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lab 4 is done
2024-12-07 10:42:57 +04:00
{
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{
"cell_type": "markdown",
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"source": [
"# Лаб работа №4\n",
"Вариант 6 - Продажа домов в округе кинг\n",
"\n",
"Задача регрессии заключается в предсказании цен на недвижимость, что поможет риэлторам и аналитикам оценить справедливую рыночную стоимость объектов.\n",
"\n",
"Задача классификации предполагает определение вероятности того, что цена дома будет выше или ниже медианы рынка, а также классификацию домов по ценовым категориям (например, низкая, средняя, высокая). Это поможет выявить предпочтения покупателей.\n",
"\n",
"Для оценки регрессионных моделей будут использоваться метрики MAE (средняя абсолютная ошибка) и R² (коэффициент детерминации), с целью достижения MAE менее 10% от средней цены. В классификации основное внимание уделяется метрикам accuracy и F1-score, с целевым значением accuracy около 80%.\n",
"\n",
"## Ориентиры для каждой задачи:\n",
"Регрессия: Медианная цена (price.median()) выбрана как стабильный ориентир.\n",
"\n",
"Классификация: Ориентиром служит средняя вероятность предсказания класса выше медианы.\n",
"\n",
"Анализ алгоритмов машинного обучения:\n",
"\n",
"### Регрессия:\n",
"\n",
"Линейная регрессия: Подходит для простых линейных зависимостей.\n",
"\n",
"Дерево решений: Учет нелинейных зависимостей и сложных закономерностей.\n",
"\n",
"Случайный лес: Ансамблевый метод, обобщающий данные и эффективно обрабатывающий выбросы.\n",
"\n",
"### Классификация:\n",
"\n",
"Логистическая регрессия: Простая модель для бинарной классификации.\n",
"\n",
"Метод опорных векторов (SVM): Эффективен на данных с четкими разделениями.\n",
"\n",
"Градиентный бустинг: Подходит для сложных и высокоразмерных данных, обеспечивает высокую точность.\n",
"\n",
"## Выбор трех моделей для каждой задачи:\n",
"\n",
"Регрессия: Линейная регрессия, Дерево решений, Случайный лес.\n",
"\n",
"Классификация: Логистическая регрессия, Метод опорных векторов (SVM), Градиентный бустинг."
]
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{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Index(['id', 'date', 'price', 'bedrooms', 'bathrooms', 'sqft_living',\n",
" 'sqft_lot', 'floors', 'waterfront', 'view', 'condition', 'grade',\n",
" 'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated', 'zipcode',\n",
" 'lat', 'long', 'sqft_living15', 'sqft_lot15'],\n",
" dtype='object')\n"
]
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"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
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" <th></th>\n",
" <th>id</th>\n",
" <th>date</th>\n",
" <th>price</th>\n",
" <th>bedrooms</th>\n",
" <th>bathrooms</th>\n",
" <th>sqft_living</th>\n",
" <th>sqft_lot</th>\n",
" <th>floors</th>\n",
" <th>waterfront</th>\n",
" <th>view</th>\n",
" <th>...</th>\n",
" <th>grade</th>\n",
" <th>sqft_above</th>\n",
" <th>sqft_basement</th>\n",
" <th>yr_built</th>\n",
" <th>yr_renovated</th>\n",
" <th>zipcode</th>\n",
" <th>lat</th>\n",
" <th>long</th>\n",
" <th>sqft_living15</th>\n",
" <th>sqft_lot15</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>7129300520</td>\n",
" <td>20141013T000000</td>\n",
" <td>221900.0</td>\n",
" <td>3</td>\n",
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" <td>1180</td>\n",
" <td>5650</td>\n",
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" <td>-122.257</td>\n",
