2023/deep_learning/baseline/baseline_sugon.ipynb

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   "cell_type": "code",
   "execution_count": 2,
   "id": "8b8c028a-4787-4d6e-b59a-9097d095eddd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/public/home/wctttty/baseline\n"
     ]
    }
   ],
   "source": [
    "# 先知晓一下当前的目录地址\n",
    "!pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f1ff002a-3b64-43ce-bafe-2471f78d0c93",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Looking in indexes: https://mirrors.aliyun.com/pypi/simple\n",
      "Requirement already satisfied: lalsuite in /public/home/wctttty/.local/lib/python3.7/site-packages (7.5)\n",
      "Requirement already satisfied: ligo-segments in /public/home/wctttty/.local/lib/python3.7/site-packages (from lalsuite) (1.4.0)\n",
      "Requirement already satisfied: lscsoft-glue in /public/home/wctttty/.local/lib/python3.7/site-packages (from lalsuite) (3.0.1)\n",
      "Requirement already satisfied: matplotlib in /opt/conda/lib/python3.7/site-packages (from lalsuite) (3.1.3)\n",
      "Requirement already satisfied: numpy>=1.7 in /opt/conda/lib/python3.7/site-packages (from lalsuite) (1.21.5)\n",
      "Requirement already satisfied: python-dateutil in /opt/conda/lib/python3.7/site-packages (from lalsuite) (2.8.2)\n",
      "Requirement already satisfied: scipy in /opt/conda/lib/python3.7/site-packages (from lalsuite) (1.7.3)\n",
      "Requirement already satisfied: six in /opt/conda/lib/python3.7/site-packages (from ligo-segments->lalsuite) (1.16.0)\n",
      "Requirement already satisfied: pyOpenSSL in /opt/conda/lib/python3.7/site-packages (from lscsoft-glue->lalsuite) (21.0.0)\n",
      "Requirement already satisfied: pyRXP in /public/home/wctttty/.local/lib/python3.7/site-packages (from lscsoft-glue->lalsuite) (3.0.1)\n",
      "Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.7/site-packages (from matplotlib->lalsuite) (0.11.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.7/site-packages (from matplotlib->lalsuite) (1.4.4)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /opt/conda/lib/python3.7/site-packages (from matplotlib->lalsuite) (3.0.9)\n",
      "Requirement already satisfied: typing-extensions in /opt/conda/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib->lalsuite) (4.3.0)\n",
      "Requirement already satisfied: cryptography>=3.3 in /opt/conda/lib/python3.7/site-packages (from pyOpenSSL->lscsoft-glue->lalsuite) (36.0.0)\n",
      "Requirement already satisfied: cffi>=1.12 in /opt/conda/lib/python3.7/site-packages (from cryptography>=3.3->pyOpenSSL->lscsoft-glue->lalsuite) (1.15.0)\n",
      "Requirement already satisfied: pycparser in /opt/conda/lib/python3.7/site-packages (from cffi>=1.12->cryptography>=3.3->pyOpenSSL->lscsoft-glue->lalsuite) (2.21)\n",
      "\u001b[33mDEPRECATION: colossalai 0.0.2-git364b418428e07-dtk2210 has a non-standard version number. pip 24.0 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of colossalai or contact the author to suggest that they release a version with a conforming version number. Discussion can be found at https://github.com/pypa/pip/issues/12063\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mDEPRECATION: faiss 1.7.2-dtk22.10-gitb7348e7df780 has a non-standard version number. pip 24.0 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of faiss or contact the author to suggest that they release a version with a conforming version number. Discussion can be found at https://github.com/pypa/pip/issues/12063\u001b[0m\u001b[33m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "# 安装必要的程序包\n",
