n",
              " 'linear_acceleration_X',\n",
              " 'linear_acceleration_Y',\n",
              " 'linear_acceleration_Z']"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "FEATURE_COLUMNS=X_train.columns.tolist()[3:]\n",
        "FEATURE_COLUMNS"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "18rW4xN3oDAx",
        "outputId": "94eb934c-fa07-4095-ec1e-2396e7543c7a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3809    128\n",
              "0       128\n",
              "3770    128\n",
              "3771    128\n",
              "3772    128\n",
              "       ... \n",
              "5       128\n",
              "6       128\n",
              "7       128\n",
              "8       128\n",
              "9       128\n",
              "Name: series_id, Length: 3810, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "X_train.series_id.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nb4BO3njoi4m",
        "outputId": "8d9bb28e-8279-410f-c57a-31c6b6028422"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "(X_train.series_id.value_counts()==128).sum() == len(y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aPCKGgxTo9Gl"
      },
      "outputs": [],
      "source": [
        "sequences=[]\n",
        "\n",
        "for series_id, group in X_train.groupby('series_id'):\n",
        "  sequences_features=group[FEATURE_COLUMNS]\n",
        "  label=y_train[y_train.series_id==series_id].iloc[0].label\n",
        "\n",
        "  sequences.append((sequences_features,label))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8rjMdHTTq-h_",
        "outputId": "f302bdec-7b93-4f53-a9da-77a983c6f1f9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(     orientation_X  orientation_Y  orientation_Z  orientation_W  \\\n",
              " 0         -0.75853       -0.63435       -0.10488       -0.10597   \n",
              " 1         -0.75853       -0.63434       -0.10490       -0.10600   \n",
              " 2         -0.75853       -0.63435       -0.10492       -0.10597   \n",
              " 3         -0.75852       -0.63436       -0.10495       -0.10597   \n",
              " 4         -0.75852       -0.63435       -0.10495       -0.10596   \n",
              " ..             ...            ...            ...            ...   \n",
              " 123       -0.75943       -0.63316       -0.10470       -0.10677   \n",
              " 124       -0.75945       -0.63313       -0.10473       -0.10683   \n",
              " 125       -0.75949       -0.63309       -0.10467       -0.10690   \n",
              " 126       -0.75950       -0.63307       -0.10464       -0.10693   \n",
              " 127       -0.75953       -0.63306       -0.10461       -0.10684   \n",
              " \n",
              "      angular_velocity_X  angular_velocity_Y  angular_velocity_Z  \\\n",
              " 0              0.107650            0.017561            0.000767   \n",
              " 1              0.067851            0.029939            0.003386   \n",
              " 2              0.007275            0.028934           -0.005978   \n",
              " 3             -0.013053            0.019448           -0.008974   \n",
              " 4              0.005135            0.007652            0.005245   \n",
              " ..                  ...                 ...                 ...   \n",
              " 123            0.041741            0.012857            0.016053   \n",
              " 124           -0.052041           -0.014752            0.021632   \n",
              " 125           -0.083083           -0.044480            0.031726   \n",
              " 126           -0.001305           -0.031012            0.017241   \n",
              " 127            0.073856            0.020353            0.018466   \n",
              " \n",
              "      linear_acceleration_X  linear_acceleration_Y  linear_acceleration_Z  \n",
              " 0                 -0.74857                2.10300                -9.7532  \n",
              " 1                  0.33995                1.50640                -9.4128  \n",
              " 2                 -0.26429                1.59220                -8.7267  \n",
              " 3                  0.42684                1.09930               -10.0960  \n",
              " 4                 -0.50969                1.46890               -10.4410  \n",
              " ..                     ...                    ...                    ...  \n",
              " 123               -0.80827                1.79890                -6.8687  \n",
              " 124               -0.36055                2.35620                -6.8926  \n",
              " 125               -0.98193                0.97328                -9.9795  \n",
              " 126               -0.72591                0.34931               -11.9770  \n",
              " 127               -0.53426                0.80346               -10.6970  \n",
              " \n",
              " [128 rows x 10 columns],\n",
              " 2)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "sequences[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Dkdof0ierBoT"
      },
      "outputs": [],
      "source": [
        "train_sequences, test_sequences=train_test_split(sequences,test_size=0.2)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ydy_Ix_krkKa",
        "outputId": "23f39c26-bcdd-4768-e247-9968464c9ae4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3048, 762)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "len(train_sequences),len(test_sequences)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GKlfkslrrrJJ"
      },
      "outputs": [],
      "source": [