" <td>1340</td>\n",
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" <tr>\n",
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" <td>20141209T000000</td>\n",
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" <td>3</td>\n",
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" <td>1951</td>\n",
" <td>1991</td>\n",
" <td>98125</td>\n",
" <td>47.7210</td>\n",
" <td>-122.319</td>\n",
" <td>1690</td>\n",
" <td>7639</td>\n",
" </tr>\n",
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" <th>2</th>\n",
" <td>5631500400</td>\n",
" <td>20150225T000000</td>\n",
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" <td>770</td>\n",
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" <td>770</td>\n",
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" <td>1933</td>\n",
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" <td>98028</td>\n",
" <td>47.7379</td>\n",
" <td>-122.233</td>\n",
" <td>2720</td>\n",
" <td>8062</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2487200875</td>\n",
" <td>20141209T000000</td>\n",
" <td>604000.0</td>\n",
" <td>4</td>\n",
" <td>3.00</td>\n",
" <td>1960</td>\n",
" <td>5000</td>\n",
" <td>1.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>7</td>\n",
" <td>1050</td>\n",
" <td>910</td>\n",
" <td>1965</td>\n",
" <td>0</td>\n",
" <td>98136</td>\n",
" <td>47.5208</td>\n",
" <td>-122.393</td>\n",
" <td>1360</td>\n",
" <td>5000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1954400510</td>\n",
" <td>20150218T000000</td>\n",
" <td>510000.0</td>\n",
" <td>3</td>\n",
" <td>2.00</td>\n",
" <td>1680</td>\n",
" <td>8080</td>\n",
" <td>1.0</td>\n",
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" <td>1800</td>\n",
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"text/plain": [
" id date price bedrooms bathrooms sqft_living \\\n",
"0 7129300520 20141013T000000 221900.0 3 1.00 1180 \n",
"1 6414100192 20141209T000000 538000.0 3 2.25 2570 \n",
"2 5631500400 20150225T000000 180000.0 2 1.00 770 \n",
"3 2487200875 20141209T000000 604000.0 4 3.00 1960 \n",
"4 1954400510 20150218T000000 510000.0 3 2.00 1680 \n",
"\n",
" sqft_lot floors waterfront view ... grade sqft_above sqft_basement \\\n",
"0 5650 1.0 0 0 ... 7 1180 0 \n",
"1 7242 2.0 0 0 ... 7 2170 400 \n",
"2 10000 1.0 0 0 ... 6 770 0 \n",
"3 5000 1.0 0 0 ... 7 1050 910 \n",
"4 8080 1.0 0 0 ... 8 1680 0 \n",
"\n",
" yr_built yr_renovated zipcode lat long sqft_living15 \\\n",
"0 1955 0 98178 47.5112 -122.257 1340 \n",
"1 1951 1991 98125 47.7210 -122.319 1690 \n",
"2 1933 0 98028 47.7379 -122.233 2720 \n",
"3 1965 0 98136 47.5208 -122.393 1360 \n",
"4 1987 0 98074 47.6168 -122.045 1800 \n",
"\n",
" sqft_lot15 \n",
"0 5650 \n",
"1 7639 \n",
"2 8062 \n",
"3 5000 \n",
"4 7503 \n",
"\n",
"[5 rows x 21 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"from sklearn import set_config\n",
"\n",
"set_config(transform_output=\"pandas\")\n",
"random_state = 42\n",
"\n",
"df = pd.read_csv(\".//static//csv//kc_house_data.csv\")\n",
"print(df.columns)\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'X_train'"
]
},
"metadata": {},
"output_type": "display_data"
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" <th>7343</th>\n",
" <td>4289900005</td>\n",
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" <td>1.5</td>\n",
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" <td>...</td>\n",
" <td>0</td>\n",
" <td>1941</td>\n",
" <td>0</td>\n",
" <td>98168</td>\n",
