DeepLearning V6 [MLP-Mixer].ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "48a4b739",
   "metadata": {},
   "source": [
    "# 1. Import Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0d9a99d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.nn.functional as F\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, multilabel_confusion_matrix\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6206691b",
   "metadata": {},
   "source": [
    "# 2. Check the coordinates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "61b22970",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_bench = pd.read_csv('./labeling/benchpress_coords.csv')\n",
    "df_dead = pd.read_csv('./labeling/deadlift_coords.csv')\n",
    "df_squat = pd.read_csv('./labeling/squat_coords.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "098732e0",
   "metadata": {},
   "outputs": [
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  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6ca142bf",
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   "outputs": [
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       "            class        x1        y1        z1        v1        x2        y2  \\\n",
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       "3    d_correct_up  0.704687  0.148774  0.440767  0.995209  0.700908  0.139350   \n",
       "4    d_correct_up  0.716225  0.146869  0.293682  0.998649  0.713821  0.136261   \n",
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       "\n",
       "[5 rows x 133 columns]"
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     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df_dead.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fe058cdc",
   "metadata": {},
   "outputs": [
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       "      <th>v31</th>\n",
       "      <th>x32</th>\n",
       "      <th>y32</th>\n",
       "      <th>z32</th>\n",
       "      <th>v32</th>\n",
       "      <th>x33</th>\n",
       "      <th>y33</th>\n",
       "      <th>z33</th>\n",
       "      <th>v33</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>s_spine_neutral_down</td>\n",
       "      <td>0.578854</td>\n",
       "      <td>0.459936</td>\n",
       "      <td>-0.178820</td>\n",
       "      <td>0.999752</td>\n",
       "      <td>0.594528</td>\n",
       "      <td>0.448642</td>\n",
       "      <td>-0.173299</td>\n",
       "      <td>0.999830</td>\n",
       "      <td>0.594286</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.313403</td>\n",
       "      <td>0.976089</td>\n",
       "      <td>0.302474</td>\n",
       "      <td>0.597096</td>\n",
       "      <td>0.656582</td>\n",
       "      <td>0.806852</td>\n",
       "      <td>0.332788</td>\n",
       "      <td>0.694722</td>\n",
       "      <td>-0.373114</td>\n",
       "      <td>0.985341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>s_spine_neutral_down</td>\n",
       "      <td>0.590740</td>\n",
       "      <td>0.410155</td>\n",
       "      <td>-0.341654</td>\n",
       "      <td>0.999727</td>\n",
       "      <td>0.595270</td>\n",
       "      <td>0.397050</td>\n",
       "      <td>-0.327615</td>\n",
       "      <td>0.999847</td>\n",
       "      <td>0.594844</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.305247</td>\n",
       "      <td>0.975016</td>\n",
       "      <td>0.359256</td>\n",
       "      <td>0.611075</td>\n",
       "      <td>0.285869</td>\n",
       "      <td>0.773935</td>\n",
       "      <td>0.334483</td>\n",
       "      <td>0.695383</td>\n",
       "      <td>-0.373157</td>\n",
       "      <td>0.984979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>s_spine_neutral_up</td>\n",
       "      <td>0.572588</td>\n",
       "      <td>0.146920</td>\n",
       "      <td>-0.468999</td>\n",
       "      <td>0.999881</td>\n",
       "      <td>0.573966</td>\n",
       "      <td>0.125410</td>\n",
       "      <td>-0.442385</td>\n",
       "      <td>0.999909</td>\n",
       "      <td>0.573949</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.462458</td>\n",
       "      <td>0.976983</td>\n",
       "      <td>0.493459</td>\n",
       "      <td>0.641891</td>\n",
       "      <td>0.653005</td>\n",
       "      <td>0.840647</td>\n",
       "      <td>0.369003</td>\n",
       "      <td>0.695396</td>\n",
       "      <td>-0.618573</td>\n",
       "      <td>0.986593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>s_spine_neutral_up</td>\n",
       "      <td>0.563577</td>\n",
       "      <td>0.157547</td>\n",
       "      <td>-0.483738</td>\n",
       "      <td>0.999883</td>\n",
       "      <td>0.564866</td>\n",
       "      <td>0.136710</td>\n",
       "      <td>-0.457015</td>\n",
       "      <td>0.999882</td>\n",
       "      <td>0.564586</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.401422</td>\n",
       "      <td>0.968094</td>\n",
       "      <td>0.488726</td>\n",
       "      <td>0.641720</td>\n",
       "      <td>0.591863</td>\n",
       "      <td>0.822999</td>\n",
       "      <td>0.332679</td>\n",
       "      <td>0.700715</td>\n",
       "      <td>-0.569745</td>\n",
       "      <td>0.981765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>s_spine_neutral_down</td>\n",
       "      <td>0.579107</td>\n",
       "      <td>0.414149</td>\n",
       "      <td>-0.166554</td>\n",
       "      <td>0.999601</td>\n",
       "      <td>0.593653</td>\n",
       "      <td>0.400882</td>\n",
       "      <td>-0.152463</td>\n",
       "      <td>0.999702</td>\n",
       "      <td>0.593684</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.601359</td>\n",
       "      <td>0.977251</td>\n",
       "      <td>0.393079</td>\n",
       "      <td>0.617481</td>\n",
       "      <td>0.678957</td>\n",
       "      <td>0.802303</td>\n",
       "      <td>0.347247</td>\n",
       "      <td>0.690384</td>\n",
       "      <td>-0.761584</td>\n",
       "      <td>0.985078</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 133 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  class        x1        y1        z1        v1        x2  \\\n",
       "0  s_spine_neutral_down  0.578854  0.459936 -0.178820  0.999752  0.594528   \n",
       "1  s_spine_neutral_down  0.590740  0.410155 -0.341654  0.999727  0.595270   \n",
       "2    s_spine_neutral_up  0.572588  0.146920 -0.468999  0.999881  0.573966   \n",
       "3    s_spine_neutral_up  0.563577  0.157547 -0.483738  0.999883  0.564866   \n",
       "4  s_spine_neutral_down  0.579107  0.414149 -0.166554  0.999601  0.593653   \n",
       "\n",
       "         y2        z2        v2        x3  ...       z31       v31       x32  \\\n",
       "0  0.448642 -0.173299  0.999830  0.594286  ... -0.313403  0.976089  0.302474   \n",
       "1  0.397050 -0.327615  0.999847  0.594844  ... -0.305247  0.975016  0.359256   \n",
       "2  0.125410 -0.442385  0.999909  0.573949  ... -0.462458  0.976983  0.493459   \n",
       "3  0.136710 -0.457015  0.999882  0.564586  ... -0.401422  0.968094  0.488726   \n",
       "4  0.400882 -0.152463  0.999702  0.593684  ... -0.601359  0.977251  0.393079   \n",
       "\n",
       "        y32       z32       v32       x33       y33       z33       v33  \n",