        "class SurfaceDataset(Dataset):\n",
        "  def __init__(self,sequences):\n",
        "    self.sequences = sequences\n",
        "\n",
        "  def __len__(self):\n",
        "    return len(self.sequences)\n",
        "\n",
        "  def __getitem__(self,idx):\n",
        "    sequence,label=self.sequences[idx]\n",
        "    return dict(\n",
        "        sequence=torch.Tensor(sequence.to_numpy()),\n",
        "        label=torch.tensor(label).long()\n",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "P9cnA8vb8z0l"
      },
      "outputs": [],
      "source": [
        "class SurfaceDataModule(pl.LightningDataModule):\n",
        "  def __init__(self,train_sequences,test_sequences,batch_size):\n",
        "    super().__init__()\n",
        "    self.train_sequences = train_sequences\n",
        "    self.test_sequences = test_sequences\n",
        "    self.batch_size = batch_size\n",
        "\n",
        "\n",
        "  def setup(self,stage=None):\n",
        "    self.train_dataset=SurfaceDataset(self.train_sequences)\n",
        "    self.test_dataset=SurfaceDataset(self.test_sequences)\n",
        "\n",
        "  def train_dataloader(self):\n",
        "    return DataLoader(\n",
        "        self.train_dataset,\n",
        "        batch_size=self.batch_size,\n",
        "        #shuffles=True,\n",
        "        num_workers=cpu_count()\n",
        "    )\n",
        "  def val_dataloader(self):\n",
        "    return DataLoader(\n",
        "        self.test_dataset,\n",
        "        batch_size=self.batch_size,\n",
        "        #shuffles=False,\n",
        "        num_workers=cpu_count()\n",
        "    )\n",
        "  def test_dataloader(self):\n",
        "    return DataLoader(\n",
        "        self.test_dataset,\n",
        "        batch_size=self.batch_size,\n",
        "        #shuffles=False,\n",
        "        num_workers=cpu_count()\n",
        "\n",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iVqtGgKm8zo5"
      },
      "outputs": [],
      "source": [
        "N_EPOCHS =250\n",
        "BATCH_SIZE = 64\n",
        "\n",
        "data_module = SurfaceDataModule(train_sequences,test_sequences,BATCH_SIZE)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1O3Y4Mjds9g3"
      },
      "source": [
        "# MODEL"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9cerPMYNs7Rt"
      },
      "outputs": [],
      "source": [
        "class SequenceModel(nn.Module):\n",
        "  def __init__(self,n_features,n_classes,n_hidden=256,n_layers=3):\n",
        "    super().__init__()\n",
        "\n",
        "    self.lstm=nn.LSTM(\n",
        "        input_size=n_features,\n",
        "        hidden_size=n_hidden,\n",
        "        num_layers=n_layers,\n",
        "        batch_first=True,\n",
        "        dropout=0.75\n",
        "    )\n",
        "\n",
        "    self.classifier=nn.Linear(n_hidden,n_classes)\n",
        "\n",
        "  def forward(self,x):\n",
        "    self.lstm.flatten_parameters()\n",
        "\n",
        "    _, (hidden,_)=self.lstm(x)\n",
        "\n",
        "    out=hidden[-1]\n",
        "    return self.classifier(out)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pQDFYN3mLVdk"
      },
      "outputs": [],
      "source": [
        "!pip install wandb"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 611
        },
        "id": "ZSx0IoVLLN_-",
        "outputId": "564e1575-1658-4b7d-a300-e5d44b941d26"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "        window._wandbApiKey = new Promise((resolve, reject) => {\n",
              "            function loadScript(url) {\n",
              "            return new Promise(function(resolve, reject) {\n",
              "                let newScript = document.createElement(\"script\");\n",
              "                newScript.onerror = reject;\n",
              "                newScript.onload = resolve;\n",
              "                document.body.appendChild(newScript);\n",
              "                newScript.src = url;\n",
              "            });\n",
              "            }\n",
              "            loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n",
              "            const iframe = document.createElement('iframe')\n",
              "            iframe.style.cssText = \"width:0;height:0;border:none\"\n",
              "            document.body.appendChild(iframe)\n",
              "            const handshake = new Postmate({\n",
              "                container: iframe,\n",
              "                url: 'https://wandb.ai/authorize'\n",
              "            });\n",
              "            const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n",
              "            handshake.then(function(child) {\n",
              "                child.on('authorize', data => {\n",
              "                    clearTimeout(timeout)\n",
              "                    resolve(data)\n",
              "                });\n",
              "            });\n",
              "            })\n",
              "        });\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n",
