DeepLearning V3[Parameters].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",
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   "id": "6ca142bf",
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   "outputs": [
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       "3    d_correct_up  0.704687  0.148774  0.440767  0.995209  0.700908  0.139350   \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": [
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  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fe058cdc",
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   "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": 11,
   "id": "b5c55c18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([239, 6])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bench_y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "53c59927",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([716, 6])"
      ]
     },
     "execution_count": 12,
     "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):\n",
    "        super(DeepClassifier, self).__init__()\n",
    "        self.fc1 = nn.Linear(input_size, 128)\n",
    "        self.fc2 = nn.Linear(128, 64)\n",
    "        self.fc3 = nn.Linear(64, 32)\n",
    "        self.fc4 = nn.Linear(32, 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",
    "        x = self.dropout(self.relu(self.fc1(x)))\n",
    "        x = self.dropout(self.relu(self.fc2(x)))\n",
    "        x = self.dropout(self.relu(self.fc3(x)))\n",
    "        x = self.fc4(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": 31,
   "id": "afdac53a",
   "metadata": {},
   "outputs": [],
   "source": [
    "bench_criterion = nn.CrossEntropyLoss()\n",
    "bench_optimizer = optim.RMSprop(bench_model.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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",
    "    loss = bench_criterion(bench_outputs, bench_y_train)\n",
    "    loss.backward()\n",
    "    bench_optimizer.step()\n",
    "    bench_train_losses.append(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": 33,
   "id": "fbca1495",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Loss: 1.2266932725906372\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": 34,
   "id": "8bcb0bfe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "샘플 1:\n",
      "입력 데이터: tensor([ 0.5088,  0.3592,  0.7493,  1.0000,  0.5188,  0.3591,  0.7876,  1.0000,\n",
      "         0.5265,  0.3603,  0.7879,  1.0000,  0.5345,  0.3619,  0.7875,  1.0000,\n",
      "         0.4997,  0.3587,  0.7923,  1.0000,  0.4936,  0.3595,  0.7923,  1.0000,\n",
      "         0.4875,  0.3606,  0.7924,  1.0000,  0.5461,  0.3716,  0.7992,  1.0000,\n",
      "         0.4790,  0.3691,  0.8272,  1.0000,  0.5233,  0.3677,  0.7394,  1.0000,\n",
      "         0.4977,  0.3668,  0.7443,  1.0000,  0.6162,  0.4159,  0.6981,  1.0000,\n",
      "         0.4133,  0.4160,  0.6916,  1.0000,  0.7235,  0.4538,  0.9193,  0.9600,\n",
      "         0.2895,  0.4709,  0.6769,  0.9967,  0.7793,  0.4028,  1.1261,  0.9895,\n",
      "         0.2588,  0.4053,  0.6340,  0.9995,  0.7937,  0.3840,  1.1935,  0.9769,\n",
      "         0.2555,  0.3880,  0.6534,  0.9981,  0.7783,  0.3840,  1.1943,  0.9730,\n",
      "         0.2709,  0.3845,  0.6676,  0.9970,  0.7693,  0.3902,  1.1314,  0.9768,\n",
      "         0.2780,  0.3893,  0.6371,  0.9973,  0.5956,  0.5067,  0.0090,  1.0000,\n",
      "         0.4225,  0.5023, -0.0100,  1.0000,  0.7932,  0.5804, -0.4660,  0.9961,\n",
      "         0.1974,  0.5590, -0.4639,  0.9979,  0.7585,  0.6848, -0.0048,  0.9183,\n",
      "         0.2608,  0.6742, -0.0599,  0.9894,  0.7185,  0.6988,  0.0372,  0.9077,\n",
      "         0.2960,  0.6846, -0.0148,  0.9516,  0.8223,  0.7427, -0.1058,  0.9048,\n",
      "         0.1884,  0.7385, -0.1725,  0.9700])\n",
      "실제 타겟 데이터: tensor([0., 0., 1., 0., 0., 0.])\n",
      "예측된 데이터: tensor(2)\n",
      "예측 확률: ['0.1187', '0.0000', '0.8813', '0.0000', '0.0000', '0.0000']\n",