       "0  0.597096  0.656582  0.806852  0.332788  0.694722 -0.373114  0.985341  \n",
       "1  0.611075  0.285869  0.773935  0.334483  0.695383 -0.373157  0.984979  \n",
       "2  0.641891  0.653005  0.840647  0.369003  0.695396 -0.618573  0.986593  \n",
       "3  0.641720  0.591863  0.822999  0.332679  0.700715 -0.569745  0.981765  \n",
       "4  0.617481  0.678957  0.802303  0.347247  0.690384 -0.761584  0.985078  \n",
       "\n",
       "[5 rows x 133 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_squat.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c03291a0",
   "metadata": {},
   "source": [
    "# 3. Data Preprocessing & Data Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a4debbf8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 데이터 로드\n",
    "bench_data = np.loadtxt(\"./labeling/benchpress_coords.csv\", delimiter=\",\", dtype=str, skiprows=1)\n",
    "labels = bench_data[:, 0]\n",
    "bench_data = bench_data[:, 1:]  # 첫 번째 열을 제외한 모든 열 선택\n",
    "\n",
    "# One-Hot Encoding\n",
    "encoder = OneHotEncoder(sparse_output=False)\n",
    "labels = encoder.fit_transform(labels.reshape(-1, 1))\n",
    "\n",
    "# 결측값 처리\n",
    "bench_data = np.nan_to_num(bench_data)\n",
    "\n",
    "bench_data = bench_data.astype(float)\n",
    "\n",
    "# 데이터 분할\n",
    "bench_X_train, bench_X_test, bench_y_train, bench_y_test = train_test_split(bench_data, labels, test_size=0.25)\n",
    "\n",
    "# 텐서 변환\n",
    "bench_X_train = torch.tensor(bench_X_train)\n",
    "bench_X_test = torch.tensor(bench_X_test)\n",
    "bench_y_train = torch.tensor(bench_y_train)\n",
    "bench_y_test = torch.tensor(bench_y_test)\n",
    "\n",
    "# float 변환\n",
    "bench_X_train = bench_X_train.float()\n",
    "bench_X_test = bench_X_test.float()\n",
    "bench_y_train = bench_y_train.float()\n",
    "bench_y_test = bench_y_test.float()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e36cffe3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 데이터 로드\n",
    "squat_data = np.loadtxt(\"./labeling/squat_coords.csv\", delimiter=\",\", dtype=str, skiprows=1)\n",
    "labels_squat = squat_data[:, 0]\n",
    "squat_data = squat_data[:, 1:]  # 첫 번째 열을 제외한 모든 열 선택\n",
    "\n",
    "# One-Hot Encoding\n",
    "encoder = OneHotEncoder(sparse_output=False)\n",
    "labels_squat = encoder.fit_transform(labels_squat.reshape(-1, 1))\n",
    "\n",
    "# 결측값 처리\n",
    "squat_data = np.nan_to_num(squat_data)\n",
    "\n",
    "squat_data = squat_data.astype(float)\n",
    "\n",
    "# 데이터 분할\n",
    "squat_X_train, squat_X_test, squat_y_train, squat_y_test = train_test_split(squat_data, labels_squat, test_size=0.25)\n",
    "\n",
    "# 텐서 변환\n",
    "squat_X_train = torch.tensor(squat_X_train)\n",
    "squat_X_test = torch.tensor(squat_X_test)\n",
    "squat_y_train = torch.tensor(squat_y_train)\n",
    "squat_y_test = torch.tensor(squat_y_test)\n",
    "\n",
    "# float 변환\n",
    "squat_X_train = squat_X_train.float()\n",
    "squat_X_test = squat_X_test.float()\n",
    "squat_y_train = squat_y_train.float()\n",
    "squat_y_test = squat_y_test.float()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "daf4f7b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 데이터 로드\n",
    "deadlift_data = np.loadtxt(\"./labeling/deadlift_coords.csv\", delimiter=\",\", dtype=str, skiprows=1)\n",
    "labels_deadlift = deadlift_data[:, 0]\n",
    "deadlift_data = deadlift_data[:, 1:]  # 첫 번째 열을 제외한 모든 열 선택\n",
    "\n",
    "# One-Hot Encoding\n",
    "encoder = OneHotEncoder(sparse_output=False)\n",
    "labels_deadlift = encoder.fit_transform(labels_deadlift.reshape(-1, 1))\n",
    "\n",
    "# 결측값 처리\n",
    "deadlift_data = np.nan_to_num(deadlift_data)\n",
    "\n",
    "deadlift_data = deadlift_data.astype(float)\n",
    "\n",
    "# 데이터 분할\n",
    "deadlift_X_train, deadlift_X_test, deadlift_y_train, deadlift_y_test = train_test_split(deadlift_data, labels_deadlift, test_size=0.25)\n",
    "\n",
    "# 텐서 변환\n",
    "deadlift_X_train = torch.tensor(deadlift_X_train)\n",
    "deadlift_X_test = torch.tensor(deadlift_X_test)\n",
    "deadlift_y_train = torch.tensor(deadlift_y_train)\n",
    "deadlift_y_test = torch.tensor(deadlift_y_test)\n",
    "\n",
    "# float 변환\n",
    "deadlift_X_train = deadlift_X_train.float()\n",
    "deadlift_X_test = deadlift_X_test.float()\n",
    "deadlift_y_train = deadlift_y_train.float()\n",
    "deadlift_y_test = deadlift_y_test.float()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3122dfa3",
   "metadata": {},
   "source": [
    "# 4. Construct Deep Learning Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b5c55c18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([239, 6])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bench_y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "53c59927",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([716, 6])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bench_y_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d56f3fb",
   "metadata": {},
   "source": [
    "### Bench Press"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "85728780",
   "metadata": {},
   "outputs": [],
   "source": [
    "class DeepClassifier(nn.Module):\n",
    "    def __init__(self, input_size, output_size, hidden_dim=128, num_blocks=4):\n",
    "        super(DeepClassifier, self).__init__()\n",
    "        self.num_blocks = num_blocks\n",
    "        self.mlp1 = nn.Linear(input_size, hidden_dim)\n",
    "        self.mlp2 = nn.Linear(hidden_dim, hidden_dim)\n",
    "        self.mlp3 = nn.Linear(hidden_dim, input_size)\n",
    "        self.fc1 = nn.Linear(input_size, 64)\n",
    "        self.fc2 = nn.Linear(64, output_size)\n",
    "        self.dropout = nn.Dropout(0.5)\n",
    "        self.relu = nn.ReLU()\n",
    "        self.softmax = nn.Softmax(dim=1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        for _ in range(self.num_blocks):\n",
    "            x = self.mlp1(x)\n",
    "            x = self.relu(x)\n",
    "            x = self.mlp2(x)\n",
    "            x = self.relu(x)\n",
    "            x = self.mlp3(x)\n",
    "        x = self.dropout(self.relu(self.fc1(x)))\n",
    "        x = self.fc2(x)\n",
    "        x = self.softmax(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "30ab9554",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 모델 생성\n",
    "input_size = 132\n",
    "output_size = 6\n",
    "bench_model = DeepClassifier(input_size, output_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "afdac53a",
   "metadata": {},
   "outputs": [],
   "source": [
    "bench_criterion = nn.CrossEntropyLoss()\n",
    "bench_optimizer = optim.Adam(bench_model.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0a7e3c75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bench_train_losses = []\n",
    "bench_test_losses = []\n",
    "\n",
    "epochs = 1000\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    bench_model.train()\n",
    "    bench_optimizer.zero_grad()\n",
    "    bench_outputs = bench_model(bench_X_train)\n",
    "    bench_loss = bench_criterion(bench_outputs, bench_y_train)\n",
    "    bench_loss.backward()\n",
    "    bench_optimizer.step()\n",
    "    bench_train_losses.append(bench_loss.item())\n",
