meisai.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "meisai",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyP3gggrTRW9ne1R/dzREvOU",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/BlackBird-BB/BlackBird-BB.github.io/blob/master/meisai.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import sys\n",
        "import time\n",
        "\n",
        "import PIL.Image\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import torch\n",
        "from torch.utils.data import DataLoader\n",
        "from torch.utils.tensorboard import SummaryWriter\n",
        "\n",
        "from moudle import RNN, TrainSet\n",
        "\n",
        "parameters = {\n",
        "    \"gold\": {\n",
        "        \"file\": \"./data/LBMA-GOLD_repaired.csv\",\n",
        "        \"train\": {\n",
        "            \"logdir\": \"./logs/gold/train\",\n",
        "            \"datadir\": \"./data/gold/train\"\n",
        "        },\n",
        "        \"pred\": {\n",
        "            \"logdir\": \"./logs/gold/pred\",\n",
        "            \"datadir\": \"./data/gold/pred\"\n",
        "        }\n",
        "    },\n",
        "    \"bc\": {\n",
        "        \"file\": \"./data/BCHAIN-MKPRU.csv\",\n",
        "        \"train\": {\n",
        "            \"logdir\": \"./logs/bc/train\",\n",
        "            \"datadir\": \"./data/bc/train\"\n",
        "        },\n",
        "        \"pred\": {\n",
        "            \"logdir\": \"./logs/bc/pred\",\n",
        "            \"datadir\": \"./data/bc/pred\"\n",
        "        }\n",
        "    }\n",
        "}\n",
        "table = parameters[\"bc\"]\n",
        "DELAY = 10\n",
        "LR = 0.001\n",
        "EPOCH = 1500\n",
        "BATCH = 10\n",
        "DAYS = 30\n",
        "TRAIN_MODE = True\n",
        "\n",
        "df = pd.read_csv(table['file'])\n",
        "\n",
        "\n",
        "def splite(data, predict=False):\n",
        "    y = data\n",
        "    end = len(y)\n",
        "    y = y[DELAY:]\n",
        "    if not predict:\n",
        "        end = end - 1\n",
        "        y = y[:-1]\n",
        "    x = []\n",
        "    for i in range(DELAY, end):\n",
        "        tmp = data[i - DELAY:i]\n",
        "        tmp = torch.flatten(torch.tensor(tmp)).numpy()\n",
        "        tmp = tmp.tolist()\n",
        "        tmp.append(y[i - DELAY])\n",
        "        x.append(tmp)\n",
        "    return x\n",
        "\n",
        "\n",
        "def cal_rsi(x):\n",
        "    poi = []\n",
        "    neg = []\n",
        "    rsi = [0]\n",
        "    for i in range(1, len(x)):\n",
        "        per = (x[i] - x[i - 1]) / x[i - 1]\n",
        "        if per > 0:\n",
        "            poi.append(per)\n",
        "        else:\n",
        "            neg.append(per)\n",
        "        rsi.append(100 * sum(poi) / (sum(poi) + (-1 * sum(neg))))\n",
        "    return rsi\n",
        "\n",
        "\n",
        "def draw(train_numpy, st, title, writer, rnn):\n",
        "    y = []\n",
        "    df_numpy = df.to_numpy()\n",
        "    orig_data = [i[1] for i in df_numpy[:100]]\n",
        "    data = pd.DataFrame(train_numpy)\n",
        "    data = data[0].to_numpy()\n",
        "    mean = np.mean(train_numpy)\n",
        "    std = np.std(train_numpy)\n",
        "    data = (data - mean) / std\n",
        "    for i in range(DELAY, len(data)):\n",
        "        x = data[i - DELAY:i]\n",
        "        tmp = rnn(torch.tensor(x).cuda().float()).cpu()\n",
        "        tmp = torch.flatten(tmp).detach().numpy()\n",
        "        y.append(tmp * std + mean)\n",
        "\n",
        "    fig = plt.figure()\n",
        "    plt.plot([i+DELAY for i in range(len(y))], y, 'b')\n",
        "    plt.plot([i for i in range(100)], orig_data, 'r')\n",
        "    plt.savefig(f\"{table['train']['datadir']}/tmp.png\")\n",
        "    plt.close()\n",
        "\n",
        "    image = PIL.Image.open(f\"{table['train']['datadir']}/tmp.png\")\n",
        "    image = np.array(image)\n",
        "    writer.add_image(title, image, st, dataformats='HWC')\n",
        "\n",
        "    torch.save(rnn.state_dict(), f\"{table['train']['datadir']}/{title}_{st}.pth\")\n",
        "\n",
        "\n",
        "def regression(datas, title, writer, detail=True):\n",
        "    datas = [i[1] for i in datas]\n",
        "\n",
        "    train_numpy = splite(datas)\n",
        "    mean = np.mean(train_numpy)\n",
        "    std = np.std(train_numpy)\n",
        "    df_numpy = (train_numpy - mean) / std\n",
        "\n",
        "    trainset = TrainSet(torch.tensor(df_numpy))\n",
        "    trainloader = DataLoader(trainset, batch_size=BATCH, shuffle=True)\n",
        "\n",
        "    rnn = RNN(DELAY).cuda()\n",
        "    loss_fnc = torch.nn.MSELoss().cuda()\n",
