ETTh1_(336)_Multivariate_AMD/notebook.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EZpopLd5U89C",
        "outputId": "406b78f4-77c4-4f02-bba9-f845b6e453f7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[33mhint: Using 'master' as the name for the initial branch. This default branch name\u001b[m\n",
            "\u001b[33mhint: is subject to change. To configure the initial branch name to use in all\u001b[m\n",
            "\u001b[33mhint: of your new repositories, which will suppress this warning, call:\u001b[m\n",
            "\u001b[33mhint: \u001b[m\n",
            "\u001b[33mhint: \tgit config --global init.defaultBranch <name>\u001b[m\n",
            "\u001b[33mhint: \u001b[m\n",
            "\u001b[33mhint: Names commonly chosen instead of 'master' are 'main', 'trunk' and\u001b[m\n",
            "\u001b[33mhint: 'development'. The just-created branch can be renamed via this command:\u001b[m\n",
            "\u001b[33mhint: \u001b[m\n",
            "\u001b[33mhint: \tgit branch -m <name>\u001b[m\n",
            "Initialized empty Git repository in /content/.git/\n",
            "remote: Enumerating objects: 57, done.\u001b[K\n",
            "remote: Counting objects: 100% (57/57), done.\u001b[K\n",
            "remote: Compressing objects: 100% (38/38), done.\u001b[K\n",
            "remote: Total 57 (delta 24), reused 43 (delta 18), pack-reused 0 (from 0)\u001b[K\n",
            "Unpacking objects: 100% (57/57), 1.81 MiB | 3.76 MiB/s, done.\n",
            "From https://github.com/TROUBADOUR000/AMD\n",
            " * [new branch]      main       -> origin/main\n",
            "Branch 'main' set up to track remote branch 'main' from 'origin'.\n",
            "Switched to a new branch 'main'\n"
          ]
        }
      ],
      "source": [
        "!git init\n",
        "!git remote add origin https://github.com/TROUBADOUR000/AMD.git\n",
        "!git fetch origin\n",
        "!git checkout main"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -r requirements.txt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "4JdxmQocVGvT",
        "outputId": "88f07e6f-1daa-40ad-add0-a05d64cbff70"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m54.6/54.6 MB\u001b[0m \u001b[31m13.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading nvidia_cusolver_cu11-11.4.0.1-2-py3-none-manylinux1_x86_64.whl (102.6 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m102.6/102.6 MB\u001b[0m \u001b[31m8.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading nvidia_cusparse_cu11-11.7.4.91-py3-none-manylinux1_x86_64.whl (173.2 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m173.2/173.2 MB\u001b[0m \u001b[31m6.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading nvidia_nccl_cu11-2.14.3-py3-none-manylinux1_x86_64.whl (177.1 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m177.1/177.1 MB\u001b[0m \u001b[31m6.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading nvidia_nvtx_cu11-11.7.91-py3-none-manylinux1_x86_64.whl (98 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m98.6/98.6 kB\u001b[0m \u001b[31m8.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading triton-2.0.0-1-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (63.3 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m63.3/63.3 MB\u001b[0m \u001b[31m11.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading scipy-1.15.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (37.7 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m37.7/37.7 MB\u001b[0m \u001b[31m48.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading lit-18.1.8-py3-none-any.whl (96 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m96.4/96.4 kB\u001b[0m \u001b[31m9.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: lit, tqdm, nvidia-nvtx-cu11, nvidia-nccl-cu11, nvidia-cusparse-cu11, nvidia-curand-cu11, nvidia-cufft-cu11, nvidia-cuda-runtime-cu11, nvidia-cuda-nvrtc-cu11, nvidia-cuda-cupti-cu11, nvidia-cublas-cu11, numpy, scipy, pandas, nvidia-cusolver-cu11, nvidia-cudnn-cu11, scikit_learn, triton, torch, torchvision, torchaudio\n",
            "  Attempting uninstall: tqdm\n",
            "    Found existing installation: tqdm 4.67.1\n",
            "    Uninstalling tqdm-4.67.1:\n",
            "      Successfully uninstalled tqdm-4.67.1\n",
            "  Attempting uninstall: numpy\n",
