109550027_Final_inference.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas in c:\\users\\chuch\\anaconda3\\lib\\site-packages (1.3.5)\n",
      "Requirement already satisfied: numpy>=1.17.3 in c:\\users\\chuch\\anaconda3\\lib\\site-packages (from pandas) (1.21.6)\n",
      "Requirement already satisfied: pytz>=2017.3 in c:\\users\\chuch\\anaconda3\\lib\\site-packages (from pandas) (2020.1)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in c:\\users\\chuch\\appdata\\roaming\\python\\python37\\site-packages (from pandas) (2.8.2)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\chuch\\appdata\\roaming\\python\\python37\\site-packages (from python-dateutil>=2.7.3->pandas) (1.16.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "[notice] A new release of pip available: 22.3 -> 22.3.1\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: numpy in c:\\users\\chuch\\anaconda3\\lib\\site-packages (1.21.6)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "[notice] A new release of pip available: 22.3 -> 22.3.1\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: scikit-learn in c:\\users\\chuch\\anaconda3\\lib\\site-packages (1.0.2)\n",
      "Requirement already satisfied: scipy>=1.1.0 in c:\\users\\chuch\\anaconda3\\lib\\site-packages (from scikit-learn) (1.7.3)\n",
      "Requirement already satisfied: joblib>=0.11 in c:\\users\\chuch\\anaconda3\\lib\\site-packages (from scikit-learn) (0.17.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\chuch\\anaconda3\\lib\\site-packages (from scikit-learn) (2.1.0)\n",
      "Requirement already satisfied: numpy>=1.14.6 in c:\\users\\chuch\\anaconda3\\lib\\site-packages (from scikit-learn) (1.21.6)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "[notice] A new release of pip available: 22.3 -> 22.3.1\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    }
   ],
   "source": [
    "%pip install pandas\n",
    "%pip install numpy\n",
    "%pip install -U scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import OrdinalEncoder, MinMaxScaler\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = pd.read_csv('train.csv')\n",
    "test_data = pd.read_csv('test.csv')\n",
    "PATH = \"model3.pt\" # load model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data preprocessing\n",
    "ids = test_data['id'].values\n",
    "X_pre = train_data.drop(['failure', 'id'],\n",
    "                        axis=1).to_numpy()\n",
    "test_pre = test_data.drop('id', axis=1).to_numpy()\n",
    "Y = train_data['failure'].values\n",
    "\n",
    "\n",
    "def preprossing(X_pre, test_pre):\n",
    "    enc = OrdinalEncoder()\n",
    "    enc.fit(X_pre)\n",
    "    X_pre = enc.transform(X_pre)\n",
    "    test_pre = enc.fit_transform(test_pre)\n",
    "\n",
    "    imp_mean = SimpleImputer(missing_values=np.nan,\n",
    "                             strategy='median')\n",
    "    imp_mean.fit(X_pre)\n",
    "    X_pre = imp_mean.transform(X_pre)\n",
    "    test_pre = imp_mean.transform(test_pre)\n",
    "\n",
    "    pca = PCA(n_components=2)\n",
    "    X_pre = pca.fit_transform(X_pre)\n",
    "    test_pre = pca.transform(test_pre)\n",
    "    scaler = MinMaxScaler()\n",
    "    X_pre = scaler.fit_transform(X_pre)\n",
    "    test_pre = scaler.transform(test_pre)\n",
    "\n",
    "    return X_pre, test_pre\n",
    "\n",
    "\n",
    "# split training and validation data\n",
    "X, test = preprossing(X_pre, test_pre)\n",
    "train_X, val_X, train_y, val_y = train_test_split(X,\n",
    "                                                  Y,\n",
    "                                                  test_size=0.1,\n",
    "                                                  shuffle=True)\n",
    "\n",
    "val_y = val_y.reshape(val_y.shape[0], 1)\n",
    "train_y = train_y.reshape(train_y.shape[0], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import TensorDataset, DataLoader\n",
