Recognizer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "id": "04ed93d5-f767-4390-af9e-e3d0f2539514",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-20T16:13:58.057246Z",
     "start_time": "2024-07-20T16:13:56.578189Z"
    }
   },
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import joblib\n",
    "import torch\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import DataLoader, TensorDataset\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from lib.ANN.BinaryMLP import BinaryMLP\n",
    "from tqdm import tqdm"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "cell_type": "code",
   "id": "17d05801-081e-4ddc-8b2d-bf9b40688729",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-20T16:13:58.090569Z",
     "start_time": "2024-07-20T16:13:58.058198Z"
    }
   },
   "source": [
    "data: pd.DataFrame = pd.read_csv(\"./dataset/ClaMP/ClaMP_Raw-5184.csv\")\n",
    "data = data.drop('e_res', axis=1)\n",
    "data = data.drop('e_res2', axis=1)\n",
    "data = data.drop('BaseOfData', axis=1)\n",
    "\n",
    "X: np.ndarray = data.iloc[:, :52].values\n",
    "y: np.ndarray = data.iloc[:, 52].values"
   ],
   "outputs": [],
   "execution_count": 2
  },
  {
   "cell_type": "code",
   "id": "e191da88-fe1e-4834-a574-a4980b22cd50",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-20T16:13:58.198431Z",
     "start_time": "2024-07-20T16:13:58.091867Z"
    }
   },
   "source": [
    "class_count = data['class'].value_counts()\n",
    "plt.bar(class_count.index, class_count)     \n",
    "plt.xticks(class_count.index)               \n",
    "plt.show()  "
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 3
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a36a2397-abce-4a2a-88fa-6576c1665bf5",
   "metadata": {},
   "source": [
    "### Generazione del seed\n",
    "\n",
    "Per assicurare che i risultati dell'addestramento siano riproducibili, viene impostato un valore specifico come seed"
   ]
  },
  {
   "cell_type": "code",
   "id": "9e4decaa-7412-474e-8b8f-aecc8f3f30f5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-20T16:13:58.204841Z",
     "start_time": "2024-07-20T16:13:58.199683Z"
    }
   },
   "source": [
    "seed = 32\n",
    "torch.manual_seed(seed)\n",
    "np.random.seed(seed)"
   ],
   "outputs": [],
   "execution_count": 4
  },
  {
   "cell_type": "code",
   "id": "84f64c19-3b1d-429d-a3e1-b0c04158320c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-20T16:13:58.216221Z",
     "start_time": "2024-07-20T16:13:58.206093Z"
    }
   },
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=seed, stratify=y)"
   ],
   "outputs": [],
   "execution_count": 5
  },
  {
   "cell_type": "code",
   "id": "ee98e10e-9546-4136-8fb3-78d34cc7a9f6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-20T16:13:58.225578Z",
     "start_time": "2024-07-20T16:13:58.217645Z"
    }
   },
   "source": [
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)"
   ],
   "outputs": [],
   "execution_count": 6
  },
  {
   "cell_type": "code",
   "id": "fe74dffb-af81-4e87-a652-46387f562ca1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-20T16:13:58.247285Z",
     "start_time": "2024-07-20T16:13:58.227440Z"
    }
   },
   "source": [
    "X_train = torch.tensor(X_train, dtype=torch.float32)\n",
    "X_test = torch.tensor(X_test, dtype=torch.float32)\n",
    "y_train = torch.tensor(y_train, dtype=torch.float32)\n",
    "y_test = torch.tensor(y_test, dtype=torch.float32)\n",
    "\n",
    "train_dataset = TensorDataset(X_train, y_train)\n",
    "test_dataset = TensorDataset(X_test, y_test)\n",
    "\n",
    "train_loader = DataLoader(train_dataset, batch_size=256, shuffle=True)\n",
    "test_loader = DataLoader(test_dataset, batch_size=256, shuffle=False)"
   ],
   "outputs": [],
   "execution_count": 7
  },
  {
   "cell_type": "code",
   "id": "439852b5-5866-4dc8-815d-89eda377688e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-20T16:13:59.007430Z",
     "start_time": "2024-07-20T16:13:58.248432Z"
    }
   },
   "source": [
    "model = BinaryMLP()\n",
    "# Use BCEWithLogitsLoss which applies sigmoid internally\n",
    "loss_fn = torch.nn.BCEWithLogitsLoss()\n",
    "optimizer = optim.Adamax(model.parameters(), lr=0.01)"
   ],
   "outputs": [],
   "execution_count": 8
  },
  {
   "cell_type": "code",
   "id": "c06c77e7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-20T16:32:46.779725Z",
     "start_time": "2024-07-20T16:13:59.008528Z"
    }
   },
   "source": [
    "num_epochs = 10000\n",
    "loss_hist = [0] * num_epochs\n",
    "accuracy_hist = [0] * num_epochs\n",
    "\n",
    "for epoch in tqdm(range(num_epochs)):\n",
    "\n",
    "    model.train()\n",
    "    total_loss = 0\n",
    "    correct_predictions = 0\n",
    "    total_samples = 0\n",
    "\n",
    "    for inputs, labels in train_loader:\n",
    "        # Forward pass\n",
    "        pred = model(inputs)\n",
    "        loss = loss_fn(pred, labels.unsqueeze(1))  # To ensure labels match pred shape\n",
