lab/05_Ewaluacja.ipynb

@@ -44,16 +44,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[279661.8663101  279261.14658016 522543.09697553 243798.45172733\n",
+      "[[332187.32537534]\n",
-      " 408919.21577439 272940.5507781  367515.38801642 592972.56867895\n",
+      " [369587.77676738]\n",
-      " 418509.89826131 943578.7139463 ]\n"
+      " [488428.70420785]\n",
+      " [300013.00301966]\n",
+      " [412118.79730411]\n",
+      " [283333.7605634 ]\n",
+      " [275209.84706017]\n",
+      " [361970.50784352]\n",
+      " [272402.36116539]\n",
+      " [328635.55642844]]\n"
      ]
     }
    ],
@@ -77,7 +84,7 @@
     "def preprocess(data):\n",
     "    \"\"\"Wstępne przetworzenie danych\"\"\"\n",
     "    data = data.replace({\"parter\": 0, \"poddasze\": 0}, regex=True)\n",
-    "    data = data.map(np.nan_to_num)  # Zamienia \"NaN\" na liczby\n",
+    "    data = data.applymap(np.nan_to_num)  # Zamienia \"NaN\" na liczby\n",
     "    return data\n",
     "\n",
     "\n",
@@ -94,7 +101,7 @@
     "data_train, data_test = train_test_split(data, test_size=0.2)\n",
     "\n",
     "# Uczenie modelu\n",
-    "y_train = pd.Series(data_train[\"cena\"])\n",
+    "y_train = pd.DataFrame(data_train[\"cena\"])\n",
     "x_train = pd.DataFrame(data_train[FEATURES])\n",
     "model = LinearRegression()  # definicja modelu\n",
     "model.fit(x_train, y_train)  # dopasowanie modelu\n",
@@ -147,14 +154,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Błąd średniokwadratowy wynosi 137394744518.31197\n"
+      "Błąd średniokwadratowy wynosi 1179760250402.185\n"
      ]
     }
    ],
@@ -175,14 +182,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.2160821272059249\n"
+      "-10.712011261173265\n"
      ]
     }
    ],
@@ -206,7 +213,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -218,6 +225,14 @@
       "F-score: 1.0\n",
       "Model score: 1.0\n"
      ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/pawel/.local/lib/python3.10/site-packages/sklearn/utils/validation.py:1111: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
+      "  y = column_or_1d(y, warn=True)\n"
+     ]
     }
    ],
    "source": [
@@ -239,7 +254,7 @@
     "data_train, data_test = train_test_split(data_iris, test_size=0.2)\n",
     "\n",
     "# Uczenie modelu\n",
-    "y_train = pd.Series(data_train[\"Iris setosa?\"])\n",
+    "y_train = pd.DataFrame(data_train[\"Iris setosa?\"])\n",
     "x_train = pd.DataFrame(data_train[FEATURES])\n",
     "model = LogisticRegression()  # definicja modelu\n",
     "model.fit(x_train, y_train)  # dopasowanie modelu\n",

lab/CNN_PyTorch.ipynb

@@ -714,7 +714,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Proces wczytywania danych i przetwarzania ich przez sieć ułatwiają klasy `Dataset` i `DataLoader`: https://pytorch.org/tutorials/beginner/basics/data_tutorial.html"
+    "Tutaj artykuł o tym, jak stworzyć dataloader dla danych z własnego pliku CSV: https://androidkt.com/load-pandas-dataframe-using-dataset-and-dataloader-in-pytorch"
    ]
   }
  ],
@@ -735,7 +735,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.3"
+   "version": "3.10.12"
   },
   "livereveal": {
    "start_slideshow_at": "selected",