      "\n",
      "샘플 2:\n",
      "입력 데이터: tensor([ 0.8048,  0.3061,  0.7661,  1.0000,  0.8194,  0.3064,  0.8118,  1.0000,\n",
      "         0.8243,  0.3086,  0.8118,  1.0000,  0.8290,  0.3109,  0.8117,  1.0000,\n",
      "         0.8071,  0.3033,  0.8385,  1.0000,  0.8037,  0.3032,  0.8385,  1.0000,\n",
      "         0.8000,  0.3032,  0.8387,  1.0000,  0.8312,  0.3256,  0.9274,  1.0000,\n",
      "         0.7949,  0.3112,  1.0554,  1.0000,  0.8060,  0.3163,  0.7973,  1.0000,\n",
      "         0.7929,  0.3129,  0.8338,  1.0000,  0.8416,  0.3708,  0.7971,  1.0000,\n",
      "         0.6889,  0.3394,  0.9947,  1.0000,  0.9513,  0.3848,  0.6705,  0.8533,\n",
      "         0.5689,  0.3421,  1.1393,  0.4734,  0.9890,  0.3413,  0.7069,  0.8517,\n",
      "         0.5395,  0.2783,  1.3134,  0.7565,  1.0085,  0.3277,  0.6998,  0.7990,\n",
      "         0.5261,  0.2642,  1.3360,  0.7939,  0.9982,  0.3250,  0.7000,  0.7716,\n",
      "         0.5364,  0.2613,  1.3116,  0.7869,  0.9897,  0.3320,  0.7050,  0.7442,\n",
      "         0.5415,  0.2653,  1.3051,  0.7677,  0.6413,  0.4187, -0.0716,  1.0000,\n",
      "         0.5260,  0.4034,  0.0716,  1.0000,  0.6180,  0.5028, -1.0090,  0.9956,\n",
      "         0.2758,  0.4059, -0.2416,  0.9875,  0.6181,  0.6346, -1.2871,  0.9812,\n",
      "         0.2603,  0.5350, -0.2582,  0.9721,  0.6070,  0.6450, -1.3208,  0.9849,\n",
      "         0.2848,  0.5599, -0.2770,  0.9699,  0.6063,  0.6876, -1.5546,  0.9969,\n",
      "         0.1425,  0.5666, -0.4717,  0.9810])\n",
      "실제 타겟 데이터: tensor([0., 1., 0., 0., 0., 0.])\n",
      "예측된 데이터: tensor(1)\n",
      "예측 확률: ['0.0002', '0.9796', '0.0000', '0.0202', '0.0000', '0.0000']\n",
      "\n",
      "샘플 3:\n",
      "입력 데이터: tensor([ 0.4936,  0.3756,  0.9138,  1.0000,  0.5032,  0.3715,  0.9815,  1.0000,\n",
      "         0.5111,  0.3726,  0.9819,  1.0000,  0.5191,  0.3738,  0.9815,  1.0000,\n",
      "         0.4827,  0.3713,  0.9996,  1.0000,  0.4757,  0.3722,  0.9995,  1.0000,\n",
      "         0.4685,  0.3735,  0.9998,  1.0000,  0.5323,  0.3834,  1.0908,  1.0000,\n",
      "         0.4589,  0.3840,  1.1734,  1.0000,  0.5103,  0.3843,  0.9313,  1.0000,\n",
      "         0.4827,  0.3845,  0.9531,  1.0000,  0.6079,  0.4243,  0.9048,  1.0000,\n",
      "         0.3997,  0.4301,  1.0576,  1.0000,  0.7406,  0.4079,  0.7727,  0.9695,\n",
      "         0.2690,  0.3957,  1.0039,  0.9957,  0.7719,  0.3249,  0.7194,  0.9675,\n",
      "         0.2350,  0.3221,  0.9669,  0.9971,  0.7743,  0.3077,  0.7140,  0.9254,\n",
      "         0.2254,  0.3023,  0.9490,  0.9922,  0.7597,  0.3063,  0.7327,  0.9118,\n",
      "         0.2469,  0.3005,  0.9922,  0.9887,  0.7558,  0.3127,  0.7191,  0.9198,\n",
      "         0.2511,  0.3064,  0.9739,  0.9875,  0.5881,  0.5126, -0.0233,  1.0000,\n",
      "         0.4330,  0.5292,  0.0231,  1.0000,  0.6908,  0.5766, -0.4446,  0.9892,\n",
      "         0.2635,  0.6454, -0.5108,  0.9874,  0.6646,  0.6288, -0.2013,  0.2715,\n",
      "         0.4008,  0.6337, -0.1301,  0.1398,  0.6741,  0.6375, -0.1716,  0.5290,\n",
      "         0.4143,  0.6178, -0.0858,  0.3438,  0.7104,  0.6767, -0.2351,  0.5144,\n",
      "         0.3701,  0.6542, -0.1158,  0.2908])\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 4:\n",
      "입력 데이터: tensor([ 0.8253,  0.3420,  0.5918,  1.0000,  0.8375,  0.3477,  0.6391,  0.9999,\n",
      "         0.8404,  0.3494,  0.6391,  0.9999,  0.8435,  0.3524,  0.6388,  0.9999,\n",
      "         0.8293,  0.3440,  0.6456,  0.9999,  0.8256,  0.3436,  0.6456,  0.9998,\n",
      "         0.8228,  0.3422,  0.6456,  0.9999,  0.8381,  0.3610,  0.7696,  1.0000,\n",
      "         0.8090,  0.3405,  0.8036,  0.9999,  0.8192,  0.3508,  0.6338,  0.9999,\n",
      "         0.8077,  0.3471,  0.6419,  0.9996,  0.8354,  0.3853,  0.6563,  1.0000,\n",
      "         0.7064,  0.3415,  0.7726,  0.9999,  0.9171,  0.4278,  0.6169,  0.9504,\n",