    "    \n",
    "    bench_model.eval()\n",
    "    with torch.no_grad():\n",
    "        bench_test_outputs = bench_model(bench_X_test)\n",
    "        bench_test_loss = bench_criterion(bench_test_outputs, bench_y_test)\n",
    "        bench_test_losses.append(bench_test_loss.item())\n",
    "\n",
    "plt.plot(range(1, epochs+1), bench_train_losses, label='Train Loss')\n",
    "plt.plot(range(1, epochs+1), bench_test_losses, label='Test Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Training and Test Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "fbca1495",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Loss: 1.3572602272033691\n"
     ]
    }
   ],
   "source": [
    "bench_model.eval()\n",
    "with torch.no_grad():\n",
    "    test_outputs = bench_model(bench_X_test)\n",
    "    test_loss = bench_criterion(test_outputs, bench_y_test)\n",
    "    print(f'Test Loss: {test_loss.item()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8bcb0bfe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "샘플 1:\n",
      "입력 데이터: tensor([ 0.5104,  0.3843,  0.9745,  1.0000,  0.5213,  0.3853,  1.0520,  1.0000,\n",
      "         0.5299,  0.3865,  1.0527,  1.0000,  0.5385,  0.3880,  1.0521,  1.0000,\n",
      "         0.4997,  0.3850,  1.0617,  1.0000,  0.4928,  0.3860,  1.0616,  1.0000,\n",
      "         0.4861,  0.3871,  1.0620,  1.0000,  0.5514,  0.3996,  1.2245,  1.0000,\n",
      "         0.4740,  0.3972,  1.2635,  1.0000,  0.5283,  0.3899,  1.0159,  1.0000,\n",
      "         0.4982,  0.3903,  1.0256,  1.0000,  0.6281,  0.4343,  1.0754,  1.0000,\n",
      "         0.4021,  0.4319,  1.1550,  1.0000,  0.7043,  0.3832,  1.1134,  0.9876,\n",
      "         0.3288,  0.3673,  1.2793,  0.9952,  0.6835,  0.2956,  1.3947,  0.9704,\n",
      "         0.3409,  0.2915,  1.5782,  0.9942,  0.6831,  0.2767,  1.4000,  0.9052,\n",
      "         0.3367,  0.2731,  1.5795,  0.9881,  0.6657,  0.2740,  1.4461,  0.8796,\n",
      "         0.3498,  0.2715,  1.6182,  0.9823,  0.6608,  0.2810,  1.4086,  0.8803,\n",
      "         0.3564,  0.2784,  1.5859,  0.9771,  0.5899,  0.5338, -0.0208,  1.0000,\n",
      "         0.4246,  0.5424,  0.0210,  1.0000,  0.7392,  0.6648, -0.2438,  0.9842,\n",
      "         0.2457,  0.6657, -0.3100,  0.9747,  0.6171,  0.6790,  0.1907,  0.1554,\n",
      "         0.4572,  0.6371,  0.1700,  0.0748,  0.5871,  0.6725,  0.2476,  0.5616,\n",
      "         0.4877,  0.6251,  0.2298,  0.3503,  0.6331,  0.6983,  0.1941,  0.3253,\n",
      "         0.5150,  0.6536,  0.2079,  0.1736])\n",
      "실제 타겟 데이터: tensor([0., 0., 0., 0., 0., 1.])\n",
      "예측된 데이터: tensor(5)\n",
      "예측 확률: ['0.0000', '0.0000', '0.0000', '0.0000', '0.0000', '1.0000']\n",
      "\n",
      "샘플 2:\n",
      "입력 데이터: tensor([ 0.5084,  0.3734,  0.8940,  1.0000,  0.5173,  0.3710,  0.9670,  1.0000,\n",
      "         0.5248,  0.3723,  0.9677,  1.0000,  0.5323,  0.3738,  0.9672,  1.0000,\n",
      "         0.4975,  0.3697,  0.9810,  1.0000,  0.4912,  0.3701,  0.9807,  1.0000,\n",
      "         0.4849,  0.3707,  0.9812,  1.0000,  0.5416,  0.3836,  1.1020,  1.0000,\n",
      "         0.4754,  0.3800,  1.1683,  1.0000,  0.5235,  0.3838,  0.9196,  1.0000,\n",
      "         0.4969,  0.3822,  0.9364,  1.0000,  0.6216,  0.4250,  0.9652,  1.0000,\n",
      "         0.4011,  0.4248,  1.0589,  1.0000,  0.7422,  0.3798,  1.0537,  0.9652,\n",
      "         0.2951,  0.3577,  1.1549,  0.9963,  0.7833,  0.2986,  1.3919,  0.9751,\n",
      "         0.2710,  0.2808,  1.4049,  0.9960,  0.7838,  0.2801,  1.4063,  0.9478,\n",
      "         0.2628,  0.2657,  1.3969,  0.9913,  0.7680,  0.2770,  1.4283,  0.9374,\n",
      "         0.2790,  0.2665,  1.4197,  0.9874,  0.7635,  0.2838,  1.3959,  0.9415,\n",
      "         0.2854,  0.2709,  1.4009,  0.9863,  0.5984,  0.5346, -0.0052,  1.0000,\n",
      "         0.4133,  0.5345,  0.0050,  1.0000,  0.8154,  0.6028, -0.6076,  0.9965,\n",
      "         0.2013,  0.5834, -0.6771,  0.9966,  0.7709,  0.7252, -0.3551,  0.9253,\n",
      "         0.2201,  0.7211, -0.4589,  0.9852,  0.7269,  0.7403, -0.3364,  0.9341,\n",
      "         0.2528,  0.7347, -0.4437,  0.9665,  0.8376,  0.7959, -0.5274,  0.9364,\n",
      "         0.1598,  0.7969, -0.6731,  0.9790])\n",
      "실제 타겟 데이터: tensor([0., 1., 0., 0., 0., 0.])\n",
      "예측된 데이터: tensor(1)\n",
      "예측 확률: ['0.0000', '1.0000', '0.0000', '0.0000', '0.0000', '0.0000']\n",
      "\n",
      "샘플 3:\n",
      "입력 데이터: tensor([ 0.2841,  0.3293,  0.7075,  0.9998,  0.2777,  0.3324,  0.7546,  0.9998,\n",
      "         0.2811,  0.3321,  0.7547,  0.9998,  0.2828,  0.3252,  0.7544,  0.9999,\n",
      "         0.2681,  0.3351,  0.7300,  0.9999,  0.2647,  0.3362,  0.7298,  0.9999,\n",
      "         0.2602,  0.3367,  0.7295,  0.9999,  0.2898,  0.3305,  0.9042,  0.9999,\n",
      "         0.2615,  0.3412,  0.7852,  0.9999,  0.3039,  0.3346,  0.7566,  0.9998,\n",
      "         0.2965,  0.3346,  0.7250,  0.9998,  0.4263,  0.3394,  0.8285,  0.9999,\n",
      "         0.2763,  0.3682,  0.5466,  1.0000,  0.5615,  0.4187,  0.7218,  0.3950,\n",
      "         0.2511,  0.4146,  0.0582,  0.9666,  0.6729,  0.4791,  0.5911,  0.4977,\n",
      "         0.2114,  0.3738, -0.2602,  0.9525,  0.7102,  0.4985,  0.5656,  0.4671,\n",
      "         0.2032,  0.3675, -0.3203,  0.9262,  0.7061,  0.4985,  0.5469,  0.4592,\n",
      "         0.2099,  0.3627, -0.2508,  0.9076,  0.6874,  0.4920,  0.5759,  0.4606,\n",
      "         0.2154,  0.3646, -0.2386,  0.8754,  0.6690,  0.4079,  0.1651,  1.0000,\n",
      "         0.5291,  0.4201, -0.1648,  1.0000,  0.9197,  0.4395, -0.2708,  0.9928,\n",
      "         0.5677,  0.4694, -0.5917,  0.9984,  0.7586,  0.5433,  0.1084,  0.9680,\n",
      "         0.4848,  0.6139, -0.5977,  0.9962,  0.7211,  0.5572,  0.1463,  0.9591,\n",
      "         0.4681,  0.6190, -0.5952,  0.9918,  0.8177,  0.5794,  0.0975,  0.9574,\n",
      "         0.4984,  0.7072, -0.7073,  0.9943])\n",
      "실제 타겟 데이터: tensor([0., 0., 0., 0., 1., 0.])\n",
      "예측된 데이터: tensor(2)\n",
      "예측 확률: ['0.0000', '0.0000', '1.0000', '0.0000', '0.0000', '0.0000']\n",
      "\n",
      "샘플 4:\n",
      "입력 데이터: tensor([ 0.7435,  0.3141,  0.4104,  1.0000,  0.7669,  0.3148,  0.4484,  1.0000,\n",
      "         0.7741,  0.3174,  0.4483,  1.0000,  0.7807,  0.3202,  0.4479,  1.0000,\n",
      "         0.7490,  0.3101,  0.4748,  1.0000,  0.7444,  0.3093,  0.4748,  0.9999,\n",
      "         0.7404,  0.3085,  0.4748,  1.0000,  0.7817,  0.3370,  0.5430,  1.0000,\n",
      "         0.7383,  0.3184,  0.6678,  1.0000,  0.7445,  0.3289,  0.4351,  0.9999,\n",
      "         0.7256,  0.3214,  0.4714,  0.9998,  0.7792,  0.3887,  0.4176,  1.0000,\n",
      "         0.6088,  0.3440,  0.6529,  0.9998,  0.8639,  0.3572,  0.2041,  0.9587,\n",
      "         0.5291,  0.3137,  0.7606,  0.6312,  0.8612,  0.2698,  0.2022,  0.9429,\n",
      "         0.5526,  0.2309,  0.7087,  0.8684,  0.8595,  0.2470,  0.1758,  0.8873,\n",
      "         0.5489,  0.2103,  0.6835,  0.8769,  0.8479,  0.2466,  0.1889,  0.8570,\n",
      "         0.5575,  0.2100,  0.6581,  0.8763,  0.8469,  0.2550,  0.2044,  0.8331,\n",
      "         0.5593,  0.2169,  0.6899,  0.8602,  0.5680,  0.4388, -0.0398,  1.0000,\n",
