Pourya_Datathon5_DownSampling_ClassWeights.ipynb

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        "<a href=\"https://colab.research.google.com/github/Yutong-Lu/Datathon-5/blob/main/Pourya_Datathon5_DownSampling_ClassWeights.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/Yutong-Lu/Datathon-5.git"
      ],
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          "base_uri": "https://localhost:8080/"
        },
        "id": "If0rWR_AkTFy",
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      "id": "If0rWR_AkTFy",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'Datathon-5'...\n",
            "remote: Enumerating objects: 29, done.\u001b[K\n",
            "remote: Counting objects: 100% (29/29), done.\u001b[K\n",
            "remote: Compressing objects: 100% (28/28), done.\u001b[K\n",
            "remote: Total 29 (delta 14), reused 5 (delta 1), pack-reused 0\u001b[K\n",
            "Receiving objects: 100% (29/29), 665.77 KiB | 2.16 MiB/s, done.\n",
            "Resolving deltas: 100% (14/14), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cd ./Datathon-5"
      ],
      "metadata": {
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        },
        "id": "TbcVW4fclwUQ",
        "outputId": "0c90fd49-8e7b-44a4-c1e6-89a6c48ba659"
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      "id": "TbcVW4fclwUQ",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/Datathon-5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cd .."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1_65mS3qoyiq",
        "outputId": "8869b370-8e86-4196-c59f-5850faf877fc"
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      "id": "1_65mS3qoyiq",
      "execution_count": null,
      "outputs": [
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          "name": "stdout",
          "text": [
            "/content\n"
          ]
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    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "87dbe515",
      "metadata": {
        "id": "87dbe515"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "import torch as t\n",
        "import torch.nn as nn\n",
        "from torch.optim import Adam\n",
        "from torch.utils.data import TensorDataset, DataLoader\n",
        "\n",
        "import warnings"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "aec78f5f",
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              "      alx     aly      alz       glx      gly      glz     arx     ary  \\\n",
              "0  2.1849 -9.6967  0.63077  0.103900 -0.84053 -0.68762 -8.6499 -4.5781   \n",
              "1  2.3876 -9.5080  0.68389  0.085343 -0.83865 -0.68369 -8.6275 -4.3198   \n",
              "2  2.4086 -9.5674  0.68113  0.085343 -0.83865 -0.68369 -8.5055 -4.2772   \n",
              "3  2.1814 -9.4301  0.55031  0.085343 -0.83865 -0.68369 -8.6279 -4.3163   \n",
              "4  2.4173 -9.3889  0.71098  0.085343 -0.83865 -0.68369 -8.7008 -4.1459   \n",
              "\n",
              "        arz      grx     gry       grz  Activity   subject  \n",
              "0  0.187760 -0.44902 -1.0103  0.034483         0  subject1  \n",
              "1  0.023595 -0.44902 -1.0103  0.034483         0  subject1  \n",
              "2  0.275720 -0.44902 -1.0103  0.034483         0  subject1  \n",
              "3  0.367520 -0.45686 -1.0082  0.025862         0  subject1  \n",
              "4  0.407290 -0.45686 -1.0082  0.025862         0  subject1  "
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              "      <td>2.1849</td>\n",
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              "      <th>1</th>\n",
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              "      <td>-9.5080</td>\n",
              "      <td>0.68389</td>\n",
              "      <td>0.085343</td>\n",
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              "      <td>0.085343</td>\n",
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              "      <td>-0.68369</td>\n",
              "      <td>-8.5055</td>\n",
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              "      <td>0.275720</td>\n",
              "      <td>-0.44902</td>\n",
              "      <td>-1.0103</td>\n",
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              "      <th>3</th>\n",
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              "      <td>-9.4301</td>\n",
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              "      <td>-8.6279</td>\n",
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              "      <td>-1.0082</td>\n",
              "      <td>0.025862</td>\n",