" <td>47.5054</td>\n",
" <td>-122.301</td>\n",
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" <th>16670</th>\n",
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" <td>20140619T000000</td>\n",
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" <td>4</td>\n",
" <td>2.75</td>\n",
" <td>3010</td>\n",
" <td>15992</td>\n",
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" <td>0</td>\n",
" <td>1996</td>\n",
" <td>0</td>\n",
" <td>98075</td>\n",
" <td>47.5895</td>\n",
" <td>-121.994</td>\n",
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" <td>1</td>\n",
" <td>2</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>88</th>\n",
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" <td>20140519T000000</td>\n",
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" <td>2</td>\n",
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" <td>1610</td>\n",
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" <td>2.0</td>\n",
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" <td>0</td>\n",
" <td>98056</td>\n",
" <td>47.5180</td>\n",
" <td>-122.194</td>\n",
" <td>1950</td>\n",
" <td>2025</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15031</th>\n",
" <td>7129303070</td>\n",
" <td>20140820T000000</td>\n",
" <td>735000.0</td>\n",
" <td>4</td>\n",
" <td>2.75</td>\n",
" <td>3040</td>\n",
" <td>2415</td>\n",
" <td>2.0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>1966</td>\n",
" <td>0</td>\n",
" <td>98118</td>\n",
" <td>47.5188</td>\n",
" <td>-122.256</td>\n",
" <td>2620</td>\n",
" <td>2433</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" </tr>\n",
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" <th>5234</th>\n",
" <td>2432000130</td>\n",
" <td>20150414T000000</td>\n",
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" <td>3</td>\n",
" <td>1.75</td>\n",
" <td>1660</td>\n",
" <td>9549</td>\n",
" <td>1.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>1956</td>\n",
" <td>0</td>\n",
" <td>98033</td>\n",
" <td>47.6503</td>\n",
" <td>-122.198</td>\n",
" <td>2090</td>\n",
" <td>9549</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
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" <th>19980</th>\n",
" <td>774100475</td>\n",
" <td>20140627T000000</td>\n",
" <td>415000.0</td>\n",
" <td>3</td>\n",
" <td>2.75</td>\n",
" <td>2600</td>\n",
" <td>64626</td>\n",
" <td>1.5</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>2009</td>\n",
" <td>0</td>\n",
" <td>98014</td>\n",
" <td>47.7185</td>\n",
" <td>-121.405</td>\n",
" <td>1740</td>\n",
" <td>64626</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3671</th>\n",
" <td>8847400115</td>\n",
" <td>20140723T000000</td>\n",
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" <td>3</td>\n",
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" <td>2420</td>\n",
" <td>208652</td>\n",
" <td>1.5</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
" <td>2005</td>\n",
" <td>0</td>\n",
" <td>98010</td>\n",
" <td>47.3666</td>\n",
" <td>-121.978</td>\n",
" <td>3180</td>\n",
" <td>212137</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>17290 rows × 23 columns</p>\n",
"</div>"
],
"text/plain": [
" id date price bedrooms bathrooms \\\n",
"20962 1278000210 20150311T000000 110000.0 2 1.00 \n",
"12284 2193300390 20140923T000000 624000.0 4 3.25 \n",
"7343 4289900005 20141230T000000 1535000.0 4 3.25 \n",
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"\n",
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"\n",