        "    opti = torch.optim.Adam(rnn.parameters(), lr=LR)\n",
        "    for step in range(EPOCH):\n",
        "        loss = None\n",
        "        for tx, ty in trainloader:\n",
        "            tx = tx.cuda()\n",
        "            ty = ty.cuda()\n",
        "            output = rnn(torch.unsqueeze(tx, dim=0))\n",
        "            loss = loss_fnc(torch.squeeze(output), ty)\n",
        "            opti.zero_grad()\n",
        "            loss.backward()\n",
        "            opti.step()\n",
        "        print(step, loss)\n",
        "        if detail and (step + 1) % 50 == 0:\n",
        "            draw(train_numpy, step, title, writer, rnn)\n",
        "    return rnn\n",
        "\n",
        "\n",
        "def prediction(datas, mdl):\n",
        "    datas = [i[1] for i in datas]\n",
        "    for day in range(DAYS):\n",
        "        x = datas\n",
        "        train_numpy = splite(x, predict=True)\n",
        "        mean = np.mean(train_numpy)\n",
        "        std = np.std(train_numpy)\n",
        "        df_numpy = (train_numpy - mean) / std\n",
        "        x = df_numpy[-1][:-1]\n",
        "        predict_x = torch.tensor(x).cuda().float()\n",
        "        output = mdl(predict_x).cpu() * std + mean\n",
        "        datas.append(output.item())\n",
        "    return datas\n",
        "\n",
        "\n",
        "def prediction_eval(orig_data, prd_data, mdl, writer):\n",
        "    orig_data = [i[1] for i in orig_data]\n",
        "    cnt = (len(orig_data) - 100) / 30\n",
        "    plt.figure()\n",
        "    plt.plot([i for i in range(len(orig_data))], orig_data, \"r\")\n",
        "    plt.plot([i for i in range(len(prd_data))], prd_data, \"b\")\n",
        "    plt.legend()\n",
        "    plt.savefig(f\"{table['pred']['datadir']}/prediction_eval_{cnt}.png\")\n",
        "    plt.close()\n",
        "    time.sleep(0.5)\n",
        "    image = PIL.Image.open(f\"{table['pred']['datadir']}/prediction_eval_{cnt}.png\")\n",
        "    image = np.array(image)\n",
        "    writer.add_image(\"Prediction_eval\", image, cnt, dataformats='HWC')\n",
        "\n",
        "    torch.save(mdl.state_dict(), f\"{table['pred']['datadir']}/mdl_{cnt}.pth\")\n",
        "    with open(f\"{table['pred']['datadir']}/prd_data_{cnt}.txt\", \"w+\") as f:\n",
        "        f.write(f\"The DataLength: {len(orig_data)}\\n\")\n",
        "        f.write(\"And the last 30 Data were pridicted by the model.\\n\")\n",
        "        f.write(f\"Date:\\n\")\n",
        "        for i in prd_data:\n",
        "            f.write(f\"{i} \")\n",
        "    with open(f\"{table['pred']['datadir']}/prd_data_all.txt\", \"a+\") as f:\n",
        "        do_pred = prd_data[-30:]\n",
        "        for i in do_pred:\n",
        "            f.write(f\"{i} \")\n",
        "    # writer.add_graph(mdl, torch.zeros(mdl.delays))\n",
        "\n",
        "\n",
        "def call_regression():\n",
        "    global DELAY, LR, BATCH, EPOCH\n",
        "    print(f\"NOW is running {LR}_{EPOCH}_{BATCH}\")\n",
        "    datas = df.to_numpy()[:100]\n",
        "    writer = SummaryWriter(table['train']['logdir'])\n",
        "\n",
        "    for DELAY in [10, 30]:\n",
        "        for LR in [0.1, 0.01, 0.001, 0.0001]:\n",
        "            for BATCH in [1, 10]:\n",
        "                regression(datas, f\"{DELAY}_{LR}_{BATCH}\", writer)\n",
        "                torch.cuda.empty_cache()\n",
        "\n",
        "    writer.close()\n",
        "\n",
        "\n",
        "def call_predict():\n",
        "    writer = SummaryWriter(table['pred']['logdir'])\n",
        "    df_numpy = df.to_numpy()\n",
        "    for i in range(100, len(df_numpy), 30):\n",
        "        datas = df_numpy[:i]\n",
        "        mdl = regression(datas, \"\", writer, False)\n",
        "        predict_result = prediction(datas, mdl)\n",
        "        prediction_eval(df_numpy[:i + 30], predict_result, mdl, writer)\n",
        "        torch.cuda.empty_cache()\n",
        "\n",
        "    writer.close()\n",
        "\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "    # os.chdir(sys.path[0])\n",
        "    if TRAIN_MODE:\n",
        "        call_regression()\n",
        "    else:\n",
        "        call_predict()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "64xGvC9poSRM",
        "outputId": "d87ae897-6e74-486a-eeef-e3a21942636b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "NOW is running 0.001_1500_10\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([1])) that is different to the input size (torch.Size([])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
            "  return F.mse_loss(input, target, reduction=self.reduction)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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    }
  ]
}