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              "      <td>subject1</td>\n",
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              "      <td>-9.3889</td>\n",
              "      <td>0.71098</td>\n",
              "      <td>0.085343</td>\n",
              "      <td>-0.83865</td>\n",
              "      <td>-0.68369</td>\n",
              "      <td>-8.7008</td>\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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              "          + ' to learn more about interactive tables.';\n",
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              "\n",
              "  @keyframes spin {\n",
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              "    60% {\n",
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            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "# Load the CSV file into a DataFrame\n",
        "file_path = 'mhealth.csv'\n",
        "df = pd.read_csv(file_path)\n",
        "\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t1mDEgAG3yJg",
        "outputId": "6c6c9744-e76d-4209-8ed7-9ba95d738b23"
      },
      "id": "t1mDEgAG3yJg",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(999999, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
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    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6c7438be",
      "metadata": {
        "colab": {
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        },
        "id": "6c7438be",
        "outputId": "a207138a-de91-41ae-e1e1-5e8abdf964be"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "alx         0\n",
              "aly         0\n",
              "alz         0\n",
              "glx         0\n",
              "gly         0\n",
              "glz         0\n",
              "arx         0\n",
              "ary         0\n",
              "arz         0\n",
              "grx         0\n",
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              "grz         0\n",
              "Activity    0\n",
              "subject     0\n",
              "dtype: int64"
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      ],
      "source": [
        "# no missing value\n",
        "df.isnull().sum()"
      ]
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      "cell_type": "code",
      "execution_count": null,
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        {
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          "data": {
            "text/plain": [
              "subject1    161280\n",
              "subject2    130561\n",
              "subject8    129024\n",
              "subject3    122112\n",
              "subject5    119808\n",
              "subject4    116736\n",
              "subject7    104448\n",
              "subject6     98304\n",
              "subject9     17726\n",
              "Name: subject, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "# Check the number of sequence for each person\n",
        "df['subject'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fd5c8bd3",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fd5c8bd3",
        "outputId": "fbc1424f-ce3b-497d-de34-80ddbb9969db"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     720946\n",
              "1      27648\n",
              "2      25406\n",
              "3      24576\n",
              "4      24576\n",
              "9      24576\n",
              "10     24576\n",
              "11     24576\n",
              "5      24576\n",
              "7      23809\n",
              "8      23501\n",
              "6      22990\n",
              "12      8243\n",
              "Name: Activity, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "# Check activity frequency\n",
        "activity_counts = df['Activity'].value_counts()\n",
        "activity_counts"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "majority_class = df['Activity'].value_counts().idxmax()\n",
        "\n",
        "# Separate majority class and other classes\n",
        "majority_data = df[df['Activity'] == majority_class]\n",
        "other_classes_data = df[df['Activity'] != majority_class]\n",
        "\n",
        "# Determine the size of the majority class\n",
        "majority_size = len(majority_data)\n",
        "\n",
        "# Downsampling the majority class\n",
        "downsampled_majority = majority_data.sample(n=majority_size // 30, random_state=42)  # Downsample to half the size\n",
        "\n",
        "# Combine the downsampled majority class with other classes\n",
        "df = pd.concat([downsampled_majority, other_classes_data])"
      ],
      "metadata": {
        "id": "KB8hSmyzfY6j"
