transformer.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/tears743/AiMate/blob/main/transformer.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CjzfP9SJrWja"
      },
      "source": [
        "```\n",
        "````引入训练集"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "9SErbXm5rWjd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8bbaa6d8-c048-42b2-cb59-c3a235e0b740"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'paper'...\n",
            "remote: Enumerating objects: 373, done.\u001b[K\n",
            "remote: Counting objects: 100% (373/373), done.\u001b[K\n",
            "remote: Compressing objects: 100% (243/243), done.\u001b[K\n",
            "remote: Total 373 (delta 157), reused 311 (delta 103), pack-reused 0\u001b[K\n",
            "Receiving objects: 100% (373/373), 11.61 MiB | 17.69 MiB/s, done.\n",
            "Resolving deltas: 100% (157/157), done.\n"
          ]
        }
      ],
      "source": [
        "\n",
        "!git clone https://tears743: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@github.com/tears743/paper.git\n",
        "# !pip install reformer_pytorch\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# !pip install reformer_pytorch\n",
        "%cd paper\n",
        "!ls"
      ],
      "metadata": {
        "id": "rOyZBxxfsnLG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "56708bec-de3c-41f9-c0ea-315e23f93aa7"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/paper\n",
            "blood_itransformer.ipynb  figures  paper\t\t\t  run.py\n",
            "data_provider\t\t  layers   README.md\t\t\t  scripts\n",
            "dataset\t\t\t  LICENSE  requirements.txt\t\t  transformer.ipynb\n",
            "experiments\t\t  model    result_long_term_forecast.txt  utils\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# from google.colab import drive\n",
        "# # drive.mount('/content/drive')\n",
        "# # !pwd\n",
        "# !cat /content/drive/MyDrive/Colab\\ Notebooks/make_csv_dataset.py\n",
        "# # ! cp /content/drive/blood/colab/ /content/paper/paper/code/make_csv_dataset.py\n",
        "# %cd paper\n",
        "\n",
        "import sys\n",
        "sys.path.append('./paper/code/')\n",
        "sys.path.append('./data_loader')\n",
        "sys.path.append('./dataset')\n",
        "sys.path.append('./experiments')\n",
        "sys.path.append('./layers')\n",
        "sys.path.append('./model')\n",
        "sys.path.append('./util')"
      ],
      "metadata": {
        "id": "SsnYWnY9sdeV"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "h_-eMhQP7Sik",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "42a4eba0-0d0a-4e24-87b9-ca03a3802109"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/paper\n"
          ]
        }
      ],
      "source": [
        "import make_csv_dataset\n",
        "import random\n",
        "import numpy as np\n",
        "\n",
        "dataLoader = make_csv_dataset.DataLoader()\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import sys\n",
        "\n",
        "sys.path.append('./data_loader')\n",
        "sys.path.append('./dataset')\n",
        "sys.path.append('./experiments')\n",
        "sys.path.append('./layers')\n",
        "sys.path.append('./model')\n",
        "sys.path.append('./util')\n",
        "!pip install reformer_pytorch"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l9dpwelY0rHl",
        "outputId": "1844ca83-bcb9-46e6-b1af-f65dfe0dbbd8"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting reformer_pytorch\n",
            "  Downloading reformer_pytorch-1.4.4-py3-none-any.whl (16 kB)\n",
            "Collecting axial-positional-embedding>=0.1.0 (from reformer_pytorch)\n",
            "  Downloading axial_positional_embedding-0.2.1.tar.gz (2.6 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting einops (from reformer_pytorch)\n",
            "  Downloading einops-0.7.0-py3-none-any.whl (44 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.6/44.6 kB\u001b[0m \u001b[31m2.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting local-attention (from reformer_pytorch)\n",
            "  Downloading local_attention-1.9.0-py3-none-any.whl (8.2 kB)\n",
            "Collecting product-key-memory (from reformer_pytorch)\n",