      "         0.4504,  0.4183,  0.0391,  1.0000,  0.5500,  0.5597, -0.1956,  0.9919,\n",
      "         0.1862,  0.4828, -0.1547,  0.9862,  0.6046,  0.5812,  0.2916,  0.8352,\n",
      "         0.3856,  0.5362,  0.3758,  0.5499,  0.6187,  0.5762,  0.3542,  0.8870,\n",
      "         0.4263,  0.5432,  0.4432,  0.6440,  0.6003,  0.6227,  0.3886,  0.9050,\n",
      "         0.3584,  0.5660,  0.4616,  0.6939])\n",
      "실제 타겟 데이터: tensor([0., 0., 0., 0., 0., 1.])\n",
      "예측된 데이터: tensor(5)\n",
      "예측 확률: ['0.0000', '0.0000', '0.0000', '0.0000', '0.0000', '1.0000']\n",
      "\n",
      "샘플 5:\n",
      "입력 데이터: tensor([ 0.7814,  0.2986,  0.5271,  1.0000,  0.8011,  0.2976,  0.5783,  1.0000,\n",
      "         0.8082,  0.3006,  0.5785,  1.0000,  0.8158,  0.3041,  0.5782,  1.0000,\n",
      "         0.7859,  0.2923,  0.6038,  1.0000,  0.7820,  0.2912,  0.6039,  1.0000,\n",
      "         0.7781,  0.2903,  0.6039,  1.0000,  0.8298,  0.3200,  0.7266,  1.0000,\n",
      "         0.7794,  0.2996,  0.8534,  1.0000,  0.7864,  0.3129,  0.5686,  1.0000,\n",
      "         0.7710,  0.3062,  0.6051,  1.0000,  0.8441,  0.3669,  0.6830,  1.0000,\n",
      "         0.6807,  0.3318,  0.8340,  1.0000,  0.9610,  0.3514,  0.5900,  0.9094,\n",
      "         0.5818,  0.3063,  0.9993,  0.8024,  0.9625,  0.3311,  0.6954,  0.6958,\n",
      "         0.5810,  0.2269,  1.1927,  0.9328,  0.9650,  0.3248,  0.7179,  0.5098,\n",
      "         0.5749,  0.2101,  1.2269,  0.9178,  0.9488,  0.3235,  0.7182,  0.4573,\n",
      "         0.5855,  0.2074,  1.2235,  0.9117,  0.9406,  0.3270,  0.6928,  0.4523,\n",
      "         0.5883,  0.2135,  1.1988,  0.8941,  0.6519,  0.4203, -0.0523,  1.0000,\n",
      "         0.5429,  0.4012,  0.0519,  1.0000,  0.6407,  0.4938, -0.8810,  0.9974,\n",
      "         0.2758,  0.4125, -0.2328,  0.9910,  0.6238,  0.6358, -1.0015,  0.9868,\n",
      "         0.2551,  0.5382, -0.1260,  0.9790,  0.5954,  0.6466, -1.0137,  0.9831,\n",
      "         0.2781,  0.5660, -0.1245,  0.9605,  0.5863,  0.6910, -1.2150,  0.9964,\n",
      "         0.1352,  0.5738, -0.3014,  0.9805])\n",
      "실제 타겟 데이터: tensor([0., 1., 0., 0., 0., 0.])\n",
      "예측된 데이터: tensor(1)\n",
      "예측 확률: ['0.0000', '1.0000', '0.0000', '0.0000', '0.0000', '0.0000']\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/23/n8c37q85651bywf1rz00z90c0000gn/T/ipykernel_40713/658835650.py:9: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  input_tensor = torch.tensor(input_sample, dtype=torch.float32)\n"
     ]
    }
   ],
   "source": [
    "# 테스트 데이터셋에서 샘플 선택\n",
    "num_samples_to_test = 5  # 확인할 샘플의 수\n",
    "for i in range(num_samples_to_test):\n",
    "    sample_index = i\n",
    "    input_sample = bench_X_test[sample_index]  # 입력 데이터\n",
    "    target_sample = bench_y_test[sample_index]  # 실제 타겟(정답) 데이터\n",
    "\n",
    "    # 입력 데이터를 Tensor로 변환\n",
    "    input_tensor = torch.tensor(input_sample, dtype=torch.float32)\n",
    "\n",
    "    # 모델에 입력하여 예측 생성\n",
    "    bench_model.eval()\n",
    "    with torch.no_grad():\n",
    "        prediction = bench_model(input_tensor.unsqueeze(0))  # unsqueeze를 사용하여 배치 차원 추가\n",
    "\n",
    "    # 예측 결과 출력\n",
    "    print(f\"샘플 {sample_index + 1}:\")\n",
    "    print(\"입력 데이터:\", input_sample)\n",
    "    print(\"실제 타겟 데이터:\", target_sample)\n",
    "    print(\"예측된 데이터:\", torch.argmax(prediction))  # 확률이 아닌 예측된 클래스를 출력\n",
    "    probabilities = prediction.squeeze().tolist()  # 텐서를 리스트로 변환\n",
    "    probabilities_str = [f\"{prob:.4f}\" for prob in probabilities]\n",
    "    print(\"예측 확률:\", probabilities_str)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9cacd475",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "정확도: 0.686\n",
      "정밀도: 0.689\n",
      "재현율: 0.686\n",
      "F1 점수: 0.688\n"
     ]
    }
   ],
   "source": [
    "bench_model.eval()\n",
    "with torch.no_grad():\n",
    "    predicted_probs = bench_model(bench_X_test)\n",
    "\n",
    "threshold = 0.8  # 임계값 설정\n",
    "predicted_labels = (predicted_probs > threshold).numpy().astype(int)\n",
    "\n",
    "# 정확도 계산\n",
    "accuracy = accuracy_score(bench_y_test, predicted_labels)\n",
    "print(f\"정확도: {accuracy:.3f}\")\n",
    "\n",
    "# 정밀도 계산\n",
    "precision = precision_score(bench_y_test, predicted_labels, average='micro')\n",
    "print(f\"정밀도: {precision:.3f}\")\n",
    "\n",
    "# 재현율 계산\n",
    "recall = recall_score(bench_y_test, predicted_labels, average='micro')\n",
    "print(f\"재현율: {recall:.3f}\")\n",
    "\n",
    "# F1 점수 계산\n",
    "f1 = f1_score(bench_y_test, predicted_labels, average='micro')\n",
    "print(f\"F1 점수: {f1:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "4414bf86",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x3600 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "confusion_matrix = multilabel_confusion_matrix(bench_y_test, predicted_labels)\n",
    "\n",
    "def plot_multilabel_confusion_matrix(confusion_matrix):\n",
    "    num_classes = len(confusion_matrix)\n",
    "    fig, axes = plt.subplots(nrows=num_classes, ncols=1, figsize=(8, 6 * num_classes))\n",
    "\n",
    "    for i in range(num_classes):\n",
    "        sns.heatmap(confusion_matrix[i], annot=True, cmap=\"Blues\", fmt=\"d\", ax=axes[i])\n",
    "        axes[i].set_title(f\"Class {i+1} Confusion Matrix\")\n",
    "        axes[i].set_xlabel(\"Predicted label\")\n",
    "        axes[i].set_ylabel(\"True label\")\n",
    "        axes[i].set_xticklabels(['True', 'False'])\n",
    "        axes[i].set_yticklabels(['True', 'False'])\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# 다중 레이블 오차 행렬 시각화\n",
    "plot_multilabel_confusion_matrix(confusion_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f5fffb7",
   "metadata": {},
   "source": [
    "### Squat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "352f5823",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 모델 생성\n",
    "input_size = 132\n",
    "output_size = 8\n",
    "squat_model = DeepClassifier(input_size, output_size)\n",
    "\n",
    "squat_criterion = nn.CrossEntropyLoss()\n",
    "squat_optimizer = optim.Adam(squat_model.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8a9e4e87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "squat_train_losses = []\n",
    "squat_test_losses = []\n",
    "\n",
    "epochs = 1000\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    squat_model.train()\n",
    "    squat_optimizer.zero_grad()\n",
    "    squat_outputs = squat_model(squat_X_train)\n",
    "    squat_loss = squat_criterion(squat_outputs, squat_y_train)\n",
    "    squat_loss.backward()\n",
    "    squat_optimizer.step()\n",
    "    squat_train_losses.append(squat_loss.item())\n",
    "    \n",
    "    squat_model.eval()\n",
    "    with torch.no_grad():\n",
    "        squat_test_outputs = squat_model(squat_X_test)\n",
    "        squat_test_loss = squat_criterion(squat_test_outputs, squat_y_test)\n",
    "        squat_test_losses.append(squat_test_loss.item())\n",
    "\n",
    "plt.plot(range(1, epochs+1), squat_train_losses, label='Train Loss')\n",