    "import torch.nn as nn\n",
    "import torch\n",
    "from torch import optim\n",
    "import torch.nn.functional as F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda\n"
     ]
    }
   ],
   "source": [
    "BATCH_SIZE = 100\n",
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<torch.utils.data.dataloader.DataLoader object at 0x000001E307B13788>\n"
     ]
    }
   ],
   "source": [
    "test_ds = TensorDataset(torch.FloatTensor(test))\n",
    "test_dl = DataLoader(test_ds,\n",
    "                     shuffle=False,\n",
    "                     batch_size=BATCH_SIZE)\n",
    "print(test_dl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "class Model(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.conv1 = nn.Sequential(\n",
    "            nn.Conv1d(1, 32, kernel_size=3),\n",
    "            nn.BatchNorm1d(32),\n",
    "            nn.Conv1d(32, 32, kernel_size=3),\n",
    "            nn.MaxPool1d(2),\n",
    "            nn.BatchNorm1d(32),\n",
    "            nn.ReLU()\n",
    "        )\n",
    "        self.conv2 = nn.Sequential(\n",
    "            nn.Conv1d(32, 48, kernel_size=3),\n",
    "            nn.BatchNorm1d(48),\n",
    "            nn.Conv1d(48, 64, kernel_size=5),\n",
    "            nn.MaxPool1d(2),\n",
    "            nn.BatchNorm1d(64),\n",
    "            nn.ReLU()\n",
    "        )\n",
    "\n",
    "        self.layer_1 = nn.Linear(128, 64)\n",
    "        self.layer_2 = nn.Linear(64, 16)\n",
    "        self.layer_out = nn.Linear(16, 1)\n",
    "\n",
    "        self.relu = nn.ReLU()\n",
    "        self.dropout = nn.Dropout(p=0.2)\n",
    "        self.sigmoid = nn.Sigmoid()\n",
    "        self.input = nn.Linear(2, 24)\n",
    "\n",
    "    def forward(self, x):\n",
    "        batch, len = x.shape\n",
    "        x = x.view(batch, 1, len)\n",
    "        x = F.relu(self.input(x))\n",
    "        x = self.conv1(x)\n",
    "        x = self.dropout(x)\n",
    "\n",
    "        x = self.conv2(x)\n",
    "        x = self.dropout(x)\n",
    "\n",
    "        x = x.view(batch, -1)\n",
    "        x = F.relu(self.layer_1(x))\n",
    "        x = self.dropout(x)\n",
    "\n",
    "        x = F.relu(self.layer_2(x))\n",
    "        x = self.layer_out(x)\n",
    "        x = self.sigmoid(x)\n",
    "        # print(x.size())\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
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      "        [0.1555]], device='cuda:0', grad_fn=<SigmoidBackward0>)\n"
     ]
    }
   ],
   "source": [
    "# load model\n",
    "\n",
    "model = Model().to(device)\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=5e-4)\n",
    "loss_fn = nn.CrossEntropyLoss()\n",
    "checkpoint = torch.load(PATH)\n",
    "model.load_state_dict(checkpoint['model_state_dict'])\n",
    "optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\n",
    "epoch = checkpoint['epoch']\n",
    "# print(epoch)\n",
    "model.eval()\n",
    "predict = []\n",
    "for attr in test_dl:\n",
    "    # print(type(attr[0]))\n",
    "    att = attr[0].to(device)\n",
    "\n",
    "    pred = model(att)\n",
    "    # pred = torch.gt(pred,0).int()\n",
    "    # pred = torch.mul(pred, 0.5)\n",
    "    # pred = torch.add(pred, 0.5)\n",
    "    print(pred)\n",
    "    for i in pred:\n",
    "        predict.append(i.item())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('submission.csv', 'w', newline='') as f:\n",
    "    csv_writer = csv.writer(f)\n",
    "    csv_writer.writerow([\"id\", \"failure\"])\n",
    "    count = 0\n",
    "    for id, p in zip(ids, predict):\n",
    "        csv_writer.writerow([id, p])"
   ]
  }
 ],
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   "display_name": "base",
   "language": "python",
   "name": "python3"
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   "file_extension": ".py",
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