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"\n",
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"\n",
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"\n",
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{
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"image/png": "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
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA0EAAAIjCAYAAADFthA8AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAACGn0lEQVR4nO3de1yTdf8/8NcYZ0UOngamxjwWukQrQwqtTPOUnTy1UtOyG6gsu++7zOGiILO0M1BqYQmlWZnZbZn6TdcBz9IU8zzTFDyEHAQ5uF2/P/xtbTJg4OBi1/V6Ph48ZNf13va+2MDrvc/nen8UgiAIICIiIiIikgkvsRMgIiIiIiJqTiyCiIiIiIhIVlgEERERERGRrLAIIiIiIiIiWWERREREREREssIiiIiIiIiIZIVFEBERERERyQqLICIiIiIikhUWQUREREREJCssgoiIiIiISFZYBBERERERkaywCCKSsQ8++ADDhw9Hx44d4ePjA5VKhcGDB+PTTz+FxWIROz0iIiKiJqEQBEEQOwkiEkdMTAzCw8Nxxx13oE2bNigqKsKWLVuwfPlyTJgwAZ9//rnYKRIRERG5HYsgIhmrrq6Gj49Pje1PPfUU3n//fZhMJlx77bXNnxgRERFRE+J0OCIZc1YAAbAVPl5e//yJWL16NUaNGoWIiAj4+fmhW7dueOWVV2A2mx3uO2TIECgUCttXu3btMGrUKOzdu9chTqFQ4KWXXnLY9sYbb0ChUGDIkCEO2ysqKvDSSy+hZ8+e8Pf3R3h4OO6//34cOXIEAHDs2DEoFAosXbrU4X6JiYlQKBSYOnWqbdvSpUuhUCjg6+uLs2fPOsTn5OTY8t6xY4fDvpUrV2LAgAEICAhAu3bt8PDDD+PkyZM1fnb79+/H+PHj0b59ewQEBKBXr16YM2cOAOCll15y+Nk4+9q0aZPt59inT58aj++K2u67YMECKBQKHDt2zGF7UVERnnnmGXTu3Bl+fn7o3r075s+f7zAl0vozXrBgQY3H7dOnj8NrtmnTJigUCnz55Ze15jh16lSXC+z09HRERUXBz88PERERSExMRFFRkcPx1vdzrcuQIUNqvOdSU1Ph5eWFzz77zGG7q+8DALXmYv/zd/X3wPreudK1117r8P4GXHs9AcBiseCdd95B37594e/vj/bt2+Puu++2vffr+5la87O+3tYvPz8/9OzZE/PmzYP956x//vknEhIS0KtXLwQEBKBt27YYN25cjfdjberLt76cr3y/LViwAIMGDULbtm0REBCAAQMG1Pqetf7dqO1n0JCffUN+l4io6XiLnQARia+oqAiXLl1CaWkpdu7ciQULFmDixIno0qWLLWbp0qVo3bo1Zs2ahdatW+P//u//MHfuXJSUlOCNN95weLzevXtjzpw5EAQBR44cwZtvvomRI0fi+PHjdeYwb968GtvNZjNGjx6NjRs3YuLEiZg5cyZKS0uxfv167N27F926dXP6eIcPH8bixYtrfT6lUomsrCw8++yztm2ZmZnw9/dHRUWFQ+zSpUvx6KOP4qabbsK8efNw+vRpvPPOO/j111+xe/duhISEAACMRiNuu+02+Pj4YMaMGbj22mtx5MgRrFmzBqmpqbj//vvRvXt32+M+++yzuO666zBjxgzbtuuuu67WnJtCeXk5Bg8ejJMnT+KJJ55Aly5d8Ntvv2H27NnIz8/H22+/3az5XOmll15CcnIyhg4divj4eBw4cAAZGRnYvn07fv31V/j4+GDOnDl47LHHAADnzp3Ds88+ixkzZuC2225r1HNmZmZCp9Nh4cKFeOihh2zbXX0f2Lvvvvtw//33AwB+/vlnLFq0qM7nru33wFUNeT2nT5+OpUuXYsSIEXjsscdw6dIl/Pzzz9iyZQtuvPFGLFu2zBZrzf2tt95Cu3btAAAdO3Z0eO4XX3wR1113HS5evIgVK1bgxRdfRIcOHTB9+nQAwPbt2/Hbb79h4sSJuOaaa3Ds2DFkZGRgyJAh2LdvHwIDA+s8tvrytbrrrrswefJkh/suXLgQ58+fd9j2zjvv4J577oFWq0VVVRWWL1+OcePG4bvvvsOoUaOc5mB//KmpqY3+2RNRCyAQkez16tVLAGD7mjx5slBdXe0QU15eXuN+TzzxhBAYGChUVFTYtg0ePFgYPHiwQ9yLL74oABDOnDlj2wZA0Ov1ttv//e9/hQ4dOggDBgxwuP/HH38sABDefPPNGs9vsVgEQRAEk8kkABAyMzNt+8aPHy/06dNH6Ny5szBlyhTb9szMTAGAMGnSJKFv37627WVlZUKbNm2Ehx56SAAgbN++XRAEQaiqqhI6dOgg9OnTR7h48aIt/rvvvhMACHPnzrVti4uLE4KCgoQ///zTaZ5X6tq1q0Nu9gYPHixERUU53Vef2u77xhtvCAAEk8lk2/bKK68IrVq1Eg4ePOgQ+8ILLwhKpVI4fvy4IAj//IzfeOONGo8bFRXl8Jr99NNPAgBh5cqVteY4ZcoUoWvXrnUex5kzZwRfX19h2LBhgtlstm1///33BQDCxx9/XOM+zt4L9bF/z/7vf/8TvL29heeee84hpiHvA0EQhOrqagGAkJycbNtmfe/Z//xd/T1ITk4WANR4L135HnL19fy///s/AYDw9NNP1/h5OHu/Osvdyvp6//TTT7ZtFRUVgpeXl5CQkGDb5uxvSE5OjgBA+PTTT2vss+dqvgCExMTEGjGjRo2q8X67Mp+qqiqhT58+wh133FHj/osXLxYAOPxuX/m3ril+l4io6UhmOpzBYMCYMWMQEREBhUKBb775psGPIQgCFixYgJ49e8LPzw+dOnWq8UkPkRRlZmZi/fr1yM7OxvTp05Gdne0wOgEAAQEBtu9LS0tx7tw53HbbbSgvL8f+/fsdYqurq3Hu3DmcPXsWOTk5WLVqFTQaje0T1CudPHkS7733HpKSktC6dWuHfV999RXatWuHp556qsb9apvmtHPnTqxcuRLz5s1zmNJn75FHHsH+/fttU2m++uorBAcH484773SI27FjB86cOYOEhAT4+/vbto8aNQq9e/fG//73PwDA2bNnYTAYMG3aNIcRtLryrI/ZbMa5c+dw7tw5VFVVNeox6rNy5UrcdtttCA0NtT3XuXPnMHToUJjNZhgMBof48vJyh7hz587VmBJpZX2f2E9da4gNGzagqqoKzzzzjMPr+Pjjj6NNmza2n727bNu2DePHj8cDDzxQY3TT1feBlfX18vPzc/n56/o96NChAwDgr7/+qvMxXH09v/rqKygUCuj1+hqP0dj3a3FxMc6dO4fjx4/j9ddfh8ViwR133GHbb/83pLq6Gn///Te6d++OkJAQ7Nq1q87Hbop87fM5f/48iouLcdtttznNxZXXsyl/l4jI/SQzHa6srAw33HADpk2bZpt60FAzZ87Ejz/+iAULFqBv374oLCxEYWGhmzMlanliYmJs3z/00ENQq9WYM2cOpk+fjtjYWABAXl4edDod/u///g8lJSUO9y8uLna4/dtvv6F9+/a22z169MA333xT68mKXq9HREQEnnjiiRpz8o8cOYJevXrB29v1P1cvvPACbrvtNowePRpPPvmk05j27dtj1KhR+Pjjj3HjjTfi448/xpQpU2oUTX/++ScAoFevXjUeo3fv3vjll18AAEePHgWARl/H48z+/fttP0cvLy90794der3eYYrW1Tp06BCMRqPD62XvzJkzDrf1er3TE9Erp0YBwLRp02zft27dGmPGjMFbb73lNNaZ2n72vr6+UKvVtv3ucPLkSYwaNQplZWX4+++/a7xXXX0fWFkLvyuLmbrU9XsQExMDhUKB2bNnIyUlxfa4V17n4+rreeTIEURERCAsLMzl/Opz77332r738vKCTqfDAw88YNt28eJFzJs3D5mZmTh58qTD9UJX/g25UlPk+9133yElJQW5ubmorKy0bXf2d8qV17Mpf5eIyP0kUwSNGDECI0aMqHV/ZWUl5syZg88//xxFRUXo06cP5s+fb7sA8Y8//kBGRgb27t1r+08uMjKyOVInanEefPBBzJkzB1u3bkV
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"from typing import Tuple\n",
"import pandas as pd\n",
"from pandas import DataFrame\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"median_price = df['price'].median()\n",
"df['above_median_price'] = np.where(df['price'] > median_price, 1, 0)\n",
"\n",
"X = df.drop(columns=['id', 'date', 'price', 'above_median_price'])\n",
"y = df['above_median_price']\n",
"\n",
"df['price_category'] = pd.cut(df['price'], bins=[0, 300000, 700000, np.inf], labels=[0, 1, 2])\n",
"\n",
"X = df.drop(columns=['id', 'date', 'price', 'price_category'])\n",
"\n",
"def split_stratified_into_train_val_test(\n",
" df_input,\n",
" stratify_colname=\"y\",\n",
" frac_train=0.6,\n",
" frac_val=0.15,\n",
" frac_test=0.25,\n",
" random_state=None,\n",
") -> Tuple[DataFrame, DataFrame, DataFrame, DataFrame, DataFrame, DataFrame]:\n",
" \n",
" if frac_train + frac_val + frac_test != 1.0:\n",
" raise ValueError(\n",
" \"fractions %f, %f, %f do not add up to 1.0\"\n",
" % (frac_train, frac_val, frac_test)\n",
" )\n",
" \n",
" if stratify_colname not in df_input.columns:\n",
" raise ValueError(\"%s is not a column in the dataframe\" % (stratify_colname))\n",
" X = df_input # Contains all columns.\n",
" y = df_input[\n",
" [stratify_colname]\n",
" ] # Dataframe of just the column on which to stratify.\n",
" \n",
" # Split original dataframe into train and temp dataframes.\n",
" df_train, df_temp, y_train, y_temp = train_test_split(\n",
" X, y, stratify=y, test_size=(1.0 - frac_train), random_state=random_state\n",
" )\n",
"\n",
" if frac_val <= 0:\n",
" assert len(df_input) == len(df_train) + len(df_temp)\n",
" return df_train, pd.DataFrame(), df_temp, y_train, pd.DataFrame(), y_temp\n",
" # Split the temp dataframe into val and test dataframes.\n",
" relative_frac_test = frac_test / (frac_val + frac_test)\n",
"\n",
" df_val, df_test, y_val, y_test = train_test_split(\n",
" df_temp,\n",
" y_temp,\n",
" stratify=y_temp,\n",
" test_size=relative_frac_test,\n",
" random_state=random_state,\n",
" )\n",
"\n",
" assert len(df_input) == len(df_train) + len(df_val) + len(df_test)\n",
" return df_train, df_val, df_test, y_train, y_val, y_test\n",
"\n",
"X_train, X_val, X_test, y_train, y_val, y_test = split_stratified_into_train_val_test(\n",
" df, stratify_colname=\"above_median_price\", frac_train=0.80, frac_val=0, frac_test=0.20, random_state=42\n",
")\n",
"\n",
"display(\"X_train\", X_train)\n",
"display(\"y_train\", y_train)\n",
"\n",
"display(\"X_test\", X_test)\n",