            "    Found existing installation: numpy 2.0.2\n",
            "    Uninstalling numpy-2.0.2:\n",
            "      Successfully uninstalled numpy-2.0.2\n",
            "  Attempting uninstall: scipy\n",
            "    Found existing installation: scipy 1.16.1\n",
            "    Uninstalling scipy-1.16.1:\n",
            "      Successfully uninstalled scipy-1.16.1\n",
            "  Attempting uninstall: pandas\n",
            "    Found existing installation: pandas 2.2.2\n",
            "    Uninstalling pandas-2.2.2:\n",
            "      Successfully uninstalled pandas-2.2.2\n",
            "  Attempting uninstall: scikit_learn\n",
            "    Found existing installation: scikit-learn 1.6.1\n",
            "    Uninstalling scikit-learn-1.6.1:\n",
            "      Successfully uninstalled scikit-learn-1.6.1\n",
            "  Attempting uninstall: triton\n",
            "    Found existing installation: triton 3.2.0\n",
            "    Uninstalling triton-3.2.0:\n",
            "      Successfully uninstalled triton-3.2.0\n",
            "  Attempting uninstall: torch\n",
            "    Found existing installation: torch 2.6.0+cu124\n",
            "    Uninstalling torch-2.6.0+cu124:\n",
            "      Successfully uninstalled torch-2.6.0+cu124\n",
            "  Attempting uninstall: torchvision\n",
            "    Found existing installation: torchvision 0.21.0+cu124\n",
            "    Uninstalling torchvision-0.21.0+cu124:\n",
            "      Successfully uninstalled torchvision-0.21.0+cu124\n",
            "  Attempting uninstall: torchaudio\n",
            "    Found existing installation: torchaudio 2.6.0+cu124\n",
            "    Uninstalling torchaudio-2.6.0+cu124:\n",
            "      Successfully uninstalled torchaudio-2.6.0+cu124\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "google-colab 1.0.0 requires pandas==2.2.2, but you have pandas 2.0.3 which is incompatible.\n",
            "cuml-cu12 25.6.0 requires scikit-learn>=1.5, but you have scikit-learn 1.3.2 which is incompatible.\n",
            "dataproc-spark-connect 0.8.3 requires tqdm>=4.67, but you have tqdm 4.66.2 which is incompatible.\n",
            "jax 0.5.3 requires numpy>=1.25, but you have numpy 1.24.3 which is incompatible.\n",
            "opencv-python 4.12.0.88 requires numpy<2.3.0,>=2; python_version >= \"3.9\", but you have numpy 1.24.3 which is incompatible.\n",
            "tensorflow 2.19.0 requires numpy<2.2.0,>=1.26.0, but you have numpy 1.24.3 which is incompatible.\n",
            "arviz 0.22.0 requires numpy>=1.26.0, but you have numpy 1.24.3 which is incompatible.\n",
            "arviz 0.22.0 requires pandas>=2.1.0, but you have pandas 2.0.3 which is incompatible.\n",
            "pywavelets 1.9.0 requires numpy<3,>=1.25, but you have numpy 1.24.3 which is incompatible.\n",
            "opencv-contrib-python 4.12.0.88 requires numpy<2.3.0,>=2; python_version >= \"3.9\", but you have numpy 1.24.3 which is incompatible.\n",
            "xarray-einstats 0.9.1 requires numpy>=1.25, but you have numpy 1.24.3 which is incompatible.\n",
            "contourpy 1.3.3 requires numpy>=1.25, but you have numpy 1.24.3 which is incompatible.\n",
            "opencv-python-headless 4.12.0.88 requires numpy<2.3.0,>=2; python_version >= \"3.9\", but you have numpy 1.24.3 which is incompatible.\n",
            "thinc 8.3.6 requires numpy<3.0.0,>=2.0.0, but you have numpy 1.24.3 which is incompatible.\n",
            "datasets 4.0.0 requires tqdm>=4.66.3, but you have tqdm 4.66.2 which is incompatible.\n",
            "pymc 5.25.1 requires numpy>=1.25.0, but you have numpy 1.24.3 which is incompatible.\n",
            "albucore 0.0.24 requires numpy>=1.24.4, but you have numpy 1.24.3 which is incompatible.\n",
            "umap-learn 0.5.9.post2 requires scikit-learn>=1.6, but you have scikit-learn 1.3.2 which is incompatible.\n",
            "xarray 2025.7.1 requires numpy>=1.26, but you have numpy 1.24.3 which is incompatible.\n",
            "xarray 2025.7.1 requires pandas>=2.2, but you have pandas 2.0.3 which is incompatible.\n",
            "treescope 0.1.10 requires numpy>=1.25.2, but you have numpy 1.24.3 which is incompatible.\n",
            "jaxlib 0.5.3 requires numpy>=1.25, but you have numpy 1.24.3 which is incompatible.\n",