      "         0.6000,  0.3541,  0.9714,  0.0427,  0.9799,  0.3717,  0.7516,  0.8956,\n",
      "         0.5384,  0.3186,  1.2044,  0.0609,  0.9915,  0.3582,  0.7580,  0.8331,\n",
      "         0.5207,  0.3100,  1.2442,  0.0743,  0.9875,  0.3546,  0.7830,  0.7974,\n",
      "         0.5247,  0.3077,  1.2091,  0.0731,  0.9798,  0.3591,  0.7631,  0.7489,\n",
      "         0.5346,  0.3143,  1.1955,  0.0741,  0.6421,  0.4294, -0.0431,  1.0000,\n",
      "         0.5248,  0.4037,  0.0431,  1.0000,  0.6084,  0.4923, -0.7540,  0.9891,\n",
      "         0.2759,  0.4094, -0.2851,  0.9730,  0.6100,  0.6292, -0.7409,  0.9662,\n",
      "         0.2581,  0.5341,  0.0616,  0.9441,  0.5689,  0.6400, -0.7354,  0.9390,\n",
      "         0.2817,  0.5559,  0.0923,  0.8505,  0.5584,  0.6942, -0.9040,  0.9874,\n",
      "         0.1389,  0.5652, -0.0359,  0.9338])\n",
      "실제 타겟 데이터: tensor([1., 0., 0., 0., 0., 0.])\n",
      "예측된 데이터: tensor(2)\n",
      "예측 확률: ['0.2732', '0.0000', '0.7268', '0.0000', '0.0000', '0.0000']\n",
      "\n",
      "샘플 5:\n",
      "입력 데이터: tensor([ 0.5162,  0.3736,  0.8887,  1.0000,  0.5234,  0.3758,  0.9352,  1.0000,\n",
      "         0.5310,  0.3776,  0.9357,  1.0000,  0.5387,  0.3796,  0.9353,  1.0000,\n",
      "         0.5036,  0.3742,  0.9558,  1.0000,  0.4976,  0.3749,  0.9557,  1.0000,\n",
      "         0.4918,  0.3756,  0.9560,  1.0000,  0.5479,  0.3916,  0.9522,  1.0000,\n",
      "         0.4788,  0.3868,  1.0493,  1.0000,  0.5306,  0.3814,  0.8707,  1.0000,\n",
      "         0.5061,  0.3795,  0.8973,  1.0000,  0.6220,  0.4277,  0.7700,  1.0000,\n",
      "         0.4024,  0.4202,  0.8835,  1.0000,  0.7363,  0.3743,  0.8312,  0.9691,\n",
      "         0.2981,  0.3560,  0.9760,  0.9972,  0.7916,  0.3006,  1.0129,  0.9668,\n",
      "         0.2607,  0.2815,  1.1399,  0.9956,  0.7977,  0.2824,  1.0228,  0.9351,\n",
      "         0.2408,  0.2622,  1.1407,  0.9903,  0.7839,  0.2811,  1.0227,  0.9237,\n",
      "         0.2611,  0.2642,  1.1583,  0.9856,  0.7765,  0.2876,  1.0068,  0.9312,\n",
      "         0.2693,  0.2694,  1.1364,  0.9857,  0.5985,  0.5319, -0.0046,  1.0000,\n",
      "         0.4168,  0.5304,  0.0044,  1.0000,  0.8084,  0.5996, -0.6189,  0.9974,\n",
      "         0.2012,  0.5822, -0.6202,  0.9958,  0.7715,  0.7261, -0.3288,  0.9196,\n",
      "         0.2202,  0.7200, -0.3554,  0.9745,  0.7294,  0.7405, -0.3062,  0.9485,\n",
      "         0.2516,  0.7347, -0.3328,  0.9648,  0.8354,  0.7949, -0.5056,  0.9573,\n",
      "         0.1580,  0.7932, -0.5571,  0.9815])\n",
      "실제 타겟 데이터: tensor([0., 1., 0., 0., 0., 0.])\n",
      "예측된 데이터: tensor(1)\n",
      "예측 확률: ['0.0000', '0.7893', '0.0000', '0.2106', '0.0000', '0.0000']\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/23/n8c37q85651bywf1rz00z90c0000gn/T/ipykernel_64197/658835650.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": 35,
   "id": "9cacd475",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "정확도: 0.774\n",
      "정밀도: 0.860\n",
      "재현율: 0.774\n",
      "F1 점수: 0.815\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": 36,
   "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": 37,
   "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.RMSprop(squat_model.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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": 39,
   "id": "a269f137",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Loss: 1.5796109437942505\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": 40,
   "id": "1ea2e0eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "샘플 1:\n",
      "입력 데이터: tensor([ 0.5493,  0.1420, -0.5631,  0.9998,  0.5509,  0.1209, -0.5358,  0.9998,\n",
      "         0.5513,  0.1195, -0.5362,  0.9999,  0.5513,  0.1181, -0.5366,  0.9999,\n",
      "         0.5372,  0.1191, -0.5953,  0.9998,  0.5281,  0.1167, -0.5955,  0.9998,\n",
      "         0.5179,  0.1139, -0.5957,  0.9998,  0.5097,  0.1136, -0.3416,  0.9999,\n",
      "         0.4674,  0.1080, -0.6062,  0.9998,  0.5285,  0.1572, -0.4824,  0.9998,\n",