            "wandb: Paste an API key from your profile and hit enter, or press ctrl+c to quit:"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " ··········\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Tracking run with wandb version 0.16.5"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Run data is saved locally in <code>/content/wandb/run-20240327_051628-73gabouj</code>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Syncing run <strong><a href='https://wandb.ai/007anirudhnandakumar/career_training/runs/73gabouj/workspace' target=\"_blank\">confused-frog-3</a></strong> to <a href='https://wandb.ai/007anirudhnandakumar/career_training' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View project at <a href='https://wandb.ai/007anirudhnandakumar/career_training' target=\"_blank\">https://wandb.ai/007anirudhnandakumar/career_training</a>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View run at <a href='https://wandb.ai/007anirudhnandakumar/career_training/runs/73gabouj/workspace' target=\"_blank\">https://wandb.ai/007anirudhnandakumar/career_training/runs/73gabouj/workspace</a>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<button onClick=\"this.nextSibling.style.display='block';this.style.display='none';\">Display W&B run</button><iframe src='https://wandb.ai/007anirudhnandakumar/career_training/runs/73gabouj?jupyter=true' style='border:none;width:100%;height:420px;display:none;'></iframe>"
            ],
            "text/plain": [
              "<wandb.sdk.wandb_run.Run at 0x7c3daa386f50>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "import wandb\n",
        "\n",
        "wandb.init(project=\"career_training\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qOc7qVE2S5sT"
      },
      "outputs": [],
      "source": [
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "p_3CYUjC5ZFH"
      },
      "outputs": [],
      "source": [
        "class SurfacePredictor(pl.LightningModule):\n",
        "  def __init__(self,n_features:int,n_classes:int):\n",
        "    super().__init__()\n",
        "    self.model=SequenceModel(n_features,n_classes)\n",
        "    self.criterion=nn.CrossEntropyLoss()\n",
        "\n",
        "  def forward(self,x,labels=None):\n",
        "    output=self.model(x)\n",
        "    loss=0\n",
        "    if labels is not None:\n",
        "      loss=self.criterion(output,labels)\n",
        "\n",
        "    # log wandb\n",
        "    wandb.log({\"loss\":loss})\n",
        "    return loss,output\n",
        "\n",
        "  def training_step(self,batch,batch_idx):\n",
        "    sequences=batch['sequence']\n",
        "    labels=batch['label']\n",
        "    loss,outputs=self(sequences,labels)\n",
        "    prediction = torch.argmax(outputs,dim=1)\n",
        "    step_accuracy = Accuracy(task=\"multiclass\", num_classes=10).to(device)(prediction, labels)\n",
        "    self.log(\"train_loss\",loss,prog_bar=True,logger=True)\n",
        "    self.log(\"train_accuracy\",step_accuracy,prog_bar=True,logger=True)\n",
        "    return {\"loss\":loss,\"accuracy\":step_accuracy}\n",
        "\n",
        "  def validation_step(self,batch,batch_idx):\n",
        "    sequences=batch['sequence']\n",
        "    labels=batch['label']\n",
        "    loss,outputs=self(sequences,labels)\n",
        "    prediction = torch.argmax(outputs,dim=1)\n",
        "    step_accuracy = Accuracy(task=\"multiclass\", num_classes=10).to(device)(prediction, labels)\n",
        "    self.log(\"val_loss\",loss,prog_bar=True,logger=True)\n",
        "    self.log(\"val_accuracy\",step_accuracy,prog_bar=True,logger=True)\n",
        "    return {\"loss\":loss,\"accuracy\":step_accuracy}\n",
        "\n",
        "  def test_step(self,batch,batch_idx):\n",
        "    sequences=batch['sequence']\n",
        "    labels=batch['label']\n",
        "    loss,outputs=self(sequences,labels)\n",
        "    prediction = torch.argmax(outputs,dim=1)\n",
        "    step_accuracy= Accuracy(task=\"multiclass\", num_classes=10).to(device)(prediction, labels)\n",
        "    self.log(\"test_loss\",loss,prog_bar=True,logger=True)\n",
        "    self.log(\"test_accuracy\",step_accuracy,prog_bar=True,logger=True)\n",
        "    return {\"loss\":loss,\"accuracy\":step_accuracy}\n",
        "\n",
        "\n",
        "\n",
        "  def configure_optimizers(self):\n",
        "    return optim.Adam(self.parameters(),lr=0.0001)\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LynJnCHo8iKr"
      },
      "outputs": [],
      "source": [
        "model = SurfacePredictor(\n",
        "    n_features=len(FEATURE_COLUMNS),\n",
        "    n_classes=len(label_encoder.classes_))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Dhjua7VBNBli"
      },
      "outputs": [],
      "source": [
        "from pytorch_lightning.loggers import WandbLogger"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hp5dhQgda7UZ",
        "outputId": "6c31c544-64ba-42ed-a62e-def9708f0c16"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n",
            "INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
            "INFO:pytorch_lightning.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
            "INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n"
          ]
        }
      ],
      "source": [
        "\n",
        "checkpoint_callback=ModelCheckpoint(\n",
        "    dirpath=\"checkpoints\",\n",
        "    filename=\"best-checkpoint\",\n",
        "    save_top_k=1,\n",
        "    verbose=True,\n",
        "    monitor=\"val_loss\",\n",
        "    mode=\"min\"\n",
        ")\n",
        "logger=WandbLogger('lightning_logs')\n",
        "\n",
        "trainer = pl.Trainer(logger=WandbLogger(project=\"career_training\"),\n",
        "                     callbacks=[checkpoint_callback],\n",
        "                     max_epochs=N_EPOCHS,\n",
        "                     accelerator=\"gpu\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loggers/wandb.py:391: There is a wandb run already in progress and newly created instances of `WandbLogger` will reuse this run. If this is not desired, call `wandb.finish()` before instantiating `WandbLogger`.\n",