    "plt.plot(range(1, epochs+1), squat_test_losses, label='Test Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Training and Test Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a269f137",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Loss: 1.435266375541687\n"
     ]
    }
   ],
   "source": [
    "squat_model.eval()\n",
    "with torch.no_grad():\n",
    "    squat_test_outputs = squat_model(squat_X_test)\n",
    "    squat_test_loss = squat_criterion(squat_test_outputs, squat_y_test)\n",
    "    print(f'Test Loss: {squat_test_loss.item()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "1ea2e0eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "샘플 1:\n",
      "입력 데이터: tensor([ 0.5739,  0.2690, -0.4254,  0.9998,  0.5767,  0.2558, -0.4017,  0.9997,\n",
      "         0.5762,  0.2545, -0.4020,  0.9997,  0.5758,  0.2525, -0.4024,  0.9997,\n",
      "         0.5700,  0.2541, -0.4623,  0.9997,  0.5645,  0.2515, -0.4625,  0.9997,\n",
      "         0.5581,  0.2487, -0.4625,  0.9997,  0.5374,  0.2425, -0.2286,  0.9998,\n",
      "         0.5057,  0.2404, -0.4990,  0.9996,  0.5543,  0.2780, -0.3528,  0.9998,\n",
      "         0.5444,  0.2756, -0.4316,  0.9998,  0.4827,  0.2973, -0.0643,  0.9996,\n",
      "         0.3516,  0.2918, -0.5063,  0.9992,  0.5549,  0.3551, -0.2197,  0.8240,\n",
      "         0.3324,  0.4037, -0.7059,  0.9343,  0.5855,  0.2868, -0.5836,  0.7735,\n",
      "         0.4403,  0.3066, -0.8485,  0.9039,  0.5904,  0.2691, -0.6535,  0.6649,\n",
      "         0.4442,  0.2846, -0.9106,  0.8705,  0.5770,  0.2645, -0.6038,  0.6541,\n",
      "         0.4442,  0.2719, -0.9284,  0.8719,  0.5717,  0.2706, -0.5744,  0.6349,\n",
      "         0.4420,  0.2806, -0.8588,  0.8389,  0.2836,  0.4376,  0.1900,  0.9991,\n",
      "         0.1895,  0.4452, -0.1893,  0.9996,  0.4895,  0.4935,  0.2775,  0.8546,\n",
      "         0.3666,  0.5546, -0.3071,  0.9903,  0.3534,  0.5893,  0.6142,  0.8010,\n",
      "         0.2687,  0.6530, -0.1314,  0.9902,  0.3111,  0.5880,  0.6438,  0.7536,\n",
      "         0.2246,  0.6668, -0.1162,  0.9724,  0.3994,  0.6308,  0.6276,  0.8231,\n",
      "         0.3571,  0.6989, -0.2301,  0.9840])\n",
      "실제 타겟 데이터: tensor([0., 0., 1., 0., 0., 0., 0., 0.])\n",
      "예측된 데이터: tensor(7)\n",
      "예측 확률: ['0.0000', '0.0000', '0.0000', '0.0000', '0.0000', '0.0000', '0.0000', '1.0000']\n",
      "\n",
      "샘플 2:\n",
      "입력 데이터: tensor([ 0.5405,  0.4461, -0.7879,  1.0000,  0.5511,  0.4353, -0.7745,  1.0000,\n",
      "         0.5589,  0.4345, -0.7745,  1.0000,  0.5659,  0.4335, -0.7746,  1.0000,\n",
      "         0.5312,  0.4349, -0.7703,  1.0000,  0.5259,  0.4341, -0.7705,  1.0000,\n",
      "         0.5200,  0.4332, -0.7705,  1.0000,  0.5780,  0.4293, -0.5844,  1.0000,\n",
      "         0.5112,  0.4306, -0.5649,  1.0000,  0.5534,  0.4541, -0.7070,  1.0000,\n",
      "         0.5305,  0.4540, -0.7020,  1.0000,  0.6318,  0.4598, -0.3672,  1.0000,\n",
      "         0.4646,  0.4654, -0.3726,  1.0000,  0.7098,  0.5245, -0.5580,  0.9902,\n",
      "         0.3824,  0.5333, -0.5564,  0.9966,  0.6699,  0.4918, -0.9272,  0.9816,\n",
      "         0.4038,  0.4946, -0.8923,  0.9935,  0.6702,  0.4810, -0.9904,  0.9554,\n",
      "         0.4004,  0.4808, -0.9547,  0.9819,  0.6585,  0.4726, -0.9733,  0.9465,\n",
      "         0.4092,  0.4748, -0.9320,  0.9781,  0.6527,  0.4760, -0.9252,  0.8931,\n",
      "         0.4159,  0.4792, -0.8874,  0.9611,  0.5983,  0.5494,  0.0022,  0.9998,\n",
      "         0.5014,  0.5478, -0.0023,  0.9999,  0.7372,  0.5796, -0.4175,  0.9954,\n",
      "         0.3628,  0.5661, -0.3738,  0.9979,  0.6860,  0.6532,  0.0714,  0.9884,\n",
      "         0.3759,  0.6522,  0.0671,  0.9972,  0.6620,  0.6639,  0.1142,  0.9677,\n",
      "         0.3990,  0.6649,  0.1054,  0.9829,  0.7312,  0.6873, -0.0371,  0.9696,\n",
      "         0.3199,  0.6830, -0.0459,  0.9868])\n",
      "실제 타겟 데이터: tensor([0., 0., 0., 0., 1., 0., 0., 0.])\n",
      "예측된 데이터: tensor(4)\n",
      "예측 확률: ['0.0000', '0.0000', '0.0000', '0.0000', '1.0000', '0.0000', '0.0000', '0.0000']\n",
      "\n",
      "샘플 3:\n",
      "입력 데이터: tensor([ 0.5402,  0.2847, -0.9927,  1.0000,  0.5523,  0.2731, -0.9835,  1.0000,\n",
      "         0.5605,  0.2725, -0.9834,  1.0000,  0.5684,  0.2717, -0.9836,  1.0000,\n",
      "         0.5309,  0.2729, -0.9664,  1.0000,  0.5254,  0.2722, -0.9666,  1.0000,\n",
      "         0.5217,  0.2719, -0.9667,  1.0000,  0.5805,  0.2710, -0.7874,  1.0000,\n",
      "         0.5203,  0.2729, -0.7012,  1.0000,  0.5567,  0.2935, -0.9089,  1.0000,\n",
      "         0.5297,  0.2940, -0.8873,  1.0000,  0.6529,  0.3233, -0.5665,  1.0000,\n",
      "         0.4697,  0.3243, -0.4866,  1.0000,  0.7409,  0.3960, -0.9172,  0.9918,\n",
      "         0.3792,  0.3906, -0.7658,  0.9981,  0.6909,  0.3301, -1.3990,  0.9795,\n",
      "         0.4096,  0.3306, -1.2738,  0.9948,  0.6841,  0.3140, -1.4941,  0.9424,\n",
      "         0.4123,  0.3148, -1.3721,  0.9785,  0.6759,  0.3063, -1.4642,  0.9299,\n",
      "         0.4212,  0.3084, -1.3429,  0.9713,  0.6726,  0.3130, -1.3965,  0.8658,\n",
      "         0.4250,  0.3146, -1.2744,  0.9458,  0.6010,  0.4582, -0.0035,  0.9999,\n",
      "         0.4975,  0.4558,  0.0032,  0.9999,  0.6079,  0.5696, -0.1724,  0.9905,\n",
      "         0.4768,  0.5659, -0.1740,  0.9923,  0.5767,  0.6544,  0.3908,  0.9809,\n",
      "         0.4795,  0.6538,  0.3730,  0.9935,  0.5604,  0.6653,  0.4348,  0.9287,\n",
      "         0.4965,  0.6649,  0.4160,  0.9402,  0.5982,  0.6968,  0.2019,  0.9726,\n",
      "         0.4585,  0.6937,  0.1817,  0.9863])\n",
      "실제 타겟 데이터: tensor([0., 1., 0., 0., 0., 0., 0., 0.])\n",
      "예측된 데이터: tensor(1)\n",
      "예측 확률: ['0.0000', '1.0000', '0.0000', '0.0000', '0.0000', '0.0000', '0.0000', '0.0000']\n",
      "\n",
      "샘플 4:\n",
      "입력 데이터: tensor([ 5.5008e-01,  2.8259e-01, -7.5634e-01,  9.9999e-01,  5.6321e-01,\n",
      "         2.6973e-01, -7.4698e-01,  9.9999e-01,  5.7108e-01,  2.6861e-01,\n",
      "        -7.4691e-01,  9.9998e-01,  5.7733e-01,  2.6767e-01, -7.4712e-01,\n",
      "         9.9998e-01,  5.4102e-01,  2.7033e-01, -7.3785e-01,  9.9998e-01,\n",
      "         5.3433e-01,  2.6976e-01, -7.3796e-01,  9.9998e-01,  5.2802e-01,\n",
      "         2.6930e-01, -7.3795e-01,  9.9998e-01,  5.8971e-01,  2.6569e-01,\n",
      "        -5.4750e-01,  9.9998e-01,  5.2432e-01,  2.6779e-01, -5.0707e-01,\n",
      "         9.9998e-01,  5.6632e-01,  2.9053e-01, -6.7105e-01,  9.9999e-01,\n",
      "         5.3906e-01,  2.9169e-01, -6.5892e-01,  9.9999e-01,  6.6031e-01,\n",
      "         3.1477e-01, -3.3526e-01,  9.9998e-01,  4.7445e-01,  3.1964e-01,\n",
      "        -3.1623e-01,  9.9999e-01,  7.4528e-01,  3.8437e-01, -6.1573e-01,\n",
      "         9.9617e-01,  3.9453e-01,  3.8294e-01, -5.4667e-01,  9.9694e-01,\n",
      "         7.2088e-01,  3.1842e-01, -1.0577e+00,  9.9098e-01,  3.9866e-01,\n",
      "         3.2399e-01, -9.7126e-01,  9.9541e-01,  7.1563e-01,  3.0359e-01,\n",
      "        -1.1378e+00,  9.6909e-01,  4.0148e-01,  3.0810e-01, -1.0540e+00,\n",
      "         9.8344e-01,  7.0634e-01,  2.9519e-01, -1.1099e+00,  9.6383e-01,\n",
      "         4.0984e-01,  3.0189e-01, -1.0322e+00,  9.8028e-01,  7.0402e-01,\n",
      "         3.0183e-01, -1.0567e+00,  9.3677e-01,  4.1311e-01,  3.0728e-01,\n",