    "!pip install --upgrade lalsuite -i https://mirrors.aliyun.com/pypi/simple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "71eb1bef-6640-42a7-8f89-f8b51668de01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'7.1.7'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# https://git.ligo.org/lscsoft/lalsuite/-/issues/300\n",
    "import lal\n",
    "lal.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "04e18008-193f-43c0-8181-6238c29b1abc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.10.0a0+git2040069-dtk2210'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "torch.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68ed9d65-97c3-4b40-850c-4adc4caecc52",
   "metadata": {},
   "source": [
    "# PyTorch深度学习框架\n",
    "\n",
    "PyTorch是一个开源的深度学习框架，由Facebook的人工智能研究团队开发。它提供了两个高级功能：\n",
    "\n",
    "1. 强大的GPU加速的张量计算（如NumPy）库。\n",
    "2. 包含自动微分系统的深度神经网络。\n",
    "\n",
    "PyTorch的设计哲学是“尽可能地保持用户对过程的控制，而不是隐藏复杂的细节”。\n",
    "\n",
    "这使得PyTorch在研究和开发新的深度学习模型时非常灵活，因为用户可以看到并控制模型的内部操作。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f343848-0a4c-41d7-a9a7-3bfcf0c773fa",
   "metadata": {},
   "source": [
    "- 这部分代码主要是引入所需要的库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4c777709-9a33-404d-b849-008aeb295688",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Bad key \"text.kerning_factor\" on line 4 in\n",
      "/opt/conda/lib/python3.7/site-packages/matplotlib/mpl-data/stylelib/_classic_test_patch.mplstyle.\n",
      "You probably need to get an updated matplotlibrc file from\n",
      "https://github.com/matplotlib/matplotlib/blob/v3.1.3/matplotlibrc.template\n",
      "or from the matplotlib source distribution\n"
     ]
    }
   ],
   "source": [
    "# 把脚本里的所有关联的程序包、类、函数都 load 一下\n",
    "from main import *\n",
    "from utils import *"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9ce52bd-54f3-4f63-8e2e-f2eb24900f97",
   "metadata": {},
   "source": [
    "## Train\n",
    "\n",
    "这段代码是一个使用PyTorch进行二分类任务的例子，任务是通过引力波数据（噪声+信号）来进行分类。\n",
    "\n",
    "首先，我们创建了一个数据生成器DatasetGenerator，它用于生成训练和测试数据集。每个epoch的样本数为100，信噪比为20。\n",
    "\n",
    "然后，我们使用PyTorch的DataLoader创建了训练和测试数据加载器，批处理大小为32，数据在每个epoch后都会被打乱。\n",
    "\n",
    "接着，我们加载了预训练的模型，如果没有预训练的模型，就会初始化一个新的模型。模型和损失历史的输出路径为./checkpoints_cnn/。\n",
    "\n",
    "然后，我们设置了优化器的参数，学习率为0.003，总的训练轮数为100，如果有预训练的模型，训练轮数会加上已经训练过的轮数。\n",
    "\n",
    "最后，我们调用train函数开始训练模型。训练过程中，模型会在每个epoch后在测试集上进行评估，并保存测试损失最小的模型。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c308524d-f3cf-487f-a9a9-376b4dccd773",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss 0.0019, train acc 1.000, test acc 0.990\n",
      "423.4 examples/sec on cuda\n"
     ]
    },
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   "source": [
    "# 初始化数据生成 class\n",
    "nsample_perepoch = 100  # 每个epoch的样本数\n",
    "dataset_train = DatasetGenerator(snr=20, nsample_perepoch=nsample_perepoch)  # 训练数据集\n",
    "dataset_test = DatasetGenerator(snr=20, nsample_perepoch=nsample_perepoch)  # 测试数据集\n",
    "\n",
    "# 创建一个DataLoader\n",
    "data_loader = DataLoader(dataset_train, batch_size=32, shuffle=True,)  # 训练数据加载器\n",
    "test_iter = DataLoader(dataset_test, batch_size=32, shuffle=True,)  # 测试数据加载器\n",
    "\n",
    "device = torch.device('cuda')  # 使用CUDA设备\n",
    "\n",
    "# 模型和损失历史的输出路径\n",
    "checkpoint_dir = './checkpoints_cnn/'\n",
    "\n",
    "# 创建模型    \n",
    "net, epoch, train_loss_history = load_model(checkpoint_dir)  # 加载模型\n",
    "net.to(device);  # 将模型转移到设备上\n",
    "\n",
    "# 优化器参数\n",
    "lr = 0.003  # 学习率\n",
    "total_epochs = 100  # 总的训练轮数\n",
    "total_epochs += epoch  # 加上已经训练过的轮数\n",
    "output_freq = 1  # 输出频率\n",
    "\n",
    "# 训练模型\n",
    "train(net, lr, nsample_perepoch, epoch, total_epochs,\n",
    "      dataset_train, data_loader, test_iter,\n",
    "      train_loss_history, checkpoint_dir, device, notebook=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "558ef5cb-3efa-4a2d-ab1a-f80933311612",