            "  Downloading product_key_memory-0.2.10-py3-none-any.whl (6.4 kB)\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (from reformer_pytorch) (2.1.0+cu118)\n",
            "Collecting colt5-attention>=0.10.14 (from product-key-memory->reformer_pytorch)\n",
            "  Downloading CoLT5_attention-0.10.19-py3-none-any.whl (18 kB)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch->reformer_pytorch) (3.13.1)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch->reformer_pytorch) (4.5.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch->reformer_pytorch) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch->reformer_pytorch) (3.2.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch->reformer_pytorch) (3.1.2)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch->reformer_pytorch) (2023.6.0)\n",
            "Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch->reformer_pytorch) (2.1.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from colt5-attention>=0.10.14->product-key-memory->reformer_pytorch) (23.2)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch->reformer_pytorch) (2.1.3)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch->reformer_pytorch) (1.3.0)\n",
            "Building wheels for collected packages: axial-positional-embedding\n",
            "  Building wheel for axial-positional-embedding (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for axial-positional-embedding: filename=axial_positional_embedding-0.2.1-py3-none-any.whl size=2882 sha256=f25bb84571a539b7d3761d794ad953be0a7c4d46d5b1fb89c532f6f78038b4c3\n",
            "  Stored in directory: /root/.cache/pip/wheels/b1/cb/39/7ce7ff2d2fd37cfe1fe7b3a3c43cf410632b2ad3b3f3986d73\n",
            "Successfully built axial-positional-embedding\n",
            "Installing collected packages: einops, local-attention, axial-positional-embedding, colt5-attention, product-key-memory, reformer_pytorch\n",
            "Successfully installed axial-positional-embedding-0.2.1 colt5-attention-0.10.19 einops-0.7.0 local-attention-1.9.0 product-key-memory-0.2.10 reformer_pytorch-1.4.4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "DuVxEzhPrWjg"
      },
      "outputs": [],
      "source": [
        "from experiments.exp_long_term_forecasting import Exp_Long_Term_Forecast\n",
        "from experiments.exp_long_term_forecasting_partial import Exp_Long_Term_Forecast_Partial\n",
        "import torch\n",
        "import types\n",
        "\n",
        "fix_seed = 2023\n",
        "random.seed(fix_seed)\n",
        "torch.manual_seed(fix_seed)\n",
        "np.random.seed(fix_seed)\n",
        "\n",
        "def build_args(is_training=True,\n",
        "               model_id='test',\n",
        "               model='iTransformer',\n",
        "               data='custom',\n",
        "               root_path='dataset/blood_station',\n",
        "               data_path='multi_input_without_feature_data.csv',\n",
        "               features='MS',\n",
        "               target='OT',\n",
        "               freq='m',\n",
        "               checkpoints='./checkpoints/',\n",
        "            # forecasting task\n",
        "               seq_len=12,\n",
        "               label_len=12,\n",
        "               pred_len=1,\n",
        "            # model define\n",
        "                enc_in = 12,\n",
        "                dec_in = 12,\n",
        "                c_out = 12,\n",
        "                d_model = 2048,\n",
        "                n_heads = 8,\n",
        "                e_layers = 2,\n",
        "                d_layers = 1,\n",
        "                d_ff = 8192,\n",
        "                moving_avg = 12,\n",
        "                factor = 1,\n",
        "                distil = False,\n",
        "                dropout = 0.2,\n",
        "                embed = 'timeF',\n",
        "                activation = 'gelu',\n",
        "                output_attention = False,\n",
        "                do_predict = False,\n",
        "            # optimizationxw\n",
        "                num_workers = 10,\n",
        "                itr = 1,\n",
        "                train_epochs = 30,\n",
        "                batch_size = 3,\n",
        "                patience = 10,\n",
