оценки

lab_3/lab_3.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -15,7 +15,7 @@
        "      dtype='object')"
       ]
      },
-     "execution_count": 106,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -99,7 +99,7 @@
        "dtype: bool"
       ]
      },
-     "execution_count": 107,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -131,7 +131,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -212,7 +212,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -247,7 +247,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -319,7 +319,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -366,7 +366,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -403,7 +403,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -776,7 +776,7 @@
        "[2000 rows x 26 columns]"
       ]
      },
-     "execution_count": 113,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -798,7 +798,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -820,14 +820,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "d:\\Study\\3 курс 5 семестр\\AIM\\AIM-PIbd-31-Yakovlev-M-G\\kernel\\Lib\\site-packages\\featuretools\\synthesis\\deep_feature_synthesis.py:169: UserWarning: Only one dataframe in entityset, changing max_depth to 1 since deeper features cannot be created\n",
+      "d:\\Study\\AIM-PIbd-31-Yakovlev-M-G\\kernel\\Lib\\site-packages\\featuretools\\synthesis\\deep_feature_synthesis.py:169: UserWarning: Only one dataframe in entityset, changing max_depth to 1 since deeper features cannot be created\n",
       "  warnings.warn(\n"
      ]
     },
@@ -1243,7 +1243,7 @@
        "[6362 rows x 28 columns]"
       ]
      },
-     "execution_count": 115,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1265,6 +1265,77 @@
     "\n",
     "feature_matrix"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Оценка качества"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Время обучения: 0.27 секунд\n",
+      "MSE: 2205809457.9675403\n",
+      "RMSE: 46966.04579872081\n",
+      "R²: 0.9623494755346685\n",
+      "MAE: 35440.813340503635 \n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "d:\\Study\\AIM-PIbd-31-Yakovlev-M-G\\kernel\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "import time\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "from sklearn.metrics import r2_score, mean_absolute_error\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "\n",
+    "X = feature_matrix.drop('price', axis=1)\n",
+    "y = feature_matrix['price']\n",
+    "\n",
+    "#Делим на выборки\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "#Начнем обучение\n",
+    "model = LinearRegression()\n",
+    "start_time = time.time()\n",
+    "model.fit(X_train, y_train)\n",
+    "\n",
+    "train_time = time.time() - start_time\n",
+    "\n",
+    "#Вычесляем показательную способность\n",
+    "y_pred = model.predict(X_test)\n",
+    "mse = mean_squared_error(y_test, y_pred)\n",
+    "rmse = mean_squared_error(y_test, y_pred, squared=False)\n",
+    "r2 = r2_score(y_test, y_pred)\n",
+    "mae = mean_absolute_error(y_test, y_pred)\n",
+    "\n",
+    "print()\n",
+    "print(f\"Время обучения: {train_time:.2f} секунд\")\n",
+    "print(\"Метрики:\")\n",
+    "print(f\"MSE: {mse}\")\n",
+    "print(f\"RMSE: {rmse}\")\n",
+    "print(f\"R²: {r2}\")\n",
+    "print(f\"MAE: {mae} \\n\")"
+   ]
   }
  ],
  "metadata": {