   "metadata": {},
   "outputs": [
    {
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   ],
   "source": [
    "dataset_train.strains.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c87efa86-30a5-4af2-921f-1f5b9113937f",
   "metadata": {},
   "source": [
    "## Evaluate\n",
    "\n",
    "这段代码定义了一个函数evaluate_gpu，用于在GPU上计算模型在数据集上的预测结果。\n",
    "\n",
    "首先，我们检查输入的模型net是否是nn.Module的实例。如果是，我们将模型设置为评估模式，并获取模型的设备。如果用户没有指定设备，我们将使用模型的第一个参数所在的设备。\n",
    "\n",
    "然后，我们定义了一个softmax函数，用于将模型的输出转换为概率。\n",
    "\n",
    "接着，我们创建了两个空列表y_hat_list和y_list，用于存储模型的预测结果和真实标签。\n",
    "\n",
    "然后，我们进入一个无需计算梯度的环境中，遍历数据加载器data_iter中的所有批次。对于每个批次，我们将数据和标签转移到设备上，并转换为适当的数据类型。然后，我们将数据通过模型进行前向传播，得到预测结果。\n",
    "\n",
    "接着，我们将预测结果通过softmax函数转换为概率，并将其转移到CPU上，然后转换为NumPy数组，取出正类的概率，并转换为列表。我们也将标签转移到CPU上，然后转换为NumPy数组，并转换为列表。\n",
    "\n",
    "最后，我们将预测结果和标签添加到y_hat_list和y_list中，并在遍历完所有批次后，将这两个列表转换为NumPy数组并返回。\n",
    "\n",
    "这个函数可以用于在训练过程中的每个epoch后，或者在训练完成后，评估模型在验证集或测试集上的性能。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e46faa35-fee7-46d6-b9f9-56ea2b399794",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 加载必要的库\n",
    "import lal  # https://git.ligo.org/lscsoft/lalsuite/-/issues/300\n",
    "from main import *\n",
    "from utils import *\n",
    "from tqdm import tqdm\n",
    "from sklearn.metrics import roc_auc_score, roc_curve\n",
    "\n",
    "def evaluate_gpu(net, data_iter, device=None):\n",
    "    \"\"\"使用GPU计算模型在数据集上的预测结果\"\"\"\n",
    "    if isinstance(net, nn.Module):\n",
    "        net.eval()  # 设置为评估模式\n",
    "        if not device:\n",
    "            device = next(iter(net.parameters())).device  # 获取设备信息\n",
    "    softmax = nn.Softmax(dim=-1)  # 定义softmax函数，dim=-1表示在最后一个维度上进行softmax运算\n",
    "    y_hat_list = []  # 初始化预测结果列表\n",
    "    y_list = []  # 初始化标签列表\n",
    "    with torch.no_grad():  # 不进行梯度计算\n",
    "        for X, y in data_iter:  # 遍历数据集\n",
    "            X = X.to(device).to(torch.float)  # 将数据转移到设备上并转换为浮点型\n",
    "            y = y.to(device).to(torch.long)  # 将标签转移到设备上并转换为长整型\n",
    "            y_hat = net(X)  # 使用模型进行预测\n",
    "\n",
    "            preds = softmax(y_hat).cpu().numpy()[:,1].tolist()  # 使用softmax函数处理预测结果，并转换为列表\n",
    "            labels = y.cpu().numpy().tolist()  # 将标签转换为列表\n",
    "\n",
    "            y_hat_list.extend(preds)  # 将预测结果添加到预测结果列表\n",
    "            y_list.extend(labels)  # 将标签添加到标签列表\n",
    "    return np.asarray(y_hat_list), np.asarray(y_list)  # 返回预测结果和标签的numpy数组```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a857e4b-0f36-4270-9fdd-f3c0f1c599bf",
   "metadata": {},
   "source": [
    "下面这段代码是一个使用PyTorch进行二分类任务的例子，任务是通过引力波数据（噪声+信号）来进行分类。\n",
    "\n",
    "首先，我们创建了一个数据生成器DatasetGenerator，它用于生成测试数据集。每个epoch的样本数为1000，信噪比为20。\n",
    "\n",
    "然后，我们使用PyTorch的DataLoader创建了测试数据加载器，批处理大小为32，数据在每个epoch后都会被打乱。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a068feb0-0e73-47f4-964f-804af9076d46",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GPU available? True\n"
     ]
    }
   ],
   "source": [
    "nsample_perepoch = 1000 # num of injections\n",
    "snr = 20  # 信噪比\n",
    "\n",
    "dataset_test = DatasetGenerator(snr=snr, nsample_perepoch=nsample_perepoch)\n",
    "data_iter = DataLoader(dataset_test, batch_size=32, shuffle=True,)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8844a63b-60c8-40f5-91b7-c54a7e177a7b",
   "metadata": {},
   "source": [
    "接着，我们加载了预训练的模型。如果没有预训练的模型，就会初始化一个新的模型。模型和损失历史的输出路径为 `./checkpoints_cnn/`。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fb840ebe-a6c1-40eb-bceb-f59eda3ea729",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Load network from checkpoints_cnn/model_e74.pt\n"
     ]
    }
   ],
   "source": [
    "# load model\n",