        "                learning_rate = 0.0001,\n",
        "                des='test',\n",
        "                loss = 'MSE',\n",
        "                lradj = 'type1',\n",
        "                use_amp = False,\n",
        "             # GPU\n",
        "                use_gpu = True,\n",
        "                gpu = 0,\n",
        "                use_multi_gpu = False,\n",
        "                devices='0,1,2,3',\n",
        "            # iTransformer\n",
        "                exp_name='MTSF',\n",
        "                channel_independence=True,\n",
        "                inverse = False,\n",
        "                class_strategy = 'projection',\n",
        "                target_root_path = './data/blood/',\n",
        "                target_data_path = 'test.csv',\n",
        "                efficient_training= False\n",
        "               ):\n",
        "    params = {\n",
        "               \"is_training\":is_training,\n",
        "               \"model_id\":model_id,\n",
        "               \"model\":model,\n",
        "               \"data\":data,\n",
        "               \"root_path\":root_path,\n",
        "               \"data_path\":data_path,\n",
        "               \"features\":features,\n",
        "               \"target\":target,\n",
        "               \"freq\":freq,\n",
        "               \"checkpoints\":checkpoints,\n",
        "            # forecasting task\n",
        "               \"seq_len\":seq_len,\n",
        "               \"label_len\":label_len,\n",
        "               \"pred_len\":pred_len,\n",
        "            # model define\n",
        "                \"enc_in\":enc_in ,\n",
        "                \"dec_in\":dec_in ,\n",
        "                \"c_out\":c_out,\n",
        "                \"d_model\" :d_model,\n",
        "                \"n_heads\" :n_heads,\n",
        "                \"e_layers\":e_layers ,\n",
        "                \"d_layers\":d_layers ,\n",
        "                \"d_ff\":d_ff ,\n",
        "                \"moving_avg\":moving_avg,\n",
        "                \"factor\":factor ,\n",
        "                \"distil\":distil ,\n",
        "                \"dropout\":dropout ,\n",
        "                \"embed\":embed ,\n",
        "                \"activation\":activation ,\n",
        "                \"output_attention\":output_attention,\n",
        "                \"do_predict\":do_predict,\n",
        "            # optimization\n",
        "                \"num_workers\":num_workers ,\n",
        "                \"itr\":itr ,\n",
        "                \"train_epochs\":train_epochs ,\n",
        "                \"batch_size\":batch_size ,\n",
        "                \"patience\":patience ,\n",
        "                \"learning_rate\":learning_rate,\n",
        "                \"des\":des,\n",
        "                \"loss\":loss ,\n",
        "                \"lradj\":lradj ,\n",
        "                \"use_amp\":use_amp ,\n",
        "             # GPU\n",
        "                \"use_gpu\" :use_gpu,\n",
        "                \"gpu\":gpu ,\n",
        "                \"use_multi_gpu\":use_multi_gpu ,\n",
        "                \"devices\":devices,\n",
        "            # iTransformer\n",
        "                \"exp_name\":exp_name,\n",
        "                \"channel_independence\":channel_independence,\n",
        "                \"inverse\":inverse ,\n",
        "                \"class_strategy\":class_strategy ,\n",
        "                \"target_root_path\":target_root_path ,\n",
        "                \"target_data_path\":target_data_path ,\n",
        "                \"efficient_training\":efficient_training\n",
        "    }\n",
        "    args = types.SimpleNamespace(**params)\n",
        "    args.use_gpu = True if torch.cuda.is_available() and args.use_gpu else False\n",
        "    if args.use_gpu and args.use_multi_gpu:\n",
        "        args.devices = args.devices.replace(' ', '')\n",
        "        device_ids = args.devices.split(',')\n",
        "        args.device_ids = [int(id_) for id_ in device_ids]\n",
        "        args.gpu = args.device_ids[0]\n",