"display(\"y_test\", y_test)\n",
"\n",
"print(df.dtypes)\n",
"\n",
"plt.figure(figsize=(10, 6))\n",
"sns.histplot(df['price'], bins=50, kde=True)\n",
"plt.title('Распределение цен на недвижимость')\n",
"plt.xlabel('Цена')\n",
"plt.ylabel('Частота')\n",
"plt.show()\n",
"\n",
"plt.figure(figsize=(10, 6))\n",
"sns.boxplot(x='bedrooms', y='price', data=df)\n",
"plt.title('Зависимость цены от количества спален')\n",
"plt.xlabel('Количество спален')\n",
"plt.ylabel('Цена')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Конвейеры предобработки"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from sklearn.base import BaseEstimator, TransformerMixin\n",
"from sklearn.compose import ColumnTransformer\n",
"from sklearn.discriminant_analysis import StandardScaler\n",
"from sklearn.impute import SimpleImputer\n",
"from sklearn.preprocessing import OneHotEncoder\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.pipeline import Pipeline\n",
"\n",
"pipeline_end = StandardScaler()\n",
"\n",
"\n",
"# Построение конвейеров предобработки\n",
"\n",
"class HouseFeatures(BaseEstimator, TransformerMixin):\n",
" def __init__(self):\n",
" pass\n",
" def fit(self, X, y=None):\n",
" return self\n",
" def transform(self, X, y=None):\n",
" # Создание новых признаков\n",
" X = X.copy()\n",
" X[\"Living_area_to_Lot_ratio\"] = X[\"sqft_living\"] / X[\"sqft_lot\"]\n",
" return X\n",
" def get_feature_names_out(self, features_in):\n",
" # Добавление имен новых признаков\n",
" new_features = [\"Living_area_to_Lot_ratio\"]\n",
" return np.append(features_in, new_features, axis=0)\n",
"\n",
"\n",
"# Обработка числовых данных. Числовой конвейр: заполнение пропущенных значений медианой и стандартизация\n",
"preprocessing_num_class = Pipeline(steps=[\n",
" ('imputer', SimpleImputer(strategy='median')),\n",
" ('scaler', StandardScaler())\n",
"])\n",
"\n",
"preprocessing_cat_class = Pipeline(steps=[\n",
" ('imputer', SimpleImputer(strategy='most_frequent')),\n",
" ('onehot', OneHotEncoder(handle_unknown='ignore'))\n",
"])\n",
"\n",
"columns_to_drop = [\"date\"]\n",
"numeric_columns = [\"sqft_living\", \"sqft_lot\", \"above_median_price\"]\n",
"cat_columns = []\n",
"\n",
"features_preprocessing = ColumnTransformer(\n",
" verbose_feature_names_out=False,\n",
" transformers=[\n",
" (\"prepocessing_num\", preprocessing_num_class, numeric_columns),\n",
" (\"prepocessing_cat\", preprocessing_cat_class, cat_columns),\n",
" ],\n",
" remainder=\"passthrough\"\n",
")\n",
"\n",
"drop_columns = ColumnTransformer(\n",
" verbose_feature_names_out=False,\n",
" transformers=[\n",
" (\"drop_columns\", \"drop\", columns_to_drop),\n",
" ],\n",
" remainder=\"passthrough\",\n",
")\n",
"\n",
"features_postprocessing = ColumnTransformer(\n",
" verbose_feature_names_out=False,\n",
" transformers=[\n",
" ('preprocessing_cat', preprocessing_cat_class, [\"price_category\"]),\n",
" ],\n",
" remainder=\"passthrough\",\n",
")\n",
"\n",
"pipeline_end = Pipeline(\n",
" [\n",
" (\"features_preprocessing\", features_preprocessing),\n",
" (\"custom_features\", HouseFeatures()),\n",
" (\"drop_columns\", drop_columns),\n",
" ]\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>sqft_living</th>\n",
" <th>sqft_lot</th>\n",
" <th>above_median_price</th>\n",
" <th>id</th>\n",
" <th>price</th>\n",
" <th>bedrooms</th>\n",
" <th>bathrooms</th>\n",
" <th>floors</th>\n",
" <th>waterfront</th>\n",
" <th>view</th>\n",
" <th>...</th>\n",
" <th>sqft_basement</th>\n",