    "device = torch.device('cuda')\n",
    "\n",
    "# Where we output our model and loss history\n",
    "checkpoint_dir = './checkpoints_cnn/'\n",
    "\n",
    "# Creat model\n",
    "net, epoch, train_loss_history = load_model(checkpoint_dir)\n",
    "net.to(device);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23ffba9f-aa35-4dc8-a836-b91bd31537e1",
   "metadata": {},
   "source": [
    "然后，我们调用evaluate_gpu函数在测试集上进行预测，得到预测结果和真实标签。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "aca689b1-847c-449a-893a-4ddff5bb3c0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_hat_list, y_list = evaluate_gpu(net, data_iter, device)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7496777-4f2c-49c6-94f3-b61538f35b46",
   "metadata": {},
   "source": [
    "接下来，我们使用roc_curve和roc_auc_score函数计算了ROC曲线和AUC值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8493c686-b789-41af-9294-0c8718a2844e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.999904"
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     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fpr, tpr, thresholds = roc_curve(y_list, y_hat_list)\n",
    "auc = roc_auc_score(y_list, y_hat_list)\n",
    "auc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b91dd179-7c9f-44d7-9112-a068d5636168",
   "metadata": {},
   "source": [
    "然后，我们绘制了ROC曲线，并标出了阈值为0.5的点。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c4339286-ce03-4429-becb-6d2117d86632",
   "metadata": {},
   "outputs": [],
   "source": [
    "index = np.argmax(thresholds < 0.5)"
   ]
  },
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   "execution_count": 18,
   "id": "b519ea2c-0e6a-4c57-924a-70b22eb9d57a",
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   "outputs": [
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     "metadata": {},
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   ],
   "source": [
    "plt.plot(fpr, tpr, color=\"deeppink\")\n",
    "plt.plot(*(np.linspace(0,1,100),)*2, 'k--', label='Luck (AUC=0.50)')\n",
    "plt.scatter(fpr[index], tpr[index], label=f'threshold={thresholds[index]:.2f}')\n",
    "plt.axis(\"square\")\n",
    "# plt.xscale('log')\n",
    "plt.xlabel(\"False Positive Rate\")\n",
    "plt.ylabel(\"True Positive Rate\")\n",
    "plt.title(\"ROC curves\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
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   "id": "f7d3f301-41f8-4937-aeb6-1bc3db795298",
   "metadata": {},
   "source": [
    "最后，我们对不同的信噪比进行了测试，绘制了不同信噪比下的ROC曲线，并计算了AUC值。"
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     "metadata": {},
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   ],
   "source": [
    "from itertools import cycle\n",
    "colors = cycle([\"deeppink\", \"aqua\", \"darkorange\", \"cornflowerblue\"])\n",
    "\n",
    "nsample_perepoch = 1000\n",
    "snr_list = [5, 10, 15, 20]\n",
    "for snr in tqdm(snr_list):\n",
    "    dataset_test = DatasetGenerator(snr=snr, nsample_perepoch=nsample_perepoch, verbose=False)\n",
    "    data_iter = DataLoader(dataset_test, batch_size=32, shuffle=True)\n",
    "    y_hat_list, y_list = evaluate_gpu(net, data_iter, device)\n",
    "\n",
    "    fpr, tpr, thresholds = roc_curve(y_list, y_hat_list)\n",
    "    auc = roc_auc_score(y_list, y_hat_list)\n",
    "    \n",
    "    plt.plot(fpr, tpr, color=next(colors), label=f'SNR={snr} (AUC={auc:.2f})')\n",
    "plt.plot(*(np.linspace(0,1,100),)*2, 'k--', label='Luck (AUC=0.50)')\n",
    "# plt.axis(\"square\")\n",
    "plt.xscale('log')\n",
    "plt.xlabel(\"False Positive Rate\")\n",
    "plt.ylabel(\"True Positive Rate\")\n",
    "plt.title(\"ROC curves\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89e9d4e0-c900-4c93-9e4e-c510e5f4acb0",
   "metadata": {},
   "source": [
    "在最后一部分，我们计算了ROC曲线的误差，并绘制了带有误差的ROC曲线。\n",