      },
      "id": "KB8hSmyzfY6j",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the total number of samples\n",
        "total_samples = df['Activity'].value_counts().sum()\n",
        "\n",
        "# Calculate inverse frequencies for each activity\n",
        "inverse_frequencies = total_samples / (len(activity_counts) * activity_counts)\n",
        "class_weights = t.tensor(inverse_frequencies.values, dtype=t.float32)\n",
        "class_weights"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q9tLP4eUZcIw",
        "outputId": "3801766b-4159-49af-c727-dfae69bc49ff"
      },
      "id": "q9tLP4eUZcIw",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([0.0323, 0.8432, 0.9177, 0.9487, 0.9487, 0.9487, 0.9487, 0.9487, 0.9487,\n",
              "        0.9792, 0.9920, 1.0141, 2.8284])"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_crosstab = pd.crosstab(df['subject'], df['Activity'], margins = False)\n",
        "print(data_crosstab)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6cybg2gMhP5e",
        "outputId": "0d513ef3-5db4-4dd2-8769-0f3f22ecef8b"
      },
      "id": "6cybg2gMhP5e",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Activity    0     1     2     3     4     5     6     7     8     9     10  \\\n",
            "subject                                                                      \n",
            "subject1  4214  3072  3072  3072  3072  3072  3072  3072  3379  3072  3072   \n",
            "subject2  3192  3072  3072  3072  3072  3072  3174  3328  3430  3072  3072   \n",
            "subject3  2878  3072  3072  3072  3072  3072  3226  3379  3175  3072  3072   \n",
            "subject4  2662  3072  3072  3072  3072  3072  3328  3277  3123  3072  3072   \n",
            "subject5  2896  3072  3072  3072  3072  3072  2765  2868  2714  3072  3072   \n",
            "subject6  2248  3072  3072  3072  3072  3072  2202  2099  2304  3072  3072   \n",
            "subject7  2326  3072  3072  3072  3072  3072  3072  2765  2816  3072  3072   \n",
            "subject8  3116  3072  3072  3072  3072  3072  2151  3021  2560  3072  3072   \n",
            "subject9   499  3072   830     0     0     0     0     0     0     0     0   \n",
            "\n",
            "Activity    11    12  \n",
            "subject               \n",
            "subject1  3072  1075  \n",
            "subject2  3072  1024  \n",
            "subject3  3072  1024  \n",
            "subject4  3072  1024  \n",
            "subject5  3072  1024  \n",
            "subject6  3072  1024  \n",
            "subject7  3072  1024  \n",
            "subject8  3072  1024  \n",
            "subject9     0     0  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "correlation_matrix = df.corr(numeric_only=True)\n",
        "\n",
        "# Plot the updated correlation heatmap\n",
        "plt.figure(figsize=(12, 10))\n",
        "sns.heatmap(correlation_matrix, annot=True, fmt=\".2f\", cmap='magma')\n",
        "plt.title('Correlation Heatmap', fontsize=15)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 855
        },
        "id": "gLfgEduzuMBf",
        "outputId": "4dfa7008-9d68-4792-c829-3a8f3819fcb0"
      },
      "id": "gLfgEduzuMBf",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d2580771",
      "metadata": {
        "id": "d2580771"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Grouping by 'patient_nbr' and creating a list of dataframes, one per group\n",
        "grouped = df.groupby('subject')\n",
        "grouped_dfs = [group for _, group in grouped]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "train_sets = []\n",
        "test_sets = []\n",
        "validation_sets = []\n",
        "for i in range(len(grouped_dfs)):\n",
        "    # Splitting the groups into training and test sets with an 80:20 ratio\n",
        "    train_set, test_set = train_test_split(grouped_dfs[i], test_size=0.2, random_state=42)\n",
        "    train_set, validation_set = train_test_split(train_set, test_size=0.2, random_state=42)\n",
        "    print(np.shape(train_set), np.shape(test_set), np.shape(validation_set))\n",
        "    numerical_columns = train_set.select_dtypes(include=['int64', 'float64']).columns.tolist()\n",
        "    numerical_columns.remove('Activity')  # Assuming 'Activity' is the target variable\n",
        "\n",
        "    scaler = StandardScaler()\n",
        "    train_set[numerical_columns] = scaler.fit_transform(train_set[numerical_columns])\n",
        "    validation_set[numerical_columns] = scaler.transform(validation_set[numerical_columns])\n",
        "    test_set[numerical_columns] = scaler.transform(test_set[numerical_columns])\n",
        "    train_sets.append(train_set)\n",