            "mizani 0.13.5 requires pandas>=2.2.0, but you have pandas 2.0.3 which is incompatible.\n",
            "plotnine 0.14.5 requires pandas>=2.2.0, but you have pandas 2.0.3 which is incompatible.\n",
            "blosc2 3.7.0 requires numpy>=1.26, but you have numpy 1.24.3 which is incompatible.\n",
            "albumentations 2.0.8 requires numpy>=1.24.4, but you have numpy 1.24.3 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed lit-18.1.8 numpy-1.24.3 nvidia-cublas-cu11-11.10.3.66 nvidia-cuda-cupti-cu11-11.7.101 nvidia-cuda-nvrtc-cu11-11.7.99 nvidia-cuda-runtime-cu11-11.7.99 nvidia-cudnn-cu11-8.5.0.96 nvidia-cufft-cu11-10.9.0.58 nvidia-curand-cu11-10.2.10.91 nvidia-cusolver-cu11-11.4.0.1 nvidia-cusparse-cu11-11.7.4.91 nvidia-nccl-cu11-2.14.3 nvidia-nvtx-cu11-11.7.91 pandas-2.0.3 scikit_learn-1.3.2 scipy-1.15.3 torch-2.0.1 torchaudio-2.0.2 torchvision-0.15.2 tqdm-4.66.2 triton-2.0.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "numpy"
                ]
              },
              "id": "31aac0e217bb47ffaa17c16271815c16"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!chmod 755 /content/scripts/ETTh1.sh"
      ],
      "metadata": {
        "id": "t7OR77mmVJ4C"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!/content/scripts/ETTh1.sh"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WA4WMOdtVutg",
        "outputId": "25c49b3a-1eb4-4156-80a1-8c608f444420"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train :  8129\n",
            "valid :  2881\n",
            "test  :  2881\n",
            "10257030\n",
            "epoch : 1\n",
            "Train\n",
            "1/10      0.60144407 : 100% 62/62 [00:08<00:00,  7.71it/s]\n",
            "train loss: 0.6014440655708313, iter_time: 123.56097082937919\n",
            "Val\n",
            "          0.82040042: 100% 21/21 [00:00<00:00, 25.33it/s]\n",
            "val loss: 0.8204004168510437, val MSE: 0.8204004168510437, val MAE: 0.6221436262130737\n",
            "epoch : 2\n",
            "Train\n",
            "2/10      0.50662333 : 100% 62/62 [00:05<00:00, 11.55it/s]\n",
            "train loss: 0.5066233277320862, iter_time: 80.82215632161787\n",
            "Val\n",
            "          0.73682082: 100% 21/21 [00:00<00:00, 31.31it/s]\n",
            "val loss: 0.7368208169937134, val MSE: 0.7368208169937134, val MAE: 0.5843243598937988\n",
            "epoch : 3\n",
            "Train\n",
            "3/10      0.49296576 : 100% 62/62 [00:06<00:00, 10.15it/s]\n",
            "train loss: 0.4929657578468323, iter_time: 92.50934277811359\n",
            "Val\n",
            "          0.70448923: 100% 21/21 [00:00<00:00, 33.04it/s]\n",
            "val loss: 0.7044892311096191, val MSE: 0.7044892311096191, val MAE: 0.5698734521865845\n",
            "epoch : 4\n",
            "Train\n",
            "4/10      0.48098579 : 100% 62/62 [00:05<00:00, 11.51it/s]\n",
            "train loss: 0.4809857904911041, iter_time: 81.14419829460883\n",
            "Val\n",
            "          0.68483692: 100% 21/21 [00:00<00:00, 33.70it/s]\n",
            "val loss: 0.6848369240760803, val MSE: 0.6848369240760803, val MAE: 0.5607990026473999\n",
            "epoch : 5\n",
            "Train\n",
            "5/10      0.46872887 : 100% 62/62 [00:06<00:00,  9.88it/s]\n",
            "train loss: 0.4687288701534271, iter_time: 95.25842051352224\n",
            "Val\n",
            "          0.68098074: 100% 21/21 [00:00<00:00, 32.33it/s]\n",
            "val loss: 0.6809807419776917, val MSE: 0.6809807419776917, val MAE: 0.5585757493972778\n",
            "epoch : 6\n",
            "Train\n",
            "6/10      0.46532431 : 100% 62/62 [00:05<00:00, 11.14it/s]\n",
            "train loss: 0.4653243124485016, iter_time: 83.8801360899402\n",
            "Val\n",
            "          0.67921346: 100% 21/21 [00:00<00:00, 32.90it/s]\n",
            "val loss: 0.6792134642601013, val MSE: 0.6792134642601013, val MAE: 0.5564220547676086\n",
            "epoch : 7\n",
            "Train\n",
            "7/10      0.46003252 : 100% 62/62 [00:06<00:00, 10.08it/s]\n",
            "train loss: 0.46003252267837524, iter_time: 93.20614799376457\n",
            "Val\n",
            "          0.67554194: 100% 21/21 [00:00<00:00, 33.76it/s]\n",
            "val loss: 0.6755419373512268, val MSE: 0.6755419373512268, val MAE: 0.5554749369621277\n",