      "        -9.7279e-01,  9.6768e-01,  6.2235e-01,  4.5752e-01,  9.3094e-04,\n",
      "         9.9987e-01,  5.1623e-01,  4.5745e-01, -1.2755e-03,  9.9990e-01,\n",
      "         6.5014e-01,  5.7035e-01, -9.8079e-02,  9.9341e-01,  4.6157e-01,\n",
      "         5.6442e-01, -6.0154e-02,  9.9605e-01,  6.3920e-01,  6.5603e-01,\n",
      "         3.5471e-01,  9.8737e-01,  4.5638e-01,  6.5269e-01,  3.7875e-01,\n",
      "         9.9446e-01,  6.2134e-01,  6.6764e-01,  3.9219e-01,  9.1535e-01,\n",
      "         4.7291e-01,  6.6612e-01,  4.1562e-01,  9.3556e-01,  6.7136e-01,\n",
      "         6.9597e-01,  2.0510e-01,  9.7834e-01,  4.1329e-01,  6.9008e-01,\n",
      "         2.3526e-01,  9.8839e-01])\n",
      "실제 타겟 데이터: tensor([0., 0., 0., 1., 0., 0., 0., 0.])\n",
      "예측된 데이터: tensor(3)\n",
      "예측 확률: ['0.0000', '0.0000', '0.0000', '1.0000', '0.0000', '0.0000', '0.0000', '0.0000']\n",
      "\n",
      "샘플 5:\n",
      "입력 데이터: tensor([ 0.5311,  0.4664, -0.9413,  1.0000,  0.5415,  0.4604, -0.9745,  1.0000,\n",
      "         0.5489,  0.4597, -0.9743,  1.0000,  0.5564,  0.4586, -0.9745,  0.9999,\n",
      "         0.5254,  0.4591, -0.9744,  1.0000,  0.5212,  0.4577, -0.9746,  1.0000,\n",
      "         0.5172,  0.4562, -0.9745,  1.0000,  0.5722,  0.4485, -0.8631,  1.0000,\n",
      "         0.5144,  0.4459, -0.8085,  1.0000,  0.5439,  0.4661, -0.8991,  1.0000,\n",
      "         0.5244,  0.4650, -0.9083,  1.0000,  0.6499,  0.4460, -0.6422,  0.9999,\n",
      "         0.4724,  0.4432, -0.6154,  1.0000,  0.7181,  0.4885, -0.8281,  0.9770,\n",
      "         0.3960,  0.4901, -0.8422,  0.9969,  0.7070,  0.4940, -1.1063,  0.8638,\n",
      "         0.4075,  0.4896, -1.2236,  0.9939,  0.7047,  0.4934, -1.1803,  0.7712,\n",
      "         0.4036,  0.4832, -1.3003,  0.9794,  0.6924,  0.4861, -1.1703,  0.7611,\n",
      "         0.4177,  0.4826, -1.2852,  0.9768,  0.6886,  0.4863, -1.1102,  0.6861,\n",
      "         0.4228,  0.4839, -1.2249,  0.9606,  0.6110,  0.5039,  0.0029,  0.9999,\n",
      "         0.5126,  0.5003, -0.0030,  0.9999,  0.6595,  0.5741, -0.4237,  0.9841,\n",
      "         0.4479,  0.5654, -0.4486,  0.9918,  0.6339,  0.6565, -0.2195,  0.9829,\n",
      "         0.4593,  0.6508, -0.2123,  0.9930,  0.6159,  0.6690, -0.2116,  0.9093,\n",
      "         0.4736,  0.6658, -0.2002,  0.9291,  0.6613,  0.6944, -0.4257,  0.9769,\n",
      "         0.4238,  0.6870, -0.4022,  0.9864])\n",
      "실제 타겟 데이터: tensor([0., 0., 0., 0., 0., 0., 0., 1.])\n",
      "예측된 데이터: tensor(6)\n",
      "예측 확률: ['0.0000', '0.0000', '0.0000', '0.0000', '0.0000', '0.0000', '1.0000', '0.0000']\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/23/n8c37q85651bywf1rz00z90c0000gn/T/ipykernel_40713/3174289499.py:9: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  input_tensor = torch.tensor(input_sample, dtype=torch.float32)\n"
     ]
    }
   ],
   "source": [
    "# 테스트 데이터셋에서 샘플 선택\n",
    "num_samples_to_test = 5  # 확인할 샘플의 수\n",
    "for i in range(num_samples_to_test):\n",
    "    sample_index = i\n",
    "    input_sample = squat_X_test[sample_index]  # 입력 데이터\n",
    "    target_sample = squat_y_test[sample_index]  # 실제 타겟(정답) 데이터\n",
    "\n",
    "    # 입력 데이터를 Tensor로 변환\n",
    "    input_tensor = torch.tensor(input_sample, dtype=torch.float32)\n",
    "\n",
    "    # 모델에 입력하여 예측 생성\n",
    "    squat_model.eval()\n",
    "    with torch.no_grad():\n",
    "        prediction = squat_model(input_tensor.unsqueeze(0))  # unsqueeze를 사용하여 배치 차원 추가\n",
    "\n",
    "    # 예측 결과 출력\n",
    "    print(f\"샘플 {sample_index + 1}:\")\n",
    "    print(\"입력 데이터:\", input_sample)\n",
    "    print(\"실제 타겟 데이터:\", target_sample)\n",
    "    print(\"예측된 데이터:\", torch.argmax(prediction))  # 확률이 아닌 예측된 클래스를 출력\n",
    "    probabilities = prediction.squeeze().tolist()  # 텐서를 리스트로 변환\n",
    "    probabilities_str = [f\"{prob:.4f}\" for prob in probabilities]\n",
    "    print(\"예측 확률:\", probabilities_str)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7d1e3234",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "정확도: 0.836\n",
      "정밀도: 0.840\n",
      "재현율: 0.836\n",
      "F1 점수: 0.838\n"
     ]
    }
   ],
   "source": [
    "squat_model.eval()\n",
    "with torch.no_grad():\n",
    "    predicted_probs = squat_model(squat_X_test)\n",
    "\n",
    "threshold = 0.8  # 임계값 설정\n",
    "predicted_labels = (predicted_probs > threshold).numpy().astype(int)\n",
    "\n",
    "# 정확도 계산\n",
    "accuracy = accuracy_score(squat_y_test, predicted_labels)\n",
    "print(f\"정확도: {accuracy:.3f}\")\n",
    "\n",
    "# 정밀도 계산\n",
    "precision = precision_score(squat_y_test, predicted_labels, average='micro')\n",
    "print(f\"정밀도: {precision:.3f}\")\n",
    "\n",
    "# 재현율 계산\n",
    "recall = recall_score(squat_y_test, predicted_labels, average='micro')\n",
    "print(f\"재현율: {recall:.3f}\")\n",
    "\n",
    "# F1 점수 계산\n",
    "f1 = f1_score(squat_y_test, predicted_labels, average='micro')\n",
    "print(f\"F1 점수: {f1:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e57f97ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x4800 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "confusion_matrix = multilabel_confusion_matrix(squat_y_test, predicted_labels)\n",
    "\n",
    "def plot_multilabel_confusion_matrix(confusion_matrix):\n",
    "    num_classes = len(confusion_matrix)\n",
    "    fig, axes = plt.subplots(nrows=num_classes, ncols=1, figsize=(8, 6 * num_classes))\n",
    "\n",
    "    for i in range(num_classes):\n",
    "        sns.heatmap(confusion_matrix[i], annot=True, cmap=\"Blues\", fmt=\"d\", ax=axes[i])\n",
    "        axes[i].set_title(f\"Class {i+1} Confusion Matrix\")\n",
    "        axes[i].set_xlabel(\"Predicted label\")\n",
    "        axes[i].set_ylabel(\"True label\")\n",
    "        axes[i].set_xticklabels(['True', 'False'])\n",
    "        axes[i].set_yticklabels(['True', 'False'])\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# 다중 레이블 오차 행렬 시각화\n",
    "plot_multilabel_confusion_matrix(confusion_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50889ab8",
   "metadata": {},
   "source": [
    "### Deadlift"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d9c8d471",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 모델 생성\n",
    "input_size = 132\n",
    "output_size = 8\n",
    "deadlift_model = DeepClassifier(input_size, output_size)\n",
    "\n",
    "deadlift_criterion = nn.CrossEntropyLoss()\n",
    "deadlift_optimizer = optim.Adam(deadlift_model.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "2a73ae3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "deadlift_train_losses = []\n",
    "deadlift_test_losses = []\n",
    "\n",
    "epochs = 1000\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    deadlift_model.train()\n",
    "    deadlift_optimizer.zero_grad()\n",
    "    deadlift_outputs = deadlift_model(deadlift_X_train)\n",
    "    deadlift_loss = deadlift_criterion(deadlift_outputs, deadlift_y_train)\n",
    "    deadlift_loss.backward()\n",
    "    deadlift_optimizer.step()\n",
    "    deadlift_train_losses.append(deadlift_loss.item())\n",
    "    \n",
    "    deadlift_model.eval()\n",
    "    with torch.no_grad():\n",