        "    return args\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "VAFXPhdqrWjh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "1cdd38e3-6a0c-4318-9234-6a82ce08554e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "trainning:%s 血小板O\n",
            "namespace(is_training=True, model_id='input_血小板O_', model='iTransformer', data='custom', root_path='dataset/blood_station', data_path='multi_input_without_feature_data.csv', features='MS', target='血小板O', freq='m', checkpoints='./checkpoints/', seq_len=12, label_len=12, pred_len=1, enc_in=12, dec_in=12, c_out=12, d_model=2048, n_heads=8, e_layers=2, d_layers=1, d_ff=8192, moving_avg=12, factor=1, distil=False, dropout=0.2, embed='timeF', activation='gelu', output_attention=False, do_predict=False, num_workers=10, itr=1, train_epochs=30, batch_size=3, patience=10, learning_rate=0.0001, des='test', loss='MSE', lradj='type1', use_amp=False, use_gpu=True, gpu=0, use_multi_gpu=False, devices='0,1,2,3', exp_name='MTSF', channel_independence=True, inverse=False, class_strategy='projection', target_root_path='./data/blood/', target_data_path='test.csv', efficient_training=False)\n",
            "Use GPU: cuda:0\n",
            ">>>>>>>start training : input_血小板O__iTransformer_custom_MS_ft12_sl12_ll1_pl2048_dm8_nh2_el1_dl8192_df1_fctimeF_ebFalse_dttest_projection_0>>>>>>>>>>>>>>>>>>>>>>>>>>\n",
            "train 80\n",
            "val 14\n",
            "test 26\n",
            "26\n",
            "Epoch: 1 cost time: 8.521039724349976\n",
            "Epoch: 1, Steps: 26 | Train Loss: 1.9257506 Vali Loss: 1.5426686 Test Loss: 1.2707757\n",
            "Validation loss decreased (inf --> 1.542669).  Saving model ...\n",
            "Updating learning rate to 0.0001\n",
            "Epoch: 2 cost time: 1.971348524093628\n",
            "Epoch: 2, Steps: 26 | Train Loss: 0.6419911 Vali Loss: 1.0715271 Test Loss: 2.4810328\n",
            "Validation loss decreased (1.542669 --> 1.071527).  Saving model ...\n",
            "Updating learning rate to 5e-05\n",
            "Epoch: 3 cost time: 1.9471285343170166\n",
            "Epoch: 3, Steps: 26 | Train Loss: 0.4527230 Vali Loss: 0.4206983 Test Loss: 0.6020308\n",
            "Validation loss decreased (1.071527 --> 0.420698).  Saving model ...\n",
            "Updating learning rate to 2.5e-05\n",
            "Epoch: 4 cost time: 2.2845709323883057\n",
            "Epoch: 4, Steps: 26 | Train Loss: 0.1724062 Vali Loss: 0.3824535 Test Loss: 0.7133849\n",
            "Validation loss decreased (0.420698 --> 0.382453).  Saving model ...\n",
            "Updating learning rate to 1.25e-05\n",
            "Epoch: 5 cost time: 1.9537835121154785\n",
            "Epoch: 5, Steps: 26 | Train Loss: 0.1086592 Vali Loss: 0.5150605 Test Loss: 0.8563339\n",
            "EarlyStopping counter: 1 out of 10\n",
            "Updating learning rate to 6.25e-06\n",
            "Epoch: 6 cost time: 1.8999319076538086\n",
            "Epoch: 6, Steps: 26 | Train Loss: 0.0963283 Vali Loss: 0.4177475 Test Loss: 0.7930989\n",
            "EarlyStopping counter: 2 out of 10\n",
            "Updating learning rate to 3.125e-06\n",
            "Epoch: 7 cost time: 2.111553192138672\n",
            "Epoch: 7, Steps: 26 | Train Loss: 0.0811386 Vali Loss: 0.4879236 Test Loss: 0.7931859\n",
            "EarlyStopping counter: 3 out of 10\n",
            "Updating learning rate to 1.5625e-06\n",
            "Epoch: 8 cost time: 2.1939504146575928\n",
            "Epoch: 8, Steps: 26 | Train Loss: 0.0862432 Vali Loss: 0.4745545 Test Loss: 0.7996893\n",
            "EarlyStopping counter: 4 out of 10\n",
            "Updating learning rate to 7.8125e-07\n",
            "Epoch: 9 cost time: 1.9376165866851807\n",
            "Epoch: 9, Steps: 26 | Train Loss: 0.0773068 Vali Loss: 0.4691131 Test Loss: 0.8082219\n",
            "EarlyStopping counter: 5 out of 10\n",
            "Updating learning rate to 3.90625e-07\n",
            "Epoch: 10 cost time: 1.9678359031677246\n",
            "Epoch: 10, Steps: 26 | Train Loss: 0.0814509 Vali Loss: 0.4870001 Test Loss: 0.8121576\n",
            "EarlyStopping counter: 6 out of 10\n",
            "Updating learning rate to 1.953125e-07\n",
            "Epoch: 11 cost time: 2.1545252799987793\n",
            "Epoch: 11, Steps: 26 | Train Loss: 0.0785579 Vali Loss: 0.4890407 Test Loss: 0.8119536\n",