            "INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "INFO:pytorch_lightning.callbacks.model_summary:\n",
            "  | Name      | Type             | Params\n",
            "-----------------------------------------------\n",
            "0 | model     | SequenceModel    | 1.3 M \n",
            "1 | criterion | CrossEntropyLoss | 0     \n",
            "-----------------------------------------------\n",
            "1.3 M     Trainable params\n",
            "0         Non-trainable params\n",
            "1.3 M     Total params\n",
            "5.318     Total estimated model params size (MB)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Sanity Checking: |          | 0/? [00:00<?, ?it/s]"
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              "model_id": "1364dcec04f947a8a60cf67489945190"
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        {
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          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/fit_loop.py:298: The number of training batches (48) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Training: |          | 0/? [00:00<?, ?it/s]"
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        {
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              "Validation: |          | 0/? [00:00<?, ?it/s]"
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              "model_id": "29448fff336e480799f890b34ef4d2d2"
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          "metadata": {}
        },
        {
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          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 0, global step 48: 'val_loss' reached 2.05724 (best 2.05724), saving model to '/content/checkpoints/best-checkpoint.ckpt' as top 1\n"
          ]
        },
        {
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          "data": {
            "text/plain": [
              "Validation: |          | 0/? [00:00<?, ?it/s]"
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          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 1, global step 96: 'val_loss' reached 2.03073 (best 2.03073), saving model to '/content/checkpoints/best-checkpoint.ckpt' as top 1\n"
          ]
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        {
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              "Validation: |          | 0/? [00:00<?, ?it/s]"
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          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 2, global step 144: 'val_loss' reached 2.02752 (best 2.02752), saving model to '/content/checkpoints/best-checkpoint.ckpt' as top 1\n"
          ]
        },
        {
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          "data": {
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              "Validation: |          | 0/? [00:00<?, ?it/s]"
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        {
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          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 3, global step 192: 'val_loss' reached 2.02101 (best 2.02101), saving model to '/content/checkpoints/best-checkpoint.ckpt' as top 1\n"
          ]
        },
        {
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          "data": {
            "text/plain": [
              "Validation: |          | 0/? [00:00<?, ?it/s]"
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 4, global step 240: 'val_loss' was not in top 1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: |          | 0/? [00:00<?, ?it/s]"
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        {
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          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 5, global step 288: 'val_loss' reached 1.97943 (best 1.97943), saving model to '/content/checkpoints/best-checkpoint.ckpt' as top 1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: |          | 0/? [00:00<?, ?it/s]"
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        {
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          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 6, global step 336: 'val_loss' was not in top 1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: |          | 0/? [00:00<?, ?it/s]"
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        {
          "output_type": "stream",
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          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 7, global step 384: 'val_loss' reached 1.86263 (best 1.86263), saving model to '/content/checkpoints/best-checkpoint.ckpt' as top 1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: |          | 0/? [00:00<?, ?it/s]"
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        {
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          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 8, global step 432: 'val_loss' reached 1.81478 (best 1.81478), saving model to '/content/checkpoints/best-checkpoint.ckpt' as top 1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: |          | 0/? [00:00<?, ?it/s]"
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        {
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          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 9, global step 480: 'val_loss' was not in top 1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: |          | 0/? [00:00<?, ?it/s]"
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          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 10, global step 528: 'val_loss' reached 1.67309 (best 1.67309), saving model to '/content/checkpoints/best-checkpoint.ckpt' as top 1\n"