    "\n",
    "这段代码展示了如何使用PyTorch进行二分类任务，并使用ROC曲线和AUC值进行模型评估。\n",
    "\n",
    "同时，也展示了如何对不同的信噪比进行测试，以及如何计算和绘制带有误差的ROC曲线。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "235972e5-dab8-4fc6-872c-9ec7fd878d8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3/3 [00:46<00:00, 15.42s/it]\n"
     ]
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from itertools import cycle\n",
    "colors = cycle([\"deeppink\", \"aqua\", \"darkorange\", \"cornflowerblue\"])\n",
    "\n",
    "def sigma(n, tp):\n",
    "    \"\"\"\n",
    "\n",
    "    :param tp:\n",
    "    :param n:\n",
    "    :return:\n",
    "    \"\"\"\n",
    "    return np.sqrt(tp*(1.-tp)/n)\n",
    "\n",
    "nsample_perepoch = 1000\n",
    "snr_list = [2, 4, 6]\n",
    "for snr in tqdm(snr_list):\n",
    "    dataset_test = DatasetGenerator(snr=snr, nsample_perepoch=nsample_perepoch, verbose=False)\n",
    "    data_iter = DataLoader(dataset_test, batch_size=32, shuffle=True)\n",
    "    y_hat_list, y_list = evaluate_gpu(net, data_iter, device)\n",
    "\n",
    "    fpr, tpr, _ = roc_curve(y_list, y_hat_list)\n",
    "    auc = roc_auc_score(y_list, y_hat_list)\n",
    "    \n",
    "    fpr_error = [sigma(len(y_list)/2., t) for t in fpr]\n",
    "    tpr_error = [sigma(len(y_list)/2., t) for t in tpr]\n",
    "    \n",
    "    color = next(colors)\n",
    "    plt.plot(fpr, tpr, color=color, label=f'SNR={snr} (AUC={auc:.2f})')\n",
    "    plt.fill_between(fpr, tpr+tpr_error, tpr-tpr_error, alpha=0.2, facecolor=color, zorder=0)\n",
    "plt.plot(*(np.linspace(0,1,100),)*2, 'k--', label='Luck (AUC=0.50)')\n",
    "# plt.axis(\"square\")\n",
    "plt.xscale('log')\n",
    "plt.yscale('log')\n",
    "plt.xlabel(\"False Positive Rate\")\n",
    "plt.ylabel(\"True Positive Rate\")\n",
    "plt.title(\"ROC curves\")\n",
    "plt.xlim(1e-4,1)\n",
    "plt.ylim(1e-4,1)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe246d54-c15f-4fc3-8875-22063a8e0f74",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd2f63f5-1d71-4d6c-a515-1deed65c8f24",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# !bash gen_test_data.sh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "284be8e2-ca10-4a84-8bd4-f6f50ba01e69",
   "metadata": {},
   "outputs": [],
   "source": [
    "import lal\n",
    "import _pickle as pickle\n",
    "from tqdm import tqdm\n",
    "from main import *\n",
    "from utils import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "012392b5-039a-488c-b791-1e116663631b",
   "metadata": {},
   "outputs": [],
   "source": [
    "sizeofeach = 100\n",
    "groud_truth = []\n",
    "test_dataset = []\n",
    "\n",
    "for snr in tqdm([2,4,5,6,7,8,9,10,15,20,25,30]):\n",
    "    test_data_addr = f'BBH_testing_1s_8192Hz_10Ksamp_25n_iSNR{snr}_Hdet_astromass_1seed_ts_0.sav'\n",
    "    with open(test_data_addr, 'rb') as rfp:\n",
    "        data = pickle.load(rfp)\n",
    "        index = np.random.randint(len(data[1]), size=sizeofeach)\n",
    "\n",
    "        groud_truth.extend(data[1][index].tolist())\n",
    "        test_dataset.append(np.expand_dims(data[0][index], 1))\n",
    "groud_truth = np.asarray(groud_truth)\n",
    "test_dataset = np.concatenate(test_dataset)\n",
    "\n",
    "groud_truth.shape, test_dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1cf9913c-fb44-46fd-a611-081a6502782e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "pd.Series(groud_truth).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7948c7e5-1ff7-447b-8a3a-c1a2f4d00567",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "pd.DataFrame({'id': range(len(groud_truth)),\n",
    "              'Public': groud_truth,\n",