            "EarlyStopping counter: 7 out of 10\n",
            "Updating learning rate to 9.765625e-08\n",
            "Epoch: 12 cost time: 2.1562743186950684\n",
            "Epoch: 12, Steps: 26 | Train Loss: 0.0749619 Vali Loss: 0.1764266 Test Loss: 0.8125013\n",
            "Validation loss decreased (0.382453 --> 0.176427).  Saving model ...\n",
            "Updating learning rate to 4.8828125e-08\n",
            "Epoch: 13 cost time: 1.9843637943267822\n",
            "Epoch: 13, Steps: 26 | Train Loss: 0.0783900 Vali Loss: 0.3525934 Test Loss: 0.8125626\n",
            "EarlyStopping counter: 1 out of 10\n",
            "Updating learning rate to 2.44140625e-08\n",
            "Epoch: 14 cost time: 1.9369585514068604\n",
            "Epoch: 14, Steps: 26 | Train Loss: 0.0808803 Vali Loss: 0.4930156 Test Loss: 0.8124816\n",
            "EarlyStopping counter: 2 out of 10\n",
            "Updating learning rate to 1.220703125e-08\n",
            "Epoch: 15 cost time: 2.287447690963745\n",
            "Epoch: 15, Steps: 26 | Train Loss: 0.0797422 Vali Loss: 0.4861607 Test Loss: 0.8123031\n",
            "EarlyStopping counter: 3 out of 10\n",
            "Updating learning rate to 6.103515625e-09\n",
            "Epoch: 16 cost time: 2.0519161224365234\n",
            "Epoch: 16, Steps: 26 | Train Loss: 0.0838846 Vali Loss: 0.4888451 Test Loss: 0.8122966\n",
            "EarlyStopping counter: 4 out of 10\n",
            "Updating learning rate to 3.0517578125e-09\n",
            "Epoch: 17 cost time: 2.1534249782562256\n",
            "Epoch: 17, Steps: 26 | Train Loss: 0.0803588 Vali Loss: 0.5002829 Test Loss: 0.8123065\n",
            "EarlyStopping counter: 5 out of 10\n",
            "Updating learning rate to 1.52587890625e-09\n",
            "Epoch: 18 cost time: 2.1821322441101074\n",
            "Epoch: 18, Steps: 26 | Train Loss: 0.0774607 Vali Loss: 0.3790297 Test Loss: 0.8123153\n",
            "EarlyStopping counter: 6 out of 10\n",
            "Updating learning rate to 7.62939453125e-10\n",
            "Epoch: 19 cost time: 2.177197217941284\n",
            "Epoch: 19, Steps: 26 | Train Loss: 0.0817477 Vali Loss: 0.5028040 Test Loss: 0.8123155\n",
            "EarlyStopping counter: 7 out of 10\n",
            "Updating learning rate to 3.814697265625e-10\n",
            "Epoch: 20 cost time: 1.9405958652496338\n",
            "Epoch: 20, Steps: 26 | Train Loss: 0.0830546 Vali Loss: 0.3781875 Test Loss: 0.8123162\n",
            "EarlyStopping counter: 8 out of 10\n",
            "Updating learning rate to 1.9073486328125e-10\n",
            "Epoch: 21 cost time: 1.955855369567871\n",
            "Epoch: 21, Steps: 26 | Train Loss: 0.0867827 Vali Loss: 0.4912736 Test Loss: 0.8123163\n",
            "EarlyStopping counter: 9 out of 10\n",
            "Updating learning rate to 9.5367431640625e-11\n",
            "Epoch: 22 cost time: 2.182100772857666\n",
            "Epoch: 22, Steps: 26 | Train Loss: 0.0841725 Vali Loss: 0.4283820 Test Loss: 0.8123163\n",
            "EarlyStopping counter: 10 out of 10\n",
            "Early stopping\n",
            ">>>>>>>testing : input_血小板O__iTransformer_custom_MS_ft12_sl12_ll1_pl2048_dm8_nh2_el1_dl8192_df1_fctimeF_ebFalse_dttest_projection_0<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<\n",
            "test 26\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "test shape: (26, 1, 1, 1) (26, 1, 1, 1)\n",
            "test shape: (26, 1, 1) (26, 1, 1)\n",
            "mse:0.8125012516975403, mae:0.6059585809707642\n",
            "preds [1.2794815 1.6518283 2.0027118 1.599173  1.3557689 2.3814394 2.7339525\n",
            " 3.3250496 3.1868684 2.976748  3.5498838 3.073165  2.797039  3.2891073\n",
            " 3.2213638 3.2280269 2.849268  3.0449693 3.7055435 3.4841278 4.5494633\n",
            " 4.467179  5.234855  4.5338316 4.399783  3.0005903] true [1.6069576 1.814117  1.7981815 1.4077659 2.8977199 2.4196599 3.5032628\n",
            " 2.9773965 3.0570734 3.2482972 3.088944  3.4793596 3.1606529 3.4235861\n",
            " 3.3359416 1.8778583 3.37578   3.6068423 3.37578   4.459383  4.5390596\n",
            " 4.6187363 4.3797064 5.232247  1.3519921 1.2165418]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# !pip3 install ipdb\n",