          ]
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        {
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            "text/plain": [
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            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 11, global step 576: 'val_loss' reached 1.62127 (best 1.62127), saving model to '/content/checkpoints/best-checkpoint.ckpt' as top 1\n"
          ]
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        {
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            "text/plain": [
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          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 12, global step 624: 'val_loss' was not in top 1\n"
          ]
        },
        {
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          "data": {
            "text/plain": [
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          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 13, global step 672: 'val_loss' reached 1.56515 (best 1.56515), saving model to '/content/checkpoints/best-checkpoint.ckpt' as top 1\n"
          ]
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        {
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            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 14, global step 720: 'val_loss' reached 1.54227 (best 1.54227), saving model to '/content/checkpoints/best-checkpoint.ckpt' as top 1\n"
          ]
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            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 15, global step 768: 'val_loss' reached 1.52560 (best 1.52560), saving model to '/content/checkpoints/best-checkpoint.ckpt' as top 1\n"
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            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 227, global step 10944: 'val_loss' was not in top 1\n"
          ]
        },
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          "output_type": "display_data",
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            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 228, global step 10992: 'val_loss' was not in top 1\n"
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        {
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            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 229, global step 11040: 'val_loss' was not in top 1\n"
          ]
        },
        {
          "output_type": "display_data",
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        {
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          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 230, global step 11088: 'val_loss' was not in top 1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: |          | 0/? [00:00<?, ?it/s]"
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          "metadata": {}
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        {
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          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 231, global step 11136: 'val_loss' was not in top 1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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          "metadata": {}
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            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 232, global step 11184: 'val_loss' was not in top 1\n"
          ]
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          "metadata": {}
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        {
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            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 233, global step 11232: 'val_loss' was not in top 1\n"
          ]
        },
        {
          "output_type": "display_data",
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            "text/plain": [
              "Validation: |          | 0/? [00:00<?, ?it/s]"
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          "metadata": {}
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        {
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          "name": "stderr",
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            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 234, global step 11280: 'val_loss' was not in top 1\n"
          ]
        },
        {
          "output_type": "display_data",
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            "text/plain": [
              "Validation: |          | 0/? [00:00<?, ?it/s]"
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 235, global step 11328: 'val_loss' was not in top 1\n"
          ]
        },
        {
          "output_type": "display_data",
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        {
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            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 236, global step 11376: 'val_loss' was not in top 1\n"
          ]
        },
        {
          "output_type": "display_data",
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          "metadata": {}
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        {
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          "name": "stderr",
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            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 237, global step 11424: 'val_loss' was not in top 1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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        {