    "        deadlift_test_outputs = deadlift_model(deadlift_X_test)\n",
    "        deadlift_test_loss = deadlift_criterion(deadlift_test_outputs, deadlift_y_test)\n",
    "        deadlift_test_losses.append(deadlift_test_loss.item())\n",
    "\n",
    "plt.plot(range(1, epochs+1), deadlift_train_losses, label='Train Loss')\n",
    "plt.plot(range(1, epochs+1), deadlift_test_losses, label='Test Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Training and Test Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f733762f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Loss: 1.658890962600708\n"
     ]
    }
   ],
   "source": [
    "deadlift_model.eval()\n",
    "with torch.no_grad():\n",
    "    deadlift_test_outputs = deadlift_model(deadlift_X_test)\n",
    "    deadlift_test_loss = deadlift_criterion(deadlift_test_outputs, deadlift_y_test)\n",
    "    print(f'Test Loss: {deadlift_test_loss.item()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "262be563",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "샘플 1:\n",
      "입력 데이터: tensor([ 0.4146,  0.4309, -0.7695,  1.0000,  0.4354,  0.4154, -0.7818,  1.0000,\n",
      "         0.4463,  0.4141, -0.7817,  1.0000,  0.4554,  0.4127, -0.7820,  1.0000,\n",
      "         0.4016,  0.4144, -0.7770,  1.0000,  0.3916,  0.4122, -0.7771,  1.0000,\n",
      "         0.3826,  0.4095, -0.7771,  1.0000,  0.4748,  0.4026, -0.6968,  1.0000,\n",
      "         0.3685,  0.3992, -0.6764,  1.0000,  0.4313,  0.4416, -0.7229,  1.0000,\n",
      "         0.3970,  0.4400, -0.7176,  1.0000,  0.5582,  0.4512, -0.5469,  1.0000,\n",
      "         0.2866,  0.4516, -0.5361,  1.0000,  0.5272,  0.5595, -0.5543,  0.9944,\n",
      "         0.3029,  0.5568, -0.5352,  0.9957,  0.4899,  0.6544, -0.6108,  0.9834,\n",
      "         0.3252,  0.6526, -0.5646,  0.9793,  0.4925,  0.6796, -0.6503,  0.9668,\n",
      "         0.3194,  0.6798, -0.5973,  0.9524,  0.4717,  0.6785, -0.6656,  0.9685,\n",
      "         0.3434,  0.6772, -0.6122,  0.9533,  0.4667,  0.6681, -0.6173,  0.9629,\n",
      "         0.3499,  0.6669, -0.5707,  0.9530,  0.4895,  0.5035, -0.0029,  1.0000,\n",
      "         0.3615,  0.4981,  0.0024,  1.0000,  0.4790,  0.6151, -0.3716,  0.8953,\n",
      "         0.3247,  0.6239, -0.3485,  0.9263,  0.4548,  0.7538, -0.2850,  0.9307,\n",
      "         0.3546,  0.7509, -0.2576,  0.9590,  0.4345,  0.7694, -0.2875,  0.7929,\n",
      "         0.3778,  0.7725, -0.2613,  0.7856,  0.4776,  0.7903, -0.4905,  0.9534,\n",
      "         0.3018,  0.7906, -0.4706,  0.9664])\n",
      "실제 타겟 데이터: tensor([1., 0., 0., 0., 0., 0., 0., 0.])\n",
      "예측된 데이터: tensor(0)\n",
      "예측 확률: ['1.0000', '0.0000', '0.0000', '0.0000', '0.0000', '0.0000', '0.0000', '0.0000']\n",
      "\n",
      "샘플 2:\n",
      "입력 데이터: tensor([ 0.5046,  0.4078, -1.0458,  1.0000,  0.5236,  0.3942, -1.0419,  1.0000,\n",
      "         0.5343,  0.3935, -1.0418,  1.0000,  0.5439,  0.3922, -1.0420,  1.0000,\n",
      "         0.4933,  0.3920, -1.0376,  1.0000,  0.4842,  0.3897, -1.0377,  1.0000,\n",
      "         0.4761,  0.3872, -1.0376,  1.0000,  0.5612,  0.3834, -0.8939,  1.0000,\n",
      "         0.4667,  0.3788, -0.8737,  1.0000,  0.5234,  0.4183, -0.9787,  1.0000,\n",
      "         0.4887,  0.4162, -0.9729,  1.0000,  0.6469,  0.4403, -0.7013,  1.0000,\n",
      "         0.3847,  0.4334, -0.6668,  1.0000,  0.6684,  0.5564, -0.6953,  0.9951,\n",
      "         0.3378,  0.5493, -0.6486,  0.9904,  0.6771,  0.6518, -0.7462,  0.9889,\n",
      "         0.3243,  0.6479, -0.6964,  0.9629,  0.6783,  0.6809, -0.7842,  0.9736,\n",
      "         0.3162,  0.6750, -0.7325,  0.9209,  0.6545,  0.6756, -0.8178,  0.9754,\n",
      "         0.3351,  0.6739, -0.7724,  0.9279,  0.6502,  0.6662, -0.7600,  0.9734,\n",
      "         0.3436,  0.6648, -0.7148,  0.9345,  0.5785,  0.5133, -0.0028,  1.0000,\n",
      "         0.4446,  0.5088,  0.0023,  1.0000,  0.6204,  0.6139, -0.5082,  0.9419,\n",
      "         0.3737,  0.6077, -0.4251,  0.9556,  0.5837,  0.7521, -0.2426,  0.9584,\n",
      "         0.3719,  0.7462, -0.1773,  0.9691,  0.5568,  0.7744, -0.2290,  0.7810,\n",
      "         0.3958,  0.7706, -0.1649,  0.8440,  0.6174,  0.8017, -0.4702,  0.9465,\n",
      "         0.3048,  0.7891, -0.3953,  0.9633])\n",
      "실제 타겟 데이터: tensor([0., 0., 0., 0., 1., 0., 0., 0.])\n",
      "예측된 데이터: tensor(7)\n",
      "예측 확률: ['0.0000', '0.0000', '0.0000', '0.0000', '0.0000', '0.0000', '0.0000', '1.0000']\n",
      "\n",
      "샘플 3:\n",
      "입력 데이터: tensor([ 0.6652,  0.4448, -0.4934,  1.0000,  0.6699,  0.4339, -0.4611,  0.9999,\n",
      "         0.6699,  0.4334, -0.4612,  0.9999,  0.6698,  0.4329, -0.4613,  0.9999,\n",
      "         0.6648,  0.4331, -0.5317,  0.9999,  0.6611,  0.4317, -0.5318,  0.9999,\n",
      "         0.6572,  0.4301, -0.5318,  0.9999,  0.6421,  0.4266, -0.2903,  0.9999,\n",
      "         0.6242,  0.4211, -0.6078,  0.9999,  0.6511,  0.4495, -0.4239,  1.0000,\n",
      "         0.6440,  0.4478, -0.5164,  1.0000,  0.5688,  0.4528, -0.0790,  0.9999,\n",
      "         0.4808,  0.4533, -0.6616,  0.9998,  0.5544,  0.5294,  0.0545,  0.8666,\n",
      "         0.4115,  0.5456, -0.7028,  0.9889,  0.5534,  0.5949, -0.0310,  0.9006,\n",
      "         0.3956,  0.6243, -0.6935,  0.9773,  0.5611,  0.6122, -0.0329,  0.8531,\n",
      "         0.3955,  0.6472, -0.7847,  0.9595,  0.5600,  0.6127, -0.1130,  0.8654,\n",
      "         0.4085,  0.6468, -0.7779,  0.9588,  0.5544,  0.6071, -0.0647,  0.8590,\n",
      "         0.4119,  0.6416, -0.6942,  0.9313,  0.3561,  0.4887,  0.1536,  0.9987,\n",
      "         0.2855,  0.4864, -0.1536,  0.9982,  0.5096,  0.5441,  0.0777,  0.7914,\n",
      "         0.4276,  0.5456, -0.3829,  0.9756,  0.4346,  0.6701,  0.1463,  0.7656,\n",
      "         0.3808,  0.6442, -0.0457,  0.9140,  0.4063,  0.6826,  0.1531,  0.6172,\n",
      "         0.3628,  0.6539, -0.0109,  0.8131,  0.4936,  0.6998,  0.0784,  0.7690,\n",
      "         0.4368,  0.6818, -0.0448,  0.8918])\n",
      "실제 타겟 데이터: tensor([0., 0., 0., 0., 1., 0., 0., 0.])\n",
      "예측된 데이터: tensor(2)\n",
      "예측 확률: ['0.0000', '0.0000', '1.0000', '0.0000', '0.0000', '0.0000', '0.0000', '0.0000']\n",
      "\n",
      "샘플 4:\n",
      "입력 데이터: tensor([ 0.6184,  0.3656,  0.8670,  0.9943,  0.6043,  0.3596,  0.8573,  0.9951,\n",
      "         0.6009,  0.3603,  0.8573,  0.9949,  0.5966,  0.3609,  0.8571,  0.9950,\n",
      "         0.6166,  0.3580,  0.8751,  0.9945,  0.6209,  0.3565,  0.8752,  0.9942,\n",
      "         0.6249,  0.3550,  0.8752,  0.9937,  0.5989,  0.3625,  0.7051,  0.9966,\n",
      "         0.6356,  0.3517,  0.8242,  0.9944,  0.6229,  0.3687,  0.8119,  0.9954,\n",
      "         0.6355,  0.3656,  0.8349,  0.9952,  0.6040,  0.3782,  0.3925,  1.0000,\n",
      "         0.7247,  0.3537,  0.7365,  0.9993,  0.5971,  0.4575,  0.2332,  0.9903,\n",
      "         0.7726,  0.4144,  0.6414,  0.1207,  0.5895,  0.5358,  0.2001,  0.9595,\n",
      "         0.8226,  0.4839,  0.4854,  0.0984,  0.5879,  0.5505,  0.1367,  0.9272,\n",
      "         0.8256,  0.4980,  0.4719,  0.1059,  0.5874,  0.5504,  0.2018,  0.9181,\n",
      "         0.8201,  0.5029,  0.4634,  0.1016,  0.5927,  0.5452,  0.2226,  0.8752,\n",