        "from os import path\n",
        "input_data = dataLoader.input_data\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "def exec_run (args):\n",
        "    print(args)\n",
        "    if args.exp_name == 'partial_train': # See Figure 8 of our paper, for the detail\n",
        "        Exp = Exp_Long_Term_Forecast_Partial\n",
        "    else: # MTSF: multivariate time series forecasting\n",
        "        Exp = Exp_Long_Term_Forecast\n",
        "\n",
        "\n",
        "    if args.is_training:\n",
        "        for ii in range(args.itr):\n",
        "            # setting record of experiments\n",
        "            setting = '{}_{}_{}_{}_ft{}_sl{}_ll{}_pl{}_dm{}_nh{}_el{}_dl{}_df{}_fc{}_eb{}_dt{}_{}_{}'.format(\n",
        "                args.model_id,\n",
        "                args.model,\n",
        "                args.data,\n",
        "                args.features,\n",
        "                args.seq_len,\n",
        "                args.label_len,\n",
        "                args.pred_len,\n",
        "                args.d_model,\n",
        "                args.n_heads,\n",
        "                args.e_layers,\n",
        "                args.d_layers,\n",
        "                args.d_ff,\n",
        "                args.factor,\n",
        "                args.embed,\n",
        "                args.distil,\n",
        "                args.des,\n",
        "                args.class_strategy, ii)\n",
        "\n",
        "            exp = Exp(args)  # set experiments\n",
        "            print('>>>>>>>start training : {}>>>>>>>>>>>>>>>>>>>>>>>>>>'.format(setting))\n",
        "            exp.train(setting)\n",
        "\n",
        "            print('>>>>>>>testing : {}<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<'.format(setting))\n",
        "            exp.test(setting)\n",
        "\n",
        "            if args.do_predict:\n",
        "                print('>>>>>>>predicting : {}<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<'.format(setting))\n",
        "                exp.predict(setting, True)\n",
        "\n",
        "            torch.cuda.empty_cache()\n",
        "            preds = np.load(f'./results/{setting}/pred.npy')\n",
        "            true = np.load(f'./results/{setting}/true.npy')\n",
        "            print('preds',preds.flatten(),'true',true.flatten())\n",
        "            plt.figure()\n",
        "            plt.plot(true.flatten(), label='GroundTruth', linewidth=2)\n",
        "            plt.plot(preds.flatten(), label='Prediction', linewidth=2)\n",
        "            plt.legend()\n",
        "            plt.show()\n",
        "    else:\n",
        "        ii = 0\n",
        "        setting = '{}_{}_{}_{}_ft{}_sl{}_ll{}_pl{}_dm{}_nh{}_el{}_dl{}_df{}_fc{}_eb{}_dt{}_{}_{}'.format(\n",
        "            args.model_id,\n",
        "            args.model,\n",
        "            args.data,\n",
        "            args.features,\n",
        "            args.seq_len,\n",
        "            args.label_len,\n",
        "            args.pred_len,\n",
        "            args.d_model,\n",
        "            args.n_heads,\n",
        "            args.e_layers,\n",
        "            args.d_layers,\n",
        "            args.d_ff,\n",
        "            args.factor,\n",
        "            args.embed,\n",
        "            args.distil,\n",
        "            args.des,\n",
        "            args.class_strategy, ii)\n",
        "\n",
        "        exp = Exp(args)  # set experiments\n",
        "        print('>>>>>>>testing : {}<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<'.format(setting))\n",
        "        exp.test(setting, test=1)\n",
        "        torch.cuda.empty_cache()\n",
        "\n",
        "\n",
        "for column in [input_data.columns[11]]:\n",
        "    input_args = build_args(model_id=f'input_{column}_',target=column)\n",
        "    print(f'trainning:%s',column)\n",
        "    exec_run(input_args)\n",
        "\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "include_colab_link": true
    },
    "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": "3.11.5"
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}