" <th>yr_built</th>\n",
" <th>yr_renovated</th>\n",
" <th>zipcode</th>\n",
" <th>lat</th>\n",
" <th>long</th>\n",
" <th>sqft_living15</th>\n",
" <th>sqft_lot15</th>\n",
" <th>price_category</th>\n",
" <th>Living_area_to_Lot_ratio</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>20962</th>\n",
" <td>-1.360742</td>\n",
" <td>-0.262132</td>\n",
" <td>-0.994693</td>\n",
" <td>1278000210</td>\n",
" <td>110000.0</td>\n",
" <td>2</td>\n",
" <td>1.00</td>\n",
" <td>1.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>1968</td>\n",
" <td>2007</td>\n",
" <td>98001</td>\n",
" <td>47.2655</td>\n",
" <td>-122.244</td>\n",
" <td>828</td>\n",
" <td>5402</td>\n",
" <td>0</td>\n",
" <td>5.191063</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12284</th>\n",
" <td>0.794390</td>\n",
" <td>-0.094121</td>\n",
" <td>1.005335</td>\n",
" <td>2193300390</td>\n",
" <td>624000.0</td>\n",
" <td>4</td>\n",
" <td>3.25</td>\n",
" <td>1.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>1130</td>\n",
" <td>1980</td>\n",
" <td>0</td>\n",
" <td>98052</td>\n",
" <td>47.6920</td>\n",
" <td>-122.099</td>\n",
" <td>2110</td>\n",
" <td>11250</td>\n",
" <td>1</td>\n",
" <td>-8.440052</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7343</th>\n",
" <td>0.837884</td>\n",
" <td>-0.272723</td>\n",
" <td>1.005335</td>\n",
" <td>4289900005</td>\n",
" <td>1535000.0</td>\n",
" <td>4</td>\n",
" <td>3.25</td>\n",
" <td>2.0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>...</td>\n",
" <td>1030</td>\n",
" <td>1908</td>\n",
" <td>2003</td>\n",
" <td>98122</td>\n",
" <td>47.6147</td>\n",
" <td>-122.285</td>\n",
" <td>2130</td>\n",
" <td>4200</td>\n",
" <td>2</td>\n",
" <td>-3.072292</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14247</th>\n",
" <td>-0.782270</td>\n",
" <td>-0.196986</td>\n",
" <td>-0.994693</td>\n",
" <td>316000145</td>\n",
" <td>235000.0</td>\n",
" <td>4</td>\n",
" <td>1.00</td>\n",
" <td>1.5</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>1941</td>\n",
" <td>0</td>\n",
" <td>98168</td>\n",
" <td>47.5054</td>\n",
" <td>-122.301</td>\n",
" <td>1280</td>\n",
" <td>7175</td>\n",
" <td>0</td>\n",
" <td>3.971201</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16670</th>\n",
" <td>1.011860</td>\n",
" <td>0.024330</td>\n",
" <td>1.005335</td>\n",
" <td>629400480</td>\n",
" <td>775000.0</td>\n",
" <td>4</td>\n",
" <td>2.75</td>\n",
" <td>2.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>1996</td>\n",
" <td>0</td>\n",
" <td>98075</td>\n",
" <td>47.5895</td>\n",
" <td>-121.994</td>\n",
" <td>3330</td>\n",
" <td>12333</td>\n",
" <td>2</td>\n",
" <td>41.589045</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>88</th>\n",
" <td>-0.510432</td>\n",
" <td>-0.324180</td>\n",
" <td>-0.994693</td>\n",
" <td>1332700270</td>\n",
" <td>215000.0</td>\n",
" <td>2</td>\n",
" <td>2.25</td>\n",
" <td>2.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>1979</td>\n",
" <td>0</td>\n",
" <td>98056</td>\n",
" <td>47.5180</td>\n",
" <td>-122.194</td>\n",
" <td>1950</td>\n",
" <td>2025</td>\n",
" <td>0</td>\n",
" <td>1.574534</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15031</th>\n",
" <td>1.044481</td>\n",
" <td>-0.314813</td>\n",
" <td>1.005335</td>\n",
" <td>7129303070</td>\n",
" <td>735000.0</td>\n",
" <td>4</td>\n",
" <td>2.75</td>\n",
" <td>2.0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>1966</td>\n",
" <td>0</td>\n",
" <td>98118</td>\n",
" <td>47.5188</td>\n",
" <td>-122.256</td>\n",
