Assignmentpart1.ipynb

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      "name": "python"
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    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import pandas as pd\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "f1G4xKFThj6v"
      },
      "execution_count": 1,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "\n",
        "excel_file = pd.ExcelFile(\"data.xlsx\")\n",
        "\n",
        "data = pd.DataFrame()\n",
        "\n",
        "\n",
        "for sheet_name in excel_file.sheet_names:\n",
        "\n",
        "    sheet_data = excel_file.parse(sheet_name)\n",
        "    \n",
        "    data = pd.concat([data, sheet_data])\n",
        "\n"
      ],
      "metadata": {
        "id": "WAxiuaqYmGge"
      },
      "execution_count": 23,
      "outputs": []
    },
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        "data"
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      "execution_count": 24,
      "outputs": [
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            "text/plain": [
              "         AT      V       AP     RH      PE\n",
              "0     14.96  41.76  1024.07  73.17  463.26\n",
              "1     25.18  62.96  1020.04  59.08  444.37\n",
              "2      5.11  39.40  1012.16  92.14  488.56\n",
              "3     20.86  57.32  1010.24  76.64  446.48\n",
              "4     10.82  37.50  1009.23  96.62  473.90\n",
              "...     ...    ...      ...    ...     ...\n",
              "9563  15.12  48.92  1011.80  72.93  462.59\n",
              "9564  33.41  77.95  1010.30  59.72  432.90\n",
              "9565  15.99  43.34  1014.20  78.66  465.96\n",
              "9566  17.65  59.87  1018.58  94.65  450.93\n",
              "9567  23.68  51.30  1011.86  71.24  451.67\n",
              "\n",
              "[47840 rows x 5 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AT</th>\n",
              "      <th>V</th>\n",
              "      <th>AP</th>\n",
              "      <th>RH</th>\n",
              "      <th>PE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>14.96</td>\n",
              "      <td>41.76</td>\n",
              "      <td>1024.07</td>\n",
              "      <td>73.17</td>\n",
              "      <td>463.26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>25.18</td>\n",
              "      <td>62.96</td>\n",
              "      <td>1020.04</td>\n",
              "      <td>59.08</td>\n",
              "      <td>444.37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5.11</td>\n",
              "      <td>39.40</td>\n",
              "      <td>1012.16</td>\n",
              "      <td>92.14</td>\n",
              "      <td>488.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>20.86</td>\n",
              "      <td>57.32</td>\n",
              "      <td>1010.24</td>\n",
              "      <td>76.64</td>\n",
              "      <td>446.48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>10.82</td>\n",
              "      <td>37.50</td>\n",
              "      <td>1009.23</td>\n",
              "      <td>96.62</td>\n",
              "      <td>473.90</td>\n",
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              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>9563</th>\n",
              "      <td>15.12</td>\n",
              "      <td>48.92</td>\n",
              "      <td>1011.80</td>\n",
              "      <td>72.93</td>\n",
              "      <td>462.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9564</th>\n",
              "      <td>33.41</td>\n",
              "      <td>77.95</td>\n",
              "      <td>1010.30</td>\n",
              "      <td>59.72</td>\n",
              "      <td>432.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9565</th>\n",
              "      <td>15.99</td>\n",
              "      <td>43.34</td>\n",
              "      <td>1014.20</td>\n",
              "      <td>78.66</td>\n",
              "      <td>465.96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9566</th>\n",
              "      <td>17.65</td>\n",
              "      <td>59.87</td>\n",
              "      <td>1018.58</td>\n",
              "      <td>94.65</td>\n",
              "      <td>450.93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9567</th>\n",
              "      <td>23.68</td>\n",
              "      <td>51.30</td>\n",
              "      <td>1011.86</td>\n",
              "      <td>71.24</td>\n",
              "      <td>451.67</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>47840 rows × 5 columns</p>\n",
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              "\n",
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    {
      "cell_type": "code",
      "source": [
        "train, test = train_test_split(data, test_size=0.2, random_state=42)\n",
        "train, val = train_test_split(train, test_size=0.25, random_state=42)\n",
        "\n",
        "# normalize the input features and the output variable\n",
        "scaler_x = StandardScaler()\n",