      "         0.5126,  0.1528, -0.5601,  0.9998,  0.4578,  0.1784, -0.1062,  0.9994,\n",
      "         0.2907,  0.1766, -0.5861,  0.9994,  0.5141,  0.2621, -0.0780,  0.6122,\n",
      "         0.2593,  0.2986, -0.7694,  0.9492,  0.5335,  0.2021, -0.1376,  0.5345,\n",
      "         0.4088,  0.1804, -0.9099,  0.8983,  0.5466,  0.1808, -0.1347,  0.4453,\n",
      "         0.4234,  0.1490, -0.9695,  0.8666,  0.5246,  0.1683, -0.1044,  0.4412,\n",
      "         0.4143,  0.1414, -0.9783,  0.8695,  0.5176,  0.1745, -0.1240,  0.4275,\n",
      "         0.4078,  0.1519, -0.9157,  0.8423,  0.2555,  0.3458,  0.1691,  0.9974,\n",
      "         0.1462,  0.3531, -0.1683,  0.9993,  0.3602,  0.4711,  0.4051,  0.8117,\n",
      "         0.2807,  0.4874, -0.4385,  0.9933,  0.3614,  0.5991,  0.7686,  0.8045,\n",
      "         0.2449,  0.6471, -0.4587,  0.9916,  0.3303,  0.6133,  0.7975,  0.7466,\n",
      "         0.2063,  0.6683, -0.4737,  0.9752,  0.4970,  0.6415,  0.7502,  0.8333,\n",
      "         0.3455,  0.7011, -0.6065,  0.9849])\n",
      "실제 타겟 데이터: tensor([0., 0., 0., 0., 0., 0., 0., 1.])\n",
      "예측된 데이터: tensor(5)\n",
      "예측 확률: ['0.0000', '0.0002', '0.0000', '0.0020', '0.0009', '0.5577', '0.0000', '0.4391']\n",
      "\n",
      "샘플 2:\n",
      "입력 데이터: tensor([ 0.5499,  0.2291, -0.3259,  0.9998,  0.5575,  0.2077, -0.3697,  0.9999,\n",
      "         0.5648,  0.2054, -0.3696,  0.9999,  0.5728,  0.2030, -0.3697,  0.9999,\n",
      "         0.5484,  0.2089, -0.3020,  0.9998,  0.5491,  0.2074, -0.3021,  0.9998,\n",
      "         0.5502,  0.2059, -0.3024,  0.9998,  0.6288,  0.1964, -0.4264,  0.9997,\n",
      "         0.5995,  0.2001, -0.1107,  0.9998,  0.5927,  0.2401, -0.3323,  0.9999,\n",
      "         0.5765,  0.2409, -0.2421,  0.9997,  0.8365,  0.2589, -0.4538,  0.9997,\n",
      "         0.6386,  0.2805,  0.0945,  0.9990,  0.8238,  0.3820, -0.7410,  0.9508,\n",
      "         0.5990,  0.3541,  0.1990,  0.6322,  0.6925,  0.2612, -0.9168,  0.9468,\n",
      "         0.5341,  0.2994,  0.1211,  0.6163,  0.6582,  0.2527, -0.9700,  0.9276,\n",
      "         0.5201,  0.2857,  0.1049,  0.5275,  0.6703,  0.2299, -0.9650,  0.9250,\n",
      "         0.5239,  0.2837,  0.1042,  0.4990,  0.6784,  0.2375, -0.9160,  0.9000,\n",
      "         0.5357,  0.2895,  0.1125,  0.4947,  0.9468,  0.5161, -0.2126,  0.9997,\n",
      "         0.8183,  0.5021,  0.2132,  0.9990,  0.8129,  0.6891, -0.2794,  0.9978,\n",
      "         0.6170,  0.5747,  0.3161,  0.8997,  0.8621,  0.8512, -0.2294,  0.9954,\n",
      "         0.6763,  0.7112,  0.6433,  0.8250,  0.9037,  0.8719, -0.2327,  0.9739,\n",
      "         0.7127,  0.7416,  0.6699,  0.7979,  0.7428,  0.9223, -0.3907,  0.9905,\n",
      "         0.5329,  0.7433,  0.6825,  0.8773])\n",
      "실제 타겟 데이터: tensor([0., 0., 0., 0., 0., 1., 0., 0.])\n",
      "예측된 데이터: tensor(5)\n",
      "예측 확률: ['0.0000', '0.0000', '0.0000', '0.0000', '0.0000', '1.0000', '0.0000', '0.0000']\n",
      "\n",
      "샘플 3:\n",
      "입력 데이터: tensor([ 0.5272,  0.4572, -1.0172,  1.0000,  0.5359,  0.4467, -1.0073,  1.0000,\n",
      "         0.5445,  0.4452, -1.0072,  1.0000,  0.5517,  0.4432, -1.0075,  1.0000,\n",
      "         0.5157,  0.4474, -0.9979,  1.0000,  0.5103,  0.4467, -0.9980,  1.0000,\n",
      "         0.5050,  0.4458, -0.9981,  1.0000,  0.5666,  0.4360, -0.8282,  1.0000,\n",
      "         0.4963,  0.4415, -0.7820,  1.0000,  0.5438,  0.4631, -0.9388,  1.0000,\n",
      "         0.5194,  0.4638, -0.9269,  1.0000,  0.6343,  0.4686, -0.6059,  0.9999,\n",
      "         0.4602,  0.4714, -0.5746,  1.0000,  0.7014,  0.5435, -0.8667,  0.9857,\n",
      "         0.3870,  0.5400, -0.8559,  0.9983,  0.6480,  0.4944, -1.2266,  0.9693,\n",
      "         0.4229,  0.4948, -1.2282,  0.9967,  0.6404,  0.4812, -1.2956,  0.9262,\n",
      "         0.4165,  0.4791, -1.2984,  0.9862,  0.6294,  0.4726, -1.2616,  0.9130,\n",
      "         0.4271,  0.4741, -1.2577,  0.9814,  0.6271,  0.4759, -1.2194,  0.8264,\n",