        "    test_sets.append(test_set)\n",
        "    validation_sets.append(validation_set)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4QyVRptzanxi",
        "outputId": "ea276468-ef1b-40d8-e588-442620973a35"
      },
      "id": "4QyVRptzanxi",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(25208, 14) (7878, 14) (6302, 14)\n",
            "(24783, 14) (7745, 14) (6196, 14)\n",
            "(24484, 14) (7652, 14) (6122, 14)\n",
            "(24313, 14) (7598, 14) (6079, 14)\n",
            "(23579, 14) (7369, 14) (5895, 14)\n",
            "(22049, 14) (6891, 14) (5513, 14)\n",
            "(23410, 14) (7316, 14) (5853, 14)\n",
            "(23326, 14) (7290, 14) (5832, 14)\n",
            "(2816, 14) (881, 14) (704, 14)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Create sequence for training set"
      ],
      "metadata": {
        "id": "GgAO-uHo2Z75"
      },
      "id": "GgAO-uHo2Z75"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6b4821a2",
      "metadata": {
        "id": "6b4821a2"
      },
      "outputs": [],
      "source": [
        "numerical_features = [\n",
        "    col for col in df.select_dtypes(include=[np.number]).columns\n",
        "    if col != 'Activity'\n",
        "]\n",
        "\n",
        "def create_sequences(data, sequence_length=100):\n",
        "    sequences = []\n",
        "    output = []\n",
        "    for i in range(0, len(data) - sequence_length + 1, sequence_length):\n",
        "        sequence = data[i:i + sequence_length]\n",
        "        # Assuming 'Activity' is the target variable and is the same for the entire sequence\n",
        "        label = sequence['Activity'].iloc[0]\n",
        "        sequences.append(sequence[numerical_features].values)\n",
        "        output.append(label)\n",
        "\n",
        "    return np.array(sequences), np.array(output)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating sequences for training and test data\n",
        "X_train_list = []\n",
        "y_train_list = []\n",
        "X_test_list = []\n",
        "y_test_list = []\n",
        "X_validation_list = []\n",
        "y_validation_list = []\n",
        "\n",
        "for i in range(len(train_sets)):\n",
        "    X_train, y_train = create_sequences(train_sets[i], sequence_length=100)\n",
        "    X_test, y_test = create_sequences(test_sets[i], sequence_length=100)\n",
        "    X_validation, y_validation = create_sequences(validation_sets[i], sequence_length=100)\n",
        "    print(X_train.shape, y_train.shape, X_test.shape, y_test.shape, X_validation.shape, y_validation.shape)\n",
        "    X_train_list.append(X_train)\n",
        "    y_train_list.append(y_train)\n",
        "    X_test_list.append(X_test)\n",
        "    y_test_list.append(y_test)\n",
        "    X_validation_list.append(X_validation)\n",
        "    y_validation_list.append(y_validation)"
      ],
      "metadata": {
        "id": "4GQ_JTOFeK5Z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aa08eef7-0207-4701-e6db-f8820b248497"
      },
      "id": "4GQ_JTOFeK5Z",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(252, 100, 12) (252,) (78, 100, 12) (78,) (63, 100, 12) (63,)\n",
            "(247, 100, 12) (247,) (77, 100, 12) (77,) (61, 100, 12) (61,)\n",
            "(244, 100, 12) (244,) (76, 100, 12) (76,) (61, 100, 12) (61,)\n",
            "(243, 100, 12) (243,) (75, 100, 12) (75,) (60, 100, 12) (60,)\n",
            "(235, 100, 12) (235,) (73, 100, 12) (73,) (58, 100, 12) (58,)\n",
            "(220, 100, 12) (220,) (68, 100, 12) (68,) (55, 100, 12) (55,)\n",
            "(234, 100, 12) (234,) (73, 100, 12) (73,) (58, 100, 12) (58,)\n",
            "(233, 100, 12) (233,) (72, 100, 12) (72,) (58, 100, 12) (58,)\n",
            "(28, 100, 12) (28,) (8, 100, 12) (8,) (7, 100, 12) (7,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = np.concatenate(X_train_list, axis=0)\n",
        "y_train = np.concatenate(y_train_list, axis=0)\n",
        "X_test = np.concatenate(X_test_list, axis=0)\n",
        "y_test = np.concatenate(y_test_list, axis=0)\n",
        "X_validation = np.concatenate(X_validation_list, axis=0)\n",
        "y_validation = np.concatenate(y_validation_list, axis=0)\n",
        "print(np.shape(X_train), np.shape(y_train), np.shape(X_test), np.shape(y_test), np.shape(X_validation), np.shape(y_validation))"
      ],
      "metadata": {
        "id": "RfPs84mKfcv8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1bcd7cc2-8e92-494d-e030-efacd54aadee"
      },
      "id": "RfPs84mKfcv8",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1936, 100, 12) (1936,) (600, 100, 12) (600,) (481, 100, 12) (481,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class Model(nn.Module):\n",