    "              'Usage': ('Public',)*len(groud_truth)}).to_csv('ground_truth.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5b0d08f-df50-4ba8-822a-b2e2f5ab5224",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('test', test_dataset)\n",
    "# np.save('groud_truth', groud_truth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a1bef6e-0b8d-4f30-983d-06f232418578",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "08c7285b-77c5-45bc-90d8-51a80e74581e",
   "metadata": {},
   "source": [
    "# Submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "19a10214-38a4-4e1d-81e9-4937740f1874",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Bad key \"text.kerning_factor\" on line 4 in\n",
      "/opt/conda/lib/python3.7/site-packages/matplotlib/mpl-data/stylelib/_classic_test_patch.mplstyle.\n",
      "You probably need to get an updated matplotlibrc file from\n",
      "https://github.com/matplotlib/matplotlib/blob/v3.1.3/matplotlibrc.template\n",
      "or from the matplotlib source distribution\n"
     ]
    }
   ],
   "source": [
    "import lal\n",
    "import _pickle as pickle\n",
    "from tqdm import tqdm\n",
    "from main import *\n",
    "from utils import *\n",
    "\n",
    "def evaluate_submissoin_gpu(net, data_iter, device=None):\n",
    "    \"\"\"使用GPU计算模型在数据集上的预测结果\"\"\"\n",
    "    if isinstance(net, nn.Module):\n",
    "        net.eval()  # 设置为评估模式\n",
    "        if not device:\n",
    "            device = next(iter(net.parameters())).device  # 获取设备信息\n",
    "    softmax = nn.Softmax(dim=-1)  # 定义softmax函数，dim=-1表示在最后一个维度上进行softmax运算\n",
    "    y_hat_list = []  # 初始化预测结果列表\n",
    "    with torch.no_grad():  # 不进行梯度计算\n",
    "        for X in data_iter:  # 遍历数据集\n",
    "            X = X.to(device).to(torch.float)  # 将数据转移到设备上并转换为浮点型\n",
    "            y_hat = net(X)  # 使用模型进行预测\n",
    "            preds = softmax(y_hat).cpu().numpy()[:,1].tolist()  # 使用softmax函数处理预测结果，并转换为列表\n",
    "            y_hat_list.extend(preds)  # 将预测结果添加到预测结果列表\n",
    "    return y_hat_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "899c0159-ae0d-4a53-b52e-547bb5544511",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1200, 1, 2, 16384)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_dataset = np.load('test.npy')\n",
    "test_dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6a9ac016-c092-4be4-a2f3-9b455d6ef1a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_loader = DataLoader(test_dataset, batch_size=32, shuffle=False,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cf02b7e0-48fa-4594-a17b-2894ebc30d5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Load network from checkpoints_cnn/model_e74.pt\n"
     ]
    }
   ],
   "source": [
    "# load model\n",
    "device = torch.device('cuda')\n",
    "\n",
    "# Where we output our model and loss history\n",
    "checkpoint_dir = './checkpoints_cnn/'\n",
    "\n",
    "# Creat model\n",
    "net, epoch, train_loss_history = load_model(checkpoint_dir)\n",
    "net.to(device);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d1652dc3-c86d-4b57-87ae-0368ff3615f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1200"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = evaluate_submissoin_gpu(net, data_loader, device)\n",
    "len(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "afe46b64-cfe0-4b66-b130-471bd5378c53",
   "metadata": {},
   "outputs": [],
   "source": [
    "# results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6b436338-3caf-49d5-8753-2e77250fe3b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "pd.DataFrame({'id': range(len(results)),\n",
    "              'target': results}, index=None).to_csv('submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa211504-9bc8-4c3c-b96b-046882e37053",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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