      "         0.4356,  0.4796, -1.2180,  0.9603,  0.5961,  0.5338,  0.0030,  0.9998,\n",
      "         0.5041,  0.5338, -0.0029,  0.9999,  0.6190,  0.5899, -0.6279,  0.9786,\n",
      "         0.4629,  0.5850, -0.5754,  0.9866,  0.5835,  0.6595, -0.1207,  0.9701,\n",
      "         0.4736,  0.6583, -0.0975,  0.9943,  0.5698,  0.6670, -0.0853,  0.9297,\n",
      "         0.4868,  0.6679, -0.0646,  0.9497,  0.5945,  0.6962, -0.3099,  0.9662,\n",
      "         0.4501,  0.6926, -0.3060,  0.9874])\n",
      "실제 타겟 데이터: tensor([1., 0., 0., 0., 0., 0., 0., 0.])\n",
      "예측된 데이터: tensor(0)\n",
      "예측 확률: ['0.9961', '0.0000', '0.0029', '0.0000', '0.0000', '0.0000', '0.0010', '0.0000']\n",
      "\n",
      "샘플 4:\n",
      "입력 데이터: tensor([ 0.5720,  0.2109, -0.2405,  0.9999,  0.5782,  0.1899, -0.2899,  1.0000,\n",
      "         0.5844,  0.1881, -0.2898,  1.0000,  0.5911,  0.1863, -0.2898,  1.0000,\n",
      "         0.5708,  0.1905, -0.2209,  0.9999,  0.5714,  0.1892, -0.2210,  0.9999,\n",
      "         0.5721,  0.1878, -0.2211,  0.9999,  0.6465,  0.1825, -0.3645,  0.9999,\n",
      "         0.6206,  0.1828, -0.0619,  0.9999,  0.6034,  0.2234, -0.2607,  0.9999,\n",
      "         0.5928,  0.2250, -0.1719,  0.9999,  0.7858,  0.2701, -0.4507,  0.9999,\n",
      "         0.6465,  0.2671,  0.1447,  0.9988,  0.6323,  0.3725, -0.5100,  0.9729,\n",
      "         0.5053,  0.3497,  0.1208,  0.6522,  0.5697,  0.2626, -0.2775,  0.9641,\n",
      "         0.5133,  0.2763, -0.0294,  0.5369,  0.5542,  0.2355, -0.2954,  0.9433,\n",
      "         0.5069,  0.2555, -0.0482,  0.4394,  0.5891,  0.2273, -0.3243,  0.9420,\n",
      "         0.5333,  0.2578, -0.0210,  0.4121,  0.6004,  0.2356, -0.2807,  0.9271,\n",
      "         0.5445,  0.2649, -0.0263,  0.4048,  0.9365,  0.5083, -0.2054,  0.9999,\n",
      "         0.8191,  0.4932,  0.2058,  0.9997,  0.7980,  0.6799, -0.3007,  0.9984,\n",
      "         0.6122,  0.5738,  0.2490,  0.8704,  0.8583,  0.8500, -0.3297,  0.9953,\n",
      "         0.6777,  0.7139,  0.4345,  0.7865,  0.9037,  0.8732, -0.3403,  0.9781,\n",
      "         0.7144,  0.7485,  0.4453,  0.7360,  0.7359,  0.9156, -0.5023,  0.9903,\n",
      "         0.5253,  0.7411,  0.4378,  0.8490])\n",
      "실제 타겟 데이터: tensor([0., 0., 0., 0., 0., 1., 0., 0.])\n",
      "예측된 데이터: tensor(5)\n",
      "예측 확률: ['0.0000', '0.0000', '0.0000', '0.0000', '0.0000', '1.0000', '0.0000', '0.0000']\n",
      "\n",
      "샘플 5:\n",
      "입력 데이터: tensor([ 0.5789,  0.4599, -0.1788,  0.9998,  0.5945,  0.4486, -0.1733,  0.9998,\n",
      "         0.5943,  0.4471, -0.1735,  0.9998,  0.5941,  0.4453, -0.1735,  0.9998,\n",
      "         0.5901,  0.4489, -0.2393,  0.9998,  0.5863,  0.4476, -0.2395,  0.9998,\n",
      "         0.5823,  0.4461, -0.2395,  0.9999,  0.5619,  0.4333, -0.0808,  0.9998,\n",
      "         0.5471,  0.4332, -0.3844,  0.9998,  0.5528,  0.4616, -0.1356,  0.9998,\n",
      "         0.5467,  0.4611, -0.2210,  0.9998,  0.4700,  0.4159,  0.1118,  0.9993,\n",
      "         0.3812,  0.4530, -0.4441,  0.9997,  0.4135,  0.4788,  0.2464,  0.2168,\n",
      "         0.2621,  0.5291, -0.2753,  0.9179,  0.4399,  0.4958,  0.1395,  0.3382,\n",
      "         0.4261,  0.5113,  0.1164,  0.8376,  0.4660,  0.4911,  0.1275,  0.3494,\n",
      "         0.4462,  0.5024,  0.1211,  0.8099,  0.4668,  0.4868,  0.0992,  0.3594,\n",
      "         0.4523,  0.4942,  0.0872,  0.8188,  0.4559,  0.4853,  0.1231,  0.3561,\n",
      "         0.4453,  0.4952,  0.1150,  0.8070,  0.2087,  0.4633,  0.1943,  0.9991,\n",
      "         0.1430,  0.4766, -0.1934,  0.9998,  0.4113,  0.4965,  0.4392,  0.6923,\n",
      "         0.3608,  0.5365, -0.3888,  0.9924,  0.2715,  0.5615,  0.6481,  0.7570,\n",
      "         0.2625,  0.6458, -0.3202,  0.9892,  0.2315,  0.5590,  0.6666,  0.7192,\n",
      "         0.2183,  0.6599, -0.3134,  0.9761,  0.3025,  0.5971,  0.6566,  0.8069,\n",
      "         0.3328,  0.6947, -0.3731,  0.9853])\n",