      "         0.8106,  0.4970,  0.4784,  0.0964,  0.7715,  0.4056, -0.1336,  0.9999,\n",
      "         0.8772,  0.3918,  0.1336,  0.9998,  0.6956,  0.5188,  0.0026,  0.9944,\n",
      "         0.8215,  0.5159,  0.2250,  0.8938,  0.6901,  0.6274,  0.1652,  0.9797,\n",
      "         0.7839,  0.6173,  0.3845,  0.9314,  0.7069,  0.6538,  0.1901,  0.9758,\n",
      "         0.8042,  0.6437,  0.4040,  0.9589,  0.6113,  0.6384,  0.1763,  0.8946,\n",
      "         0.7340,  0.6240,  0.3777,  0.7793])\n",
      "실제 타겟 데이터: tensor([0., 0., 0., 0., 0., 0., 1., 0.])\n",
      "예측된 데이터: tensor(0)\n",
      "예측 확률: ['0.9996', '0.0000', '0.0000', '0.0000', '0.0000', '0.0000', '0.0004', '0.0000']\n",
      "\n",
      "샘플 5:\n",
      "입력 데이터: tensor([ 0.6087,  0.2483, -0.7893,  1.0000,  0.6050,  0.2353, -0.7179,  0.9999,\n",
      "         0.6049,  0.2348, -0.7182,  0.9999,  0.6046,  0.2342, -0.7185,  1.0000,\n",
      "         0.5970,  0.2348, -0.7868,  1.0000,  0.5909,  0.2341, -0.7874,  1.0000,\n",
      "         0.5841,  0.2334, -0.7879,  1.0000,  0.5781,  0.2361, -0.4575,  0.9999,\n",
      "         0.5495,  0.2359, -0.7174,  0.9999,  0.5996,  0.2594, -0.6628,  1.0000,\n",
      "         0.5905,  0.2586, -0.7485,  1.0000,  0.5505,  0.3152, -0.1740,  0.9999,\n",
      "         0.4419,  0.3075, -0.7615,  0.9997,  0.5324,  0.3896,  0.0195,  0.7936,\n",
      "         0.3822,  0.4019, -0.7333,  0.9880,  0.5292,  0.4622, -0.0365,  0.8979,\n",
      "         0.3665,  0.4859, -0.7262,  0.9858,  0.5345,  0.4830, -0.0365,  0.8511,\n",
      "         0.3843,  0.5057, -0.8000,  0.9727,  0.5310,  0.4830, -0.1235,  0.8654,\n",
      "         0.4083,  0.5048, -0.8102,  0.9727,  0.5258,  0.4767, -0.0669,  0.8556,\n",
      "         0.4175,  0.4955, -0.7310,  0.9548,  0.3939,  0.4030,  0.1545,  0.9979,\n",
      "         0.3063,  0.4058, -0.1540,  0.9978,  0.4630,  0.5267,  0.0030,  0.6047,\n",
      "         0.3805,  0.5351, -0.2964,  0.8651,  0.4616,  0.6622,  0.0843,  0.4322,\n",
      "         0.3951,  0.6815, -0.2239,  0.5280,  0.4487,  0.6815,  0.0847,  0.3210,\n",
      "         0.3643,  0.7045, -0.2262,  0.4526,  0.5591,  0.6862, -0.0540,  0.4508,\n",
      "         0.4825,  0.7036, -0.4233,  0.5339])\n",
      "실제 타겟 데이터: tensor([0., 0., 0., 0., 0., 0., 0., 1.])\n",
      "예측된 데이터: tensor(3)\n",
      "예측 확률: ['0.0000', '0.0000', '0.0000', '0.9784', '0.0000', '0.0000', '0.0000', '0.0215']\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/23/n8c37q85651bywf1rz00z90c0000gn/T/ipykernel_40713/2186968047.py:9: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  input_tensor = torch.tensor(input_sample, dtype=torch.float32)\n"
     ]
    }
   ],
   "source": [
    "# 테스트 데이터셋에서 샘플 선택\n",
    "num_samples_to_test = 5  # 확인할 샘플의 수\n",
    "for i in range(num_samples_to_test):\n",
    "    sample_index = i\n",
    "    input_sample = deadlift_X_test[sample_index]  # 입력 데이터\n",
    "    target_sample = deadlift_y_test[sample_index]  # 실제 타겟(정답) 데이터\n",
    "\n",
    "    # 입력 데이터를 Tensor로 변환\n",
    "    input_tensor = torch.tensor(input_sample, dtype=torch.float32)\n",
    "\n",
    "    # 모델에 입력하여 예측 생성\n",
    "    deadlift_model.eval()\n",
    "    with torch.no_grad():\n",
    "        prediction = deadlift_model(input_tensor.unsqueeze(0))  # unsqueeze를 사용하여 배치 차원 추가\n",
    "\n",
    "    # 예측 결과 출력\n",
    "    print(f\"샘플 {sample_index + 1}:\")\n",
    "    print(\"입력 데이터:\", input_sample)\n",
    "    print(\"실제 타겟 데이터:\", target_sample)\n",
    "    print(\"예측된 데이터:\", torch.argmax(prediction))  # 확률이 아닌 예측된 클래스를 출력\n",
    "    probabilities = prediction.squeeze().tolist()  # 텐서를 리스트로 변환\n",
    "    probabilities_str = [f\"{prob:.4f}\" for prob in probabilities]\n",
    "    print(\"예측 확률:\", probabilities_str)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "11299320",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "정확도: 0.600\n",
      "정밀도: 0.620\n",
      "재현율: 0.600\n",
      "F1 점수: 0.610\n"
     ]
    }
   ],
   "source": [
    "deadlift_model.eval()\n",
    "with torch.no_grad():\n",
    "    predicted_probs = deadlift_model(deadlift_X_test)\n",
    "\n",
    "threshold = 0.8  # 임계값 설정\n",
    "predicted_labels = (predicted_probs > threshold).numpy().astype(int)\n",
    "\n",
    "# 정확도 계산\n",
    "accuracy = accuracy_score(deadlift_y_test, predicted_labels)\n",
    "print(f\"정확도: {accuracy:.3f}\")\n",
    "\n",
    "# 정밀도 계산\n",
    "precision = precision_score(deadlift_y_test, predicted_labels, average='micro')\n",
    "print(f\"정밀도: {precision:.3f}\")\n",
    "\n",
    "# 재현율 계산\n",
    "recall = recall_score(deadlift_y_test, predicted_labels, average='micro')\n",
    "print(f\"재현율: {recall:.3f}\")\n",
    "\n",
    "# F1 점수 계산\n",
    "f1 = f1_score(deadlift_y_test, predicted_labels, average='micro')\n",
    "print(f\"F1 점수: {f1:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "41d1840a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x4800 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "confusion_matrix = multilabel_confusion_matrix(deadlift_y_test, predicted_labels)\n",
    "\n",
    "def plot_multilabel_confusion_matrix(confusion_matrix):\n",
    "    num_classes = len(confusion_matrix)\n",
    "    fig, axes = plt.subplots(nrows=num_classes, ncols=1, figsize=(8, 6 * num_classes))\n",
    "\n",
    "    for i in range(num_classes):\n",
    "        sns.heatmap(confusion_matrix[i], annot=True, cmap=\"Blues\", fmt=\"d\", ax=axes[i])\n",
    "        axes[i].set_title(f\"Class {i+1} Confusion Matrix\")\n",
    "        axes[i].set_xlabel(\"Predicted label\")\n",
    "        axes[i].set_ylabel(\"True label\")\n",
    "        axes[i].set_xticklabels(['True', 'False'])\n",
    "        axes[i].set_yticklabels(['True', 'False'])\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# 다중 레이블 오차 행렬 시각화\n",
    "plot_multilabel_confusion_matrix(confusion_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0eb5693",
   "metadata": {},
   "source": [
    "# Save models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "38c44f0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "# Bench Press\n",
    "with open('benchpress_model_deep.pkl', 'wb') as f:\n",
    "    pickle.dump(bench_model, f)\n",
    "\n",
    "# Squat\n",
    "with open('squat_model_deep.pkl', 'wb') as f:\n",
    "    pickle.dump(squat_model, f)\n",
    "\n",
    "# Deadlift\n",
    "with open('deadlift_model_deep.pkl', 'wb') as f:\n",
    "    pickle.dump(deadlift_model, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "eca7f824",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([239, 132])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bench_X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ffeba6c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([232, 132])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "squat_X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "923f2a22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([285, 132])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deadlift_X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc644d48",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "PyTorch",
   "language": "python",
   "name": "pytorch_env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}