        "scaler_y = StandardScaler()\n",
        "\n",
        "train_x = scaler_x.fit_transform(train.iloc[:,:-1].values)\n",
        "train_y = scaler_y.fit_transform(train.iloc[:,-1].values.reshape(-1,1))\n",
        "\n",
        "val_x = scaler_x.transform(val.iloc[:,:-1].values)\n",
        "val_y = scaler_y.transform(val.iloc[:,-1].values.reshape(-1,1))\n",
        "\n",
        "test_x = scaler_x.transform(test.iloc[:,:-1].values)\n",
        "test_y = scaler_y.transform(test.iloc[:,-1].values.reshape(-1,1))"
      ],
      "metadata": {
        "id": "LWTxhaIPhu64"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(train_x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5cM8i-kbjTxe",
        "outputId": "d1adafa6-03ea-47fb-c36d-626027438d09"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 1.28458868  1.56703203 -0.26703263 -0.05892276]\n",
            " [-0.81269777 -0.99184572  1.41444384 -0.71268882]\n",
            " [ 1.36087797  1.35110955 -0.53943182 -0.99845449]\n",
            " ...\n",
            " [ 1.96985393  1.85126454 -0.57474282 -1.05327764]\n",
            " [ 0.07600561  0.26600091  0.5098095   0.31044821]\n",
            " [ 0.09072811  0.24558642  0.18696602  0.66679869]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(train_y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "032FnFufjX7m",
        "outputId": "efde19a0-d4ef-4a52-eb4c-632964c4035e"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[-1.6221037 ]\n",
            " [ 1.02173861]\n",
            " [-1.28738836]\n",
            " ...\n",
            " [-1.36858458]\n",
            " [-0.09573164]\n",
            " [-0.11792917]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class Net(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Net, self).__init__()\n",
        "        self.fc1 = nn.Linear(4, 16)\n",
        "        self.fc2 = nn.Linear(16, 32)\n",
        "        self.fc3 = nn.Linear(32, 1)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = torch.relu(self.fc1(x))\n",
        "        x = torch.relu(self.fc2(x))\n",
        "        x = self.fc3(x)\n",
        "        return x"
      ],
      "metadata": {
        "id": "4qN19tlhh_80"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "net = Net()\n",
        "\n",
        "# define the loss function and the optimizer\n",
        "criterion = nn.MSELoss()\n",
        "optimizer = torch.optim.Adam(net.parameters(), lr=0.01)"
      ],
      "metadata": {
        "id": "Gbmwcl5kiIQX"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for epoch in range(1000):\n",
        "    optimizer.zero_grad()\n",
        "    outputs = net(torch.Tensor(train_x))\n",
        "    loss = criterion(outputs, torch.Tensor(train_y))\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "\n",
        "    if epoch % 100 == 0:\n",
        "        val_outputs = net(torch.Tensor(val_x))\n",
        "        val_loss = criterion(val_outputs, torch.Tensor(val_y))\n",
        "        print(\"Epoch: {}, Loss: {}, Validation Loss: {}\".format(epoch, loss.item(), val_loss.item()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "16Ack6IgiJdv",
        "outputId": "66bcfe2e-01e4-438d-adb7-a6a88365468c"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 0, Loss: 0.970426619052887, Validation Loss: 0.8352819085121155\n",
            "Epoch: 100, Loss: 0.06016606464982033, Validation Loss: 0.059740252792835236\n",
            "Epoch: 200, Loss: 0.05666033923625946, Validation Loss: 0.05620970577001572\n",
            "Epoch: 300, Loss: 0.055517349392175674, Validation Loss: 0.054999519139528275\n",
            "Epoch: 400, Loss: 0.054500989615917206, Validation Loss: 0.05410519614815712\n",
            "Epoch: 500, Loss: 0.05385856702923775, Validation Loss: 0.05362418293952942\n",
            "Epoch: 600, Loss: 0.05324777960777283, Validation Loss: 0.05312761664390564\n",
            "Epoch: 700, Loss: 0.05250966176390648, Validation Loss: 0.05260808393359184\n",
            "Epoch: 800, Loss: 0.05205231532454491, Validation Loss: 0.052301738411188126\n",
            "Epoch: 900, Loss: 0.05156160145998001, Validation Loss: 0.05183578282594681\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_outputs = net(torch.Tensor(test_x))\n",
        "print(test_outputs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OAibEBqPiT7v",
        "outputId": "e8ccc4f9-e8bc-43b6-f079-0f959e181877"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[-1.1972],\n",
            "        [ 0.8850],\n",
            "        [-0.8563],\n",
            "        ...,\n",
            "        [-1.4702],\n",
            "        [ 0.3486],\n",
            "        [ 1.6085]], grad_fn=<AddmmBackward0>)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_loss = criterion(test_outputs, torch.Tensor(test_y))\n",
        "print(\"Test Loss: {}\".format(test_loss.item()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z2hwMDfOip0K",
        "outputId": "79fd919c-6d0a-4344-c6e9-9d031213a5b9"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.05049177259206772\n"
          ]
        }
      ]
    }
  ]
}