    "        \n",
    "        # Backward pass and optimization\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        optimizer.zero_grad()\n",
    "        \n",
    "        # Calculate total loss\n",
    "        total_loss += loss.item() * labels.size(0)\n",
    "\n",
    "        # Calculate total accuracy\n",
    "        with torch.no_grad():\n",
    "            prediction = torch.sigmoid(pred)\n",
    "            prediction = (prediction >= 0.5).float()\n",
    "            correct_predictions += (prediction == labels.unsqueeze(1)).sum().item()\n",
    "            total_samples += labels.size(0)\n",
    "    \n",
    "    # Calculate average loss and accuracy for the epoch\n",
    "    loss_hist[epoch] = total_loss / total_samples\n",
    "    accuracy_hist[epoch] = correct_predictions / total_samples"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [18:47<00:00,  8.87it/s]  \n"
     ]
    }
   ],
   "execution_count": 9
  },
  {
   "cell_type": "code",
   "id": "e83e7c70-43f5-4830-8779-463eedcd0f1b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-20T16:32:46.926706Z",
     "start_time": "2024-07-20T16:32:46.782799Z"
    }
   },
   "source": [
    "fig, ax = plt.subplots(figsize=(6, 5))\n",
    "ax.plot(loss_hist, lw=3)\n",
    "ax.set_xlabel('Epoch', fontsize=15)\n",
    "ax.tick_params(axis='both', which='major', labelsize=15)\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 10
  },
  {
   "cell_type": "code",
   "id": "d66fdabe-ea7a-4b66-a14d-ab917bfd0b5b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-20T16:32:47.097962Z",
     "start_time": "2024-07-20T16:32:46.929961Z"
    }
   },
   "source": [
    "fig, ax = plt.subplots(figsize=(6, 5))\n",
    "ax.plot(accuracy_hist, lw=3)\n",
    "ax.set_xlabel('Epoch', fontsize=15)\n",
    "ax.tick_params(axis='both', which='major', labelsize=15)\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 11
  },
  {
   "cell_type": "code",
   "id": "6655610b-14fa-4bb5-b13c-02d54b922999",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-20T16:32:47.134432Z",
     "start_time": "2024-07-20T16:32:47.099517Z"
    }
   },
   "source": [
    "model.eval()\n",
    "with torch.no_grad():\n",
    "    \n",
    "    TP = 0\n",
    "    TN = 0  \n",
    "    FP = 0 \n",
    "    FN = 0 \n",
    "   \n",
    "    for inputs, labels in test_loader:\n",
    "        outputs = model(inputs)\n",
    "        outputs = torch.sigmoid(outputs)\n",
    "\n",
    "        for label, output in zip(labels, outputs.data):\n",
    "            prediction = np.where(output >= 0.5, 1,0)\n",
    "            \n",
    "            if prediction == 1 and label == 1:\n",
    "                TP += 1\n",
    "            elif prediction == 0 and label == 1:\n",
    "                FN += 1\n",
    "            elif prediction == 1 and label == 0:\n",
    "                FP += 1\n",
    "            else:\n",
    "                TN += 1\n"
   ],
   "outputs": [],
   "execution_count": 12
  },
  {
   "cell_type": "code",
   "id": "f6d6a8f0-c8d2-45f7-af4f-d0bde4c3c0e8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-20T16:32:47.141332Z",
     "start_time": "2024-07-20T16:32:47.135899Z"
    }
   },
   "source": [
    "accuracy = (TP + TN) / (TP + TN + FP + FN) * 100\n",
    "precision = TP / (TP + FP) * 100 if (TP + FP) != 0 else 0\n",
    "recall = TP / (TP + FN) * 100 if (TP + FN) != 0 else 0\n",
    "accuracy = round(accuracy,2)\n",
    "precision = round(precision,2)\n",
    "recall = round(recall,2)\n",
    "print(f'Accuracy on the test set: {accuracy}%')\n",
    "print(f'Precision on the test set: {precision}%')\n",
    "print(f'Recall on the test set: {recall}%')"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on the test set: 96.82%\n",
      "Precision on the test set: 97.01%\n",
      "Recall on the test set: 96.83%\n"
     ]
    }
   ],
   "execution_count": 13
  },
  {
   "cell_type": "code",
   "id": "6c313a66-d229-4365-b1a2-34ad0b0673a2",
   "metadata": {
    "scrolled": true,
    "ExecuteTime": {
     "end_time": "2024-07-20T16:32:47.230897Z",
     "start_time": "2024-07-20T16:32:47.142566Z"
    }
   },
   "source": [
    "confusion_mat = [[TN, FP],\n",
    "                 [FN, TP]]\n",
    "\n",
    "labels = ['Positive', 'Negative']\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(confusion_mat, annot=True, fmt=\"d\", cmap='Blues', cbar=False)\n",
    "plt.xlabel('Predicted Label')\n",
    "plt.ylabel('True Label')\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 14
  },
  {
   "cell_type": "code",
   "id": "6f856b6c-5f61-4900-b752-5df804c867a5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-20T16:32:47.243172Z",
     "start_time": "2024-07-20T16:32:47.233095Z"
    }
   },
   "source": [
    "torch.save(model, \"models/recognizer/model.pt\")\n",
    "_ = joblib.dump(scaler, 'models/recognizer/scaler.joblib')"
   ],
   "outputs": [],
   "execution_count": 15
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}