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          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 238, global step 11472: 'val_loss' was not in top 1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 239, global step 11520: 'val_loss' was not in top 1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: |          | 0/? [00:00<?, ?it/s]"
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 240, global step 11568: 'val_loss' was not in top 1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: |          | 0/? [00:00<?, ?it/s]"
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 241, global step 11616: 'val_loss' was not in top 1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: |          | 0/? [00:00<?, ?it/s]"
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 242, global step 11664: 'val_loss' was not in top 1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: |          | 0/? [00:00<?, ?it/s]"
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        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 243, global step 11712: 'val_loss' was not in top 1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: |          | 0/? [00:00<?, ?it/s]"
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 244, global step 11760: 'val_loss' was not in top 1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: |          | 0/? [00:00<?, ?it/s]"
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 245, global step 11808: 'val_loss' was not in top 1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: |          | 0/? [00:00<?, ?it/s]"
            ],
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 246, global step 11856: 'val_loss' was not in top 1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: |          | 0/? [00:00<?, ?it/s]"
            ],
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              "version_major": 2,
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              "model_id": "f03f99e07aab4a02950d7d08494d937c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 247, global step 11904: 'val_loss' was not in top 1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: |          | 0/? [00:00<?, ?it/s]"
            ],
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              "version_major": 2,
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              "model_id": "7364b3b501504fa0be24b5fa670956ae"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 248, global step 11952: 'val_loss' was not in top 1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: |          | 0/? [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3bab32b28e0d4a4bbac494370483050a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 249, global step 12000: 'val_loss' was not in top 1\n",
            "INFO:pytorch_lightning.utilities.rank_zero:`Trainer.fit` stopped: `max_epochs=250` reached.\n"
          ]
        }
      ],
      "source": [
        "trainer.fit(model, data_module)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.test(model,data_module)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 186,
          "referenced_widgets": [
            "a52abffe8a9145918153c0d04e7129d1",
            "9e182688f6164be498fe85a10acd15e8",
            "19933224748c45d39b3e7ffac25a2a7f",
            "af011aa4d7aa4fa18670a549e52a8b09",
            "c7d7d65720be43a18aafe2c2bc842ec6",
            "eda0bc6f65304ae8999cdd031e52b258",
            "561c88f19f5f4af0aa2c41c02f813b91",
            "5db3f83aedc645a48a90977b0d2917e7",
            "dc24f4128fc04271b505512f6423ec7f",
            "98beeb599b3e4157982e9ab0b5e5a811",
            "95148417b7264b67a0d32bb4cdb21e51"
          ]
        },
        "id": "N9rTwPtEZF8-",
        "outputId": "b9e06f94-3506-4a47-d8a5-8b41639cce7e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Testing: |          | 0/? [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a52abffe8a9145918153c0d04e7129d1"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1m       Test metric       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      DataLoader 0       \u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
              "│\u001b[36m \u001b[0m\u001b[36m      test_accuracy      \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.8070865869522095    \u001b[0m\u001b[35m \u001b[0m│\n",
              "│\u001b[36m \u001b[0m\u001b[36m        test_loss        \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.6015949249267578    \u001b[0m\u001b[35m \u001b[0m│\n",
              "└───────────────────────────┴───────────────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\">        Test metric        </span>┃<span style=\"font-weight: bold\">       DataLoader 0        </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
              "│<span style=\"color: #008080; text-decoration-color: #008080\">       test_accuracy       </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.8070865869522095     </span>│\n",
              "│<span style=\"color: #008080; text-decoration-color: #008080\">         test_loss         </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.6015949249267578     </span>│\n",
              "└───────────────────────────┴───────────────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'test_loss': 0.6015949249267578, 'test_accuracy': 0.8070865869522095}]"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Predictions"
      ],
      "metadata": {
        "id": "WEvnwVbJbLwV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "trained_model = SurfacePredictor.load_from_checkpoint(\n",
        "    trainer.checkpoint_callback.best_model_path,\n",
        "    n_features=len(FEATURE_COLUMNS),\n",
