pushibus

project-cancer-classification-pca-nn.ipynb

@@ -242,6 +242,7 @@
     "import torch.nn as nn\n",
     "import torch.optim as optim\n",
     "import torch.nn.functional as F\n",
+    "from sklearn.metrics import confusion_matrix\n",
     "\n",
     "class CancerClassifierNN(nn.Module):\n",
     "    \"\"\"\n",
@@ -297,7 +298,31 @@
     "        x = F.relu(self.fc3(x))\n",
     "        x = self.dropout(x)\n",
     "        x = torch.softmax(self.fc4(x), dim=1)  # Oder F.log_softmax(x, dim=1) für Mehrklassenklassifikation\n",
-    "        return x"
+    "        return x\n",
+    "    \n",
+    "    def calculate_confusion_matrix(self, model, dataset_loader):\n",
+    "        \"\"\"\n",
+    "        Berechnet die Konfusionsmatrix für das gegebene Modell und den Datensatz.\n",
+    "\n",
+    "        Parameters:\n",
+    "            model (torch.nn.Module): Das PyTorch-Modell.\n",
+    "            dataset_loader (torch.utils.data.DataLoader): Der DataLoader für den Datensatz.\n",
+    "\n",
+    "        Returns:\n",
+    "            np.array: Die Konfusionsmatrix.\n",
+    "        \"\"\"\n",
+    "        model.eval()\n",
+    "        all_preds = []\n",
+    "        all_targets = []\n",
+    "\n",
+    "        with torch.no_grad():\n",
+    "            for data, target in dataset_loader:\n",
+    "                outputs = model(data)\n",
+    "                _, preds = torch.max(outputs, 1)\n",
+    "                all_preds.extend(preds.cpu().numpy())\n",
+    "                all_targets.extend(target.cpu().numpy())\n",
+    "\n",
+    "        return confusion_matrix(all_targets, all_preds)"
    ]
   },
   {