      "실제 타겟 데이터: tensor([0., 0., 0., 0., 0., 0., 1., 0.])\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_64197/3174289499.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": 41,
   "id": "7d1e3234",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "정확도: 0.642\n",
      "정밀도: 0.734\n",
      "재현율: 0.642\n",
      "F1 점수: 0.685\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": 42,
   "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": 59,
   "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.BCELoss()\n",
    "deadlift_criterion = nn.CrossEntropyLoss()\n",
    "deadlift_optimizer = optim.RMSprop(bench_model.parameters(), lr=0.001)\n",
    "#deadlift_optimizer = optim.Adam(deadlift_model.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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": 66,
   "id": "f733762f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Loss: 2.079949140548706\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": 67,
   "id": "262be563",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "샘플 1:\n",
      "입력 데이터: tensor([ 6.3179e-01,  4.6372e-01, -4.1330e-01,  9.9993e-01,  6.4140e-01,\n",
      "         4.5339e-01, -3.9553e-01,  9.9989e-01,  6.4191e-01,  4.5235e-01,\n",
      "        -3.9565e-01,  9.9986e-01,  6.4243e-01,  4.5129e-01, -3.9585e-01,\n",
      "         9.9990e-01,  6.3520e-01,  4.5343e-01, -4.4887e-01,  9.9993e-01,\n",
      "         6.3130e-01,  4.5229e-01, -4.4899e-01,  9.9991e-01,  6.2727e-01,\n",
      "         4.5088e-01, -4.4895e-01,  9.9993e-01,  6.1652e-01,  4.4257e-01,\n",
      "        -2.8493e-01,  9.9976e-01,  5.9445e-01,  4.3815e-01, -5.2439e-01,\n",
      "         9.9985e-01,  6.1265e-01,  4.6591e-01, -3.6419e-01,  9.9993e-01,\n",
      "         6.0541e-01,  4.6467e-01, -4.3423e-01,  9.9994e-01,  5.4877e-01,\n",
      "         4.4001e-01, -1.1854e-01,  9.9990e-01,  4.5842e-01,  4.5739e-01,\n",
      "        -5.5030e-01,  9.9975e-01,  5.2044e-01,  5.1279e-01,  7.7384e-04,\n",
      "         7.3524e-01,  3.7595e-01,  5.3084e-01, -5.4821e-01,  9.8514e-01,\n",
      "         5.1044e-01,  5.7193e-01, -4.5062e-02,  8.4960e-01,  3.5241e-01,\n",
      "         6.0314e-01, -4.9337e-01,  9.8466e-01,  5.1319e-01,  5.8711e-01,\n",
      "        -4.6581e-02,  7.9976e-01,  3.4481e-01,  6.2231e-01, -5.6865e-01,\n",
      "         9.7185e-01,  5.1132e-01,  5.8914e-01, -1.1687e-01,  8.1425e-01,\n",
      "         3.5630e-01,  6.2565e-01, -5.5625e-01,  9.7163e-01,  5.0768e-01,\n",
      "         5.8326e-01, -7.3320e-02,  8.0817e-01,  3.6039e-01,  6.1800e-01,\n",
      "        -4.9242e-01,  9.5312e-01,  3.6008e-01,  4.3678e-01,  1.3628e-01,\n",
      "         9.9882e-01,  2.8832e-01,  4.4650e-01, -1.3635e-01,  9.9828e-01,\n",
      "         4.5752e-01,  5.2452e-01,  1.1872e-01,  6.4512e-01,  4.1418e-01,\n",
      "         5.3241e-01, -1.6130e-01,  8.8758e-01,  3.8004e-01,  6.0754e-01,\n",
      "         2.7811e-01,  5.6255e-01,  3.6735e-01,  6.2147e-01,  1.3288e-01,\n",
      "         6.5166e-01,  3.5488e-01,  6.2067e-01,  2.9293e-01,  4.1805e-01,\n",
      "         3.4755e-01,  6.3469e-01,  1.6614e-01,  5.2877e-01,  4.2367e-01,\n",
      "         6.2954e-01,  2.2822e-01,  5.8161e-01,  4.1325e-01,  6.5168e-01,\n",
      "         1.4348e-01,  6.6998e-01])\n",
      "실제 타겟 데이터: tensor([0., 0., 0., 0., 0., 0., 1., 0.])\n",
      "예측된 데이터: tensor(7)\n",
      "예측 확률: ['0.1450', '0.1039', '0.1236', '0.1188', '0.1117', '0.1318', '0.1172', '0.1480']\n",
      "\n",
      "샘플 2:\n",
      "입력 데이터: tensor([ 0.6188,  0.4354, -0.6611,  0.9999,  0.6241,  0.4246, -0.6254,  0.9998,\n",
      "         0.6243,  0.4239, -0.6255,  0.9998,  0.6244,  0.4232, -0.6258,  0.9998,\n",
      "         0.6187,  0.4231, -0.6874,  0.9998,  0.6147,  0.4214, -0.6876,  0.9998,\n",
      "         0.6101,  0.4195, -0.6876,  0.9998,  0.5984,  0.4147, -0.4298,  0.9996,\n",
      "         0.5695,  0.4099, -0.7080,  0.9997,  0.6055,  0.4406, -0.5816,  0.9999,\n",