        "    def __init__(self, input_size, hidden_size, num_classes):\n",
        "        super(Model, self).__init__()\n",
        "\n",
        "        self.rnn_layer1 = nn.LSTM(input_size, hidden_size, batch_first=True)\n",
        "        self.dropout1 = nn.Dropout(0.5)\n",
        "        self.rnn_layer2 = nn.LSTM(hidden_size, hidden_size, batch_first=True)\n",
        "        self.dropout2 = nn.Dropout(0.5)\n",
        "        self.fc_layer1 = nn.Linear(hidden_size, hidden_size)\n",
        "        self.batch_normalization = nn.BatchNorm1d(hidden_size)\n",
        "        self.fc_layer2 = nn.Linear(hidden_size, hidden_size * 2)\n",
        "        self.batch_normalization2 = nn.BatchNorm1d(hidden_size * 2)\n",
        "        self.fc_layer3 = nn.Linear(hidden_size * 2, hidden_size)\n",
        "        self.batch_normalization3 = nn.BatchNorm1d(hidden_size)\n",
        "        self.fc_layer4 = nn.Linear(hidden_size, hidden_size // 2)\n",
        "        self.batch_normalization4 = nn.BatchNorm1d(hidden_size // 2)\n",
        "        self.fc_layer5 = nn.Linear(hidden_size // 2, num_classes)\n",
        "        self.dropout_fc = nn.Dropout(0.2)\n",
        "        self.relu = nn.ReLU()\n",
        "\n",
        "    def forward(self, x):\n",
        "        out, _ = self.rnn_layer1(x)\n",
        "        out = self.dropout1(out)\n",
        "        out, _ = self.rnn_layer2(out)\n",
        "        out = self.dropout2(out)\n",
        "        out = out[:, -1, :]\n",
        "        out = self.relu(self.fc_layer1(out))\n",
        "        out = self.batch_normalization(out)\n",
        "        out = self.relu(self.fc_layer2(out))\n",
        "        out = self.batch_normalization2(out)\n",
        "        out = self.dropout_fc(out)\n",
        "        out = self.relu(self.fc_layer3(out))\n",
        "        out = self.batch_normalization3(out)\n",
        "        out = self.relu(self.fc_layer4(out))\n",
        "        out = self.batch_normalization4(out)\n",
        "        out = self.dropout_fc(out)\n",
        "        out = self.fc_layer5(out)\n",
        "        return out"
      ],
      "metadata": {
        "id": "7SoiKfiJgJ25"
      },
      "id": "7SoiKfiJgJ25",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "input_size = X_train.shape[2]\n",
        "hidden_size = 256\n",
        "num_classes = 13\n",
        "learning_rate = 0.001\n",
        "batch_size = 256\n",
        "\n",
        "# Create an instance of the model\n",
        "model = Model(input_size, hidden_size, num_classes)\n",
        "\n",
        "# Loss function and optimizer\n",
        "loss_fn = nn.CrossEntropyLoss(weight=class_weights)\n",
        "optimizer = Adam(model.parameters(), lr=learning_rate)"
      ],
      "metadata": {
        "id": "N-VEozTehl5j"
      },
      "id": "N-VEozTehl5j",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Data preparation\n",
        "train_dataset = TensorDataset(t.tensor(X_train, dtype=t.float32), t.tensor(y_train, dtype=t.long))\n",
        "train_data_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
        "\n",
        "validation_dataset = TensorDataset(t.tensor(X_validation, dtype=t.float32), t.tensor(y_validation, dtype=t.long))\n",
        "validation_data_loader = DataLoader(validation_dataset, batch_size=batch_size, shuffle=False)\n",
        "\n",
        "test_dataset = TensorDataset(t.tensor(X_test, dtype=t.float32), t.tensor(y_test, dtype=t.long))\n",
        "test_data_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)"
      ],
      "metadata": {
        "id": "MlSDdO9JJV-M"
      },
      "id": "MlSDdO9JJV-M",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Lists to store accuracies and losses\n",
        "train_accuracy_list = []\n",
        "validation_accuracy_list = []\n",
        "train_loss_list = []\n",
        "\n",
        "best_validation_accuracy = 0.0\n",
        "best_model_state = None\n",
        "epochs = 50\n",
        "\n",
        "# Training loop\n",
        "for epoch in range(epochs):\n",
        "    if epoch % 10 == 0:\n",
        "        learning_rate *= 0.95  # Learning rate scheduling\n",
        "\n",
        "    per_epoch_loss_list = []\n",
        "    for X, Y in train_data_loader:\n",
        "        # Forward pass through the model\n",
        "        out = model(X)\n",
        "\n",
        "        # Loss calculation\n",
        "        loss = loss_fn(out, Y)\n",
        "        per_epoch_loss_list.append(loss.item())\n",
        "\n",
        "        # Backward pass and optimization\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        per_epoch_loss_list.append(loss.item())\n",
