From a367ed7abf6e320ea4a14122f6016c10a8339143 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Pawe=C5=82=20Sk=C3=B3rzewski?= <pawel.skorzewski@amu.edu.pl>
Date: Thu, 11 Apr 2024 10:45:55 +0200
Subject: [PATCH] Uaktualnienie lab. 5

---
 lab/05_Ewaluacja.ipynb | 39 ++++++++++++---------------------------
 1 file changed, 12 insertions(+), 27 deletions(-)

diff --git a/lab/05_Ewaluacja.ipynb b/lab/05_Ewaluacja.ipynb
index f2fc458..aa73f83 100644
--- a/lab/05_Ewaluacja.ipynb
+++ b/lab/05_Ewaluacja.ipynb
@@ -44,23 +44,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[332187.32537534]\n",
-      " [369587.77676738]\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"
+      "[279661.8663101  279261.14658016 522543.09697553 243798.45172733\n",
+      " 408919.21577439 272940.5507781  367515.38801642 592972.56867895\n",
+      " 418509.89826131 943578.7139463 ]\n"
      ]
     }
    ],
@@ -84,7 +77,7 @@
     "def preprocess(data):\n",
     "    \"\"\"Wstępne przetworzenie danych\"\"\"\n",
     "    data = data.replace({\"parter\": 0, \"poddasze\": 0}, regex=True)\n",
-    "    data = data.applymap(np.nan_to_num)  # Zamienia \"NaN\" na liczby\n",
+    "    data = data.map(np.nan_to_num)  # Zamienia \"NaN\" na liczby\n",
     "    return data\n",
     "\n",
     "\n",
@@ -101,7 +94,7 @@
     "data_train, data_test = train_test_split(data, test_size=0.2)\n",
     "\n",
     "# Uczenie modelu\n",
-    "y_train = pd.DataFrame(data_train[\"cena\"])\n",
+    "y_train = pd.Series(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",
@@ -154,14 +147,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Błąd średniokwadratowy wynosi 1179760250402.185\n"
+      "Błąd średniokwadratowy wynosi 137394744518.31197\n"
      ]
     }
    ],
@@ -182,14 +175,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "-10.712011261173265\n"
+      "0.2160821272059249\n"
      ]
     }
    ],
@@ -213,7 +206,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -225,14 +218,6 @@
       "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": [
@@ -254,7 +239,7 @@
     "data_train, data_test = train_test_split(data_iris, test_size=0.2)\n",
     "\n",
     "# Uczenie modelu\n",
-    "y_train = pd.DataFrame(data_train[\"Iris setosa?\"])\n",
+    "y_train = pd.Series(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",