      "         0.5972,  0.4383, -0.6627,  0.9999,  0.5426,  0.4478, -0.2003,  0.9999,\n",
      "         0.4435,  0.4510, -0.6889,  0.9997,  0.5298,  0.5211, -0.0504,  0.8532,\n",
      "         0.3861,  0.5413, -0.7117,  0.9864,  0.5245,  0.5877, -0.1123,  0.9036,\n",
      "         0.3835,  0.6244, -0.7012,  0.9709,  0.5169,  0.6051, -0.1191,  0.8471,\n",
      "         0.3779,  0.6415, -0.7823,  0.9512,  0.5142,  0.6052, -0.1899,  0.8614,\n",
      "         0.3974,  0.6439, -0.7834,  0.9503,  0.5126,  0.5991, -0.1418,  0.8548,\n",
      "         0.3993,  0.6368, -0.7043,  0.9205,  0.3470,  0.4935,  0.1404,  0.9984,\n",
      "         0.2724,  0.4950, -0.1401,  0.9985,  0.4897,  0.5474,  0.0089,  0.7467,\n",
      "         0.4186,  0.5497, -0.3501,  0.9868,  0.4193,  0.6651,  0.1079,  0.7617,\n",
      "         0.3743,  0.6684, -0.1773,  0.9803,  0.3932,  0.6869,  0.1139,  0.6749,\n",
      "         0.3394,  0.6842, -0.1635,  0.9270,  0.4948,  0.6972,  0.0124,  0.8105,\n",
      "         0.4491,  0.7095, -0.2805,  0.9724])\n",
      "실제 타겟 데이터: tensor([0., 0., 0., 0., 1., 0., 0., 0.])\n",
      "예측된 데이터: tensor(0)\n",
      "예측 확률: ['0.1473', '0.1042', '0.1227', '0.1196', '0.1120', '0.1315', '0.1170', '0.1456']\n",
      "\n",
      "샘플 3:\n",
      "입력 데이터: tensor([ 0.6199,  0.4695, -0.5491,  0.9994,  0.6353,  0.4579, -0.5281,  0.9994,\n",
      "         0.6364,  0.4568, -0.5284,  0.9993,  0.6376,  0.4554, -0.5287,  0.9995,\n",
      "         0.6301,  0.4571, -0.5889,  0.9995,  0.6274,  0.4552, -0.5890,  0.9993,\n",
      "         0.6246,  0.4531, -0.5890,  0.9995,  0.6156,  0.4434, -0.3913,  0.9988,\n",
      "         0.6057,  0.4408, -0.6647,  0.9988,  0.6021,  0.4714, -0.4878,  0.9997,\n",
      "         0.5964,  0.4687, -0.5677,  0.9998,  0.5604,  0.4416, -0.2036,  0.9999,\n",
      "         0.4597,  0.4375, -0.6579,  0.9995,  0.5171,  0.5156, -0.0838,  0.8196,\n",
      "         0.4057,  0.5280, -0.6962,  0.9899,  0.4989,  0.5873, -0.1471,  0.8648,\n",
      "         0.4056,  0.6101, -0.7145,  0.9845,  0.4905,  0.6038, -0.1622,  0.7789,\n",
      "         0.3985,  0.6253, -0.8023,  0.9716,  0.4888,  0.6050, -0.2309,  0.7949,\n",
      "         0.4090,  0.6297, -0.8006,  0.9713,  0.4865,  0.5996, -0.1754,  0.7865,\n",
      "         0.4136,  0.6238, -0.7170,  0.9554,  0.3849,  0.4568,  0.1255,  0.9982,\n",
      "         0.3054,  0.4460, -0.1250,  0.9983,  0.4515,  0.5390, -0.1012,  0.5768,\n",
      "         0.3851,  0.5313, -0.3708,  0.9455,  0.4039,  0.6466, -0.0400,  0.5720,\n",
      "         0.3710,  0.6529, -0.3141,  0.9577,  0.3729,  0.6590, -0.0361,  0.5802,\n",
      "         0.3513,  0.6806, -0.3117,  0.9117,  0.4609,  0.6709, -0.1803,  0.7085,\n",
      "         0.4436,  0.6816, -0.4897,  0.9597])\n",
      "실제 타겟 데이터: tensor([1., 0., 0., 0., 0., 0., 0., 0.])\n",
      "예측된 데이터: tensor(0)\n",
      "예측 확률: ['0.1471', '0.1045', '0.1228', '0.1197', '0.1120', '0.1311', '0.1169', '0.1458']\n",
      "\n",
      "샘플 4:\n",
      "입력 데이터: tensor([ 0.4958,  0.4216, -1.1926,  1.0000,  0.5128,  0.4069, -1.1847,  1.0000,\n",
      "         0.5243,  0.4057, -1.1846,  1.0000,  0.5338,  0.4040, -1.1848,  1.0000,\n",
      "         0.4816,  0.4062, -1.1790,  1.0000,  0.4732,  0.4046, -1.1791,  1.0000,\n",
      "         0.4643,  0.4027, -1.1790,  1.0000,  0.5504,  0.3946, -0.9999,  1.0000,\n",
      "         0.4538,  0.3958, -0.9732,  1.0000,  0.5161,  0.4322, -1.1097,  1.0000,\n",
      "         0.4798,  0.4318, -1.1018,  1.0000,  0.6434,  0.4527, -0.7644,  1.0000,\n",
      "         0.3814,  0.4474, -0.7088,  0.9999,  0.6719,  0.5678, -0.7572,  0.9900,\n",
      "         0.3334,  0.5636, -0.6841,  0.9807,  0.6792,  0.6624, -0.8360,  0.9802,\n",
      "         0.3196,  0.6584, -0.7608,  0.9313,  0.6805,  0.6910, -0.8883,  0.9511,\n",
      "         0.3091,  0.6860, -0.8071,  0.8534,  0.6545,  0.6854, -0.9264,  0.9565,\n",
      "         0.3298,  0.6841, -0.8560,  0.8703,  0.6491,  0.6767, -0.8525,  0.9546,\n",