        "    n_classes=len(label_encoder.classes_)\n",
        "\n",
        ")\n",
        "trained_model.freeze()"
      ],
      "metadata": {
        "id": "Q0vi_yAZZF0B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ERROR FIX on device"
      ],
      "metadata": {
        "id": "u_hhm-U8eS4K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "device = next(trained_model.parameters()).device\n",
        "print(f\"Model device: {device}\")\n",
        "print(f\"Sequence device: {sequence.device}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0_CFr-NbdrXz",
        "outputId": "547ca0b7-46e9-4158-8ef2-65f9ae5a35f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model device: cuda:0\n",
            "Sequence device: cpu\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "if device != sequence.device:\n",
        "  trained_model.to(sequence.device)"
      ],
      "metadata": {
        "id": "KgFpHu_7eGyD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "device = next(trained_model.parameters()).device\n",
        "print(f\"Model device: {device}\")\n",
        "print(f\"Sequence device: {sequence.device}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r0qODeF9eKgW",
        "outputId": "7cdfbbc1-3534-4ee5-87f8-7d2fb29197e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model device: cpu\n",
            "Sequence device: cpu\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_dataset = SurfaceDataset(test_sequences)\n",
        "\n",
        "predictions=[]\n",
        "lables=[]\n",
        "\n",
        "for item in tqdm(test_dataset):\n",
        "  sequence=item['sequence']\n",
        "  label=item['label']\n",
        "\n",
        "  _, output = trained_model(sequence.unsqueeze(dim=0)) #RuntimeError: Input and parameter tensors are not at the same device, found input tensor at cpu and parameter tensor at cuda:0\n",
        "  prediction=torch.argmax(output,dim=1)\n",
        "  predictions.append(prediction.item())\n",
        "  lables.append(label.item())"
      ],
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              "  0%|          | 0/762 [00:00<?, ?it/s]"
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      "cell_type": "code",
      "source": [
        "print(classification_report(lables,predictions,\n",
        "                            target_names=label_encoder.classes_))"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                        precision    recall  f1-score   support\n",
            "\n",
            "                carpet       0.80      0.77      0.79        43\n",
            "              concrete       0.76      0.79      0.77       143\n",
            "         fine_concrete       0.78      0.73      0.75        81\n",
            "            hard_tiles       1.00      0.33      0.50         6\n",
            "hard_tiles_large_space       0.85      0.86      0.86        59\n",
            "              soft_pvc       0.87      0.83      0.85       145\n",
            "            soft_tiles       0.85      0.96      0.90        54\n",
            "                 tiled       0.82      0.82      0.82       105\n",
            "                  wood       0.76      0.78      0.77       126\n",
            "\n",
            "              accuracy                           0.81       762\n",
            "             macro avg       0.83      0.76      0.78       762\n",
            "          weighted avg       0.81      0.81      0.81       762\n",
            "\n"
          ]
        }
      ]
    },
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      "cell_type": "markdown",
      "metadata": {
        "id": "oDYjKgb15ezU"
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      "source": [
        "Error fix\n"
      ]
    },
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        },
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        "outputId": "8c6206ff-d0d5-495d-f6aa-048ccac0aef7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<__main__.SurfaceDataModule object at 0x781856b98100>\n"
          ]
        }
      ],
      "source": [
        "print(data_module)"
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    },
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      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<function SurfaceDataset.__getitem__ at 0x7818772ee440>\n"
          ]
        }
      ],
      "source": [
        "print(SurfaceDataset.__getitem__)"
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          "output_type": "stream",
          "text": [
            "<__main__.SurfaceDataset object at 0x781855dff820>\n"
          ]
        }
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      "source": [
        "print(data_module.train_dataloader().dataset)"
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          "text": [
            "<class 'torch.utils.data.dataloader.DataLoader'>\n"
          ]
        }
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        "# Print the type of data_module.train_dataloader()\n",
        "print(type(data_module.train_dataloader()))"
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    },
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          "text": [
            "0 sequence label\n",
            "1 sequence label\n",
            "2 sequence label\n",
            "3 sequence label\n",
            "4 sequence label\n",
            "5 sequence label\n",
            "6 sequence label\n",
            "7 sequence label\n",
            "8 sequence label\n",
            "9 sequence label\n",
            "10 sequence label\n"
          ]
        }
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