            "epoch : 8\n",
            "Train\n",
            "8/10      0.45266217 : 100% 62/62 [00:05<00:00, 11.41it/s]\n",
            "train loss: 0.4526621699333191, iter_time: 81.85278215715962\n",
            "Val\n",
            "          0.67506903: 100% 21/21 [00:00<00:00, 34.15it/s]\n",
            "val loss: 0.6750690340995789, val MSE: 0.6750690340995789, val MAE: 0.5550152063369751\n",
            "epoch : 9\n",
            "Train\n",
            "9/10      0.44773224 : 100% 62/62 [00:06<00:00, 10.29it/s]\n",
            "train loss: 0.44773223996162415, iter_time: 91.29196597683814\n",
            "Val\n",
            "          0.67277884: 100% 21/21 [00:00<00:00, 35.12it/s]\n",
            "val loss: 0.672778844833374, val MSE: 0.672778844833374, val MAE: 0.5537628531455994\n",
            "epoch : 10\n",
            "Train\n",
            "10/10     0.44881442 : 100% 62/62 [00:05<00:00, 11.31it/s]\n",
            "train loss: 0.44881442189216614, iter_time: 82.62132829235443\n",
            "Val\n",
            "          0.67874551: 100% 21/21 [00:00<00:00, 33.61it/s]\n",
            "val loss: 0.6787455081939697, val MSE: 0.6787455081939697, val MAE: 0.5550520420074463\n",
            "Final Test\n",
            "0.37052971: 100% 22/22 [00:00<00:00, 33.33it/s]\n",
            "test loss: 0.3705297112464905, test MSE: 0.3705297112464905, test MAE: 0.39880824089050293\n",
            "train :  8129\n",
            "valid :  2881\n",
            "test  :  2881\n",
            "11830662\n",
            "epoch : 1\n",
            "Train\n",
            "1/10      0.65102619 : 100% 62/62 [00:06<00:00, 10.33it/s]\n",
            "train loss: 0.65102618932724, iter_time: 90.06819032853649\n",
            "Val\n",
            "          1.0254356 : 100% 21/21 [00:00<00:00, 32.56it/s]\n",
            "val loss: 1.0254355669021606, val MSE: 1.0254355669021606, val MAE: 0.6977430582046509\n",
            "epoch : 2\n",
            "Train\n",
            "2/10      0.56224662 : 100% 62/62 [00:06<00:00,  9.86it/s]\n",
            "train loss: 0.5622466206550598, iter_time: 94.55450888602964\n",
            "Val\n",
            "          0.97521573: 100% 21/21 [00:00<00:00, 30.56it/s]\n",
            "val loss: 0.9752157330513, val MSE: 0.9752157330513, val MAE: 0.6721831560134888\n",
            "epoch : 3\n",
            "Train\n",
            "3/10      0.53594863 : 100% 62/62 [00:05<00:00, 10.97it/s]\n",
            "train loss: 0.535948634147644, iter_time: 84.49610971635386\n",
            "Val\n",
            "          0.94620836: 100% 21/21 [00:00<00:00, 32.13it/s]\n",
            "val loss: 0.9462083578109741, val MSE: 0.9462083578109741, val MAE: 0.658670961856842\n",
            "epoch : 4\n",
            "Train\n",
            "4/10      0.5283004  : 100% 62/62 [00:06<00:00,  9.87it/s]\n",
            "train loss: 0.528300404548645, iter_time: 94.54969821437713\n",
            "Val\n",
            "          0.93700159: 100% 21/21 [00:00<00:00, 31.58it/s]\n",
            "val loss: 0.9370015859603882, val MSE: 0.9370015859603882, val MAE: 0.6544781923294067\n",
            "epoch : 5\n",
            "Train\n",
            "5/10      0.52128452 : 100% 62/62 [00:05<00:00, 10.91it/s]\n",
            "train loss: 0.5212845206260681, iter_time: 85.06250766015822\n",
            "Val\n",
            "          0.93003291: 100% 21/21 [00:00<00:00, 26.47it/s]\n",
            "val loss: 0.9300329089164734, val MSE: 0.9300329089164734, val MAE: 0.6511669754981995\n",
            "epoch : 6\n",
            "Train\n",
            "6/10      0.51188546 :  44% 27/62 [00:02<00:03,  9.48it/s]\n",
            "Traceback (most recent call last):\n",
            "  File \"/content/main.py\", line 292, in <module>\n",
            "    main(args)\n",
            "  File \"/content/main.py\", line 98, in main\n",
            "    loss.backward()\n",
            "  File \"/usr/local/lib/python3.11/dist-packages/torch/_tensor.py\", line 487, in backward\n",
            "    torch.autograd.backward(\n",
            "  File \"/usr/local/lib/python3.11/dist-packages/torch/autograd/__init__.py\", line 200, in backward\n",
            "    Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass\n",
            "KeyboardInterrupt\n",
            "^C\n"
          ]
        }
      ]
    }
  ]
}