      "         0.3393,  0.6748, -0.7835,  0.8860,  0.5826,  0.5193, -0.0071,  1.0000,\n",
      "         0.4498,  0.5160,  0.0069,  0.9999,  0.6220,  0.6103, -0.6194,  0.9187,\n",
      "         0.3731,  0.5969, -0.5374,  0.9282,  0.5843,  0.7523, -0.2886,  0.9400,\n",
      "         0.3713,  0.7438, -0.2321,  0.9520,  0.5611,  0.7730, -0.2633,  0.7572,\n",
      "         0.3961,  0.7665, -0.2158,  0.8285,  0.6083,  0.8018, -0.5498,  0.9289,\n",
      "         0.3093,  0.7887, -0.4825,  0.9463])\n",
      "실제 타겟 데이터: tensor([0., 0., 0., 0., 1., 0., 0., 0.])\n",
      "예측된 데이터: tensor(0)\n",
      "예측 확률: ['0.1494', '0.1021', '0.1228', '0.1224', '0.1128', '0.1297', '0.1179', '0.1429']\n",
      "\n",
      "샘플 5:\n",
      "입력 데이터: tensor([ 0.6518,  0.2546,  0.7450,  0.9991,  0.6472,  0.2451,  0.6968,  0.9989,\n",
      "         0.6448,  0.2450,  0.6968,  0.9991,  0.6423,  0.2450,  0.6968,  0.9990,\n",
      "         0.6571,  0.2431,  0.7252,  0.9985,  0.6620,  0.2415,  0.7252,  0.9986,\n",
      "         0.6670,  0.2400,  0.7250,  0.9983,  0.6493,  0.2446,  0.4700,  0.9989,\n",
      "         0.6804,  0.2396,  0.5966,  0.9976,  0.6546,  0.2603,  0.6672,  0.9982,\n",
      "         0.6662,  0.2585,  0.7038,  0.9976,  0.6442,  0.2744,  0.2229,  1.0000,\n",
      "         0.7874,  0.2670,  0.5239,  0.9987,  0.6218,  0.3537,  0.1367,  0.9904,\n",
      "         0.8186,  0.3394,  0.4814,  0.3437,  0.5974,  0.4331,  0.2263,  0.9380,\n",
      "         0.7596,  0.4090,  0.4432,  0.1141,  0.5919,  0.4559,  0.1983,  0.8903,\n",
      "         0.7565,  0.4236,  0.4462,  0.0984,  0.5960,  0.4557,  0.2172,  0.8826,\n",
      "         0.7396,  0.4264,  0.4325,  0.0924,  0.6018,  0.4472,  0.2364,  0.8233,\n",
      "         0.7385,  0.4197,  0.4350,  0.0907,  0.7610,  0.4005, -0.0989,  0.9998,\n",
      "         0.8558,  0.3942,  0.0983,  0.9993,  0.7091,  0.5186, -0.0759,  0.9933,\n",
      "         0.8154,  0.5115,  0.1612,  0.9504,  0.6903,  0.6361, -0.1404,  0.9869,\n",
      "         0.8116,  0.6231,  0.1010,  0.9418,  0.7114,  0.6607, -0.1391,  0.9873,\n",
      "         0.8195,  0.6476,  0.0896,  0.9413,  0.6150,  0.6417, -0.2064,  0.8875,\n",
      "         0.7630,  0.6288,  0.0179,  0.6701])\n",
      "실제 타겟 데이터: tensor([0., 0., 0., 0., 0., 0., 0., 1.])\n",
      "예측된 데이터: tensor(7)\n",
      "예측 확률: ['0.1428', '0.1046', '0.1266', '0.1169', '0.1108', '0.1307', '0.1145', '0.1531']\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/23/n8c37q85651bywf1rz00z90c0000gn/T/ipykernel_64197/2186968047.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": 68,
   "id": "11299320",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "정확도: 0.000\n",
      "정밀도: 0.000\n",
      "재현율: 0.000\n",
      "F1 점수: 0.000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/min_leon/anaconda3/envs/pytorch_env/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1471: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\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": 69,
   "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": 457,
   "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": 441,
   "id": "eca7f824",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([239, 132])"
      ]
     },
     "execution_count": 441,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bench_X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 442,
   "id": "ffeba6c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([232, 132])"
      ]
     },
     "execution_count": 442,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "squat_X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "id": "923f2a22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([285, 132])"
      ]
     },
     "execution_count": 443,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deadlift_X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc644d48",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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