        "\n",
        "\n",
        "    # Evaluation on training and validation data\n",
        "    model.eval()\n",
        "\n",
        "    with t.no_grad():\n",
        "        # Training data\n",
        "        train_correct, train_total = 0, 0\n",
        "        for X, Y in train_data_loader:\n",
        "            out = model(X)\n",
        "            _, predicted = t.max(out.data, 1)\n",
        "            train_total += Y.size(0)\n",
        "            train_correct += (predicted == Y).sum().item()\n",
        "        train_accuracy = 100 * train_correct / train_total\n",
        "\n",
        "        # Validation data\n",
        "        validation_correct, validation_total = 0, 0\n",
        "        for X, Y in validation_data_loader:\n",
        "            out = model(X)\n",
        "            _, predicted = t.max(out.data, 1)\n",
        "            validation_total += Y.size(0)\n",
        "            validation_correct += (predicted == Y).sum().item()\n",
        "        validation_accuracy = 100 * validation_correct / validation_total\n",
        "\n",
        "        # Track best model and its state\n",
        "        if validation_accuracy > best_validation_accuracy:\n",
        "            best_validation_accuracy = validation_accuracy\n",
        "            best_model_state = {\n",
        "                'model': model.state_dict(),\n",
        "            }\n",
        "\n",
        "        # Print accuracy for the current epoch\n",
        "        print(f'Epoch {epoch}/{epochs} ---> Train Accuracy: {train_accuracy}%, Validation Accuracy: {validation_accuracy}%')\n",
        "\n",
        "        # Append accuracy values to lists\n",
        "        train_accuracy_list.append(train_accuracy)\n",
        "        validation_accuracy_list.append(validation_accuracy)\n",
        "\n",
        "    # Calculate and append the average loss for the epoch\n",
        "    train_loss_list.append(sum(per_epoch_loss_list) / len(per_epoch_loss_list))\n",
        "\n",
        "    # Set the model back to train mode\n",
        "    model.train()\n",
        "\n",
        "\n",
        "t.save(best_model_state, 'best_model.pth')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PhG-B63Suiad",
        "outputId": "35e779ab-efba-451a-ee68-3395d6e625d6"
      },
      "id": "PhG-B63Suiad",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 0/50 ---> Train Accuracy: 8.316115702479339%, Validation Accuracy: 8.731808731808732%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot training and validation accuracy over epochs\n",
        "plt.plot([i for i in range(len(train_accuracy_list))], train_accuracy_list, label=\"Train\")\n",
        "plt.plot([i for i in range(len(validation_accuracy_list))], validation_accuracy_list, label=\"Validation\")\n",
        "plt.legend(loc=\"upper left\")\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "xhgWkLQWwwkt"
      },
      "id": "xhgWkLQWwwkt",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the best model parameters\n",
        "best_model_state = t.load('best_model.pth')\n",
        "\n",
        "# Initialize a new model with the same architecture\n",
        "best_model = Model(input_size, hidden_size, num_classes)\n",
        "best_model.load_state_dict(best_model_state)\n",
        "\n",
        "# Set the best model to evaluation mode\n",
        "best_model.eval()\n",
        "\n",
        "# Loop through the test data and evaluate using the best model\n",
        "with t.no_grad():\n",
        "    test_correct = 0\n",
        "    test_total = 0\n",
        "    for X_test, Y_test in test_data_loader:\n",
        "        out = best_model(X_test)\n",
        "        _, predicted = t.max(out.data, 1)\n",
        "        test_total += Y_test.size(0)\n",
        "        test_correct += (predicted == Y_test).sum().item()\n",
        "    test_accuracy = 100 * test_correct / test_total\n",
        "\n",
        "# Print or use the test accuracy\n",
        "print(f'Test Accuracy: {test_accuracy}%')\n"
      ],
      "metadata": {
        "id": "6lxmoGu5dxT3"
      },
      "id": "6lxmoGu5dxT3",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "conf_matrix = confusion_matrix(test_targets, test_predictions)\n",
        "print('Confusion Matrix:')\n",
        "print(conf_matrix)"
      ],
      "metadata": {
        "id": "KsDgxvGFjI7g"
      },
      "id": "KsDgxvGFjI7g",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.4"
    },
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "include_colab_link": true
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 5
}