" <td>2620</td>\n",
" <td>2433</td>\n",
" <td>2</td>\n",
" <td>-3.317784</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5234</th>\n",
" <td>-0.456065</td>\n",
" <td>-0.136611</td>\n",
" <td>1.005335</td>\n",
" <td>2432000130</td>\n",
" <td>675000.0</td>\n",
" <td>3</td>\n",
" <td>1.75</td>\n",
" <td>1.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>1956</td>\n",
" <td>0</td>\n",
" <td>98033</td>\n",
" <td>47.6503</td>\n",
" <td>-122.198</td>\n",
" <td>2090</td>\n",
" <td>9549</td>\n",
" <td>1</td>\n",
" <td>3.338418</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19980</th>\n",
" <td>0.566046</td>\n",
" <td>1.239169</td>\n",
" <td>-0.994693</td>\n",
" <td>774100475</td>\n",
" <td>415000.0</td>\n",
" <td>3</td>\n",
" <td>2.75</td>\n",
" <td>1.5</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>2009</td>\n",
" <td>0</td>\n",
" <td>98014</td>\n",
" <td>47.7185</td>\n",
" <td>-121.405</td>\n",
" <td>1740</td>\n",
" <td>64626</td>\n",
" <td>1</td>\n",
" <td>0.456795</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3671</th>\n",
" <td>0.370323</td>\n",
" <td>4.836825</td>\n",
" <td>1.005335</td>\n",
" <td>8847400115</td>\n",
" <td>590000.0</td>\n",
" <td>3</td>\n",
" <td>2.00</td>\n",
" <td>1.5</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>2005</td>\n",
" <td>0</td>\n",
" <td>98010</td>\n",
" <td>47.3666</td>\n",
" <td>-121.978</td>\n",
" <td>3180</td>\n",
" <td>212137</td>\n",
" <td>1</td>\n",
" <td>0.076563</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>17290 rows × 23 columns</p>\n",
"</div>"
],
"text/plain": [
" sqft_living sqft_lot above_median_price id price \\\n",
"20962 -1.360742 -0.262132 -0.994693 1278000210 110000.0 \n",
"12284 0.794390 -0.094121 1.005335 2193300390 624000.0 \n",
"7343 0.837884 -0.272723 1.005335 4289900005 1535000.0 \n",
"14247 -0.782270 -0.196986 -0.994693 316000145 235000.0 \n",
"16670 1.011860 0.024330 1.005335 629400480 775000.0 \n",
"... ... ... ... ... ... \n",
"88 -0.510432 -0.324180 -0.994693 1332700270 215000.0 \n",
"15031 1.044481 -0.314813 1.005335 7129303070 735000.0 \n",
"5234 -0.456065 -0.136611 1.005335 2432000130 675000.0 \n",
"19980 0.566046 1.239169 -0.994693 774100475 415000.0 \n",
"3671 0.370323 4.836825 1.005335 8847400115 590000.0 \n",
"\n",
" bedrooms bathrooms floors waterfront view ... sqft_basement \\\n",
"20962 2 1.00 1.0 0 0 ... 0 \n",
"12284 4 3.25 1.0 0 0 ... 1130 \n",
"7343 4 3.25 2.0 0 3 ... 1030 \n",
"14247 4 1.00 1.5 0 0 ... 0 \n",
"16670 4 2.75 2.0 0 0 ... 0 \n",
"... ... ... ... ... ... ... ... \n",
"88 2 2.25 2.0 0 0 ... 0 \n",
"15031 4 2.75 2.0 1 4 ... 0 \n",
"5234 3 1.75 1.0 0 0 ... 0 \n",
"19980 3 2.75 1.5 0 0 ... 0 \n",
"3671 3 2.00 1.5 0 0 ... 0 \n",
"\n",
" yr_built yr_renovated zipcode lat long sqft_living15 \\\n",
"20962 1968 2007 98001 47.2655 -122.244 828 \n",
"12284 1980 0 98052 47.6920 -122.099 2110 \n",
"7343 1908 2003 98122 47.6147 -122.285 2130 \n",
"14247 1941 0 98168 47.5054 -122.301 1280 \n",
"16670 1996 0 98075 47.5895 -121.994 3330 \n",
"... ... ... ... ... ... ... \n",
"88 1979 0 98056 47.5180 -122.194 1950 \n",
"15031 1966 0 98118 47.5188 -122.256 2620 \n",
"5234 1956 0 98033 47.6503 -122.198 2090 \n",
"19980 2009 0 98014 47.7185 -121.405 1740 \n",
"3671 2005 0 98010 47.3666 -121.978 3180 \n",
"\n",
" sqft_lot15 price_category Living_area_to_Lot_ratio \n",
"20962 5402 0 5.191063 \n",
"12284 11250 1 -8.440052 \n",
"7343 4200 2 -3.072292 \n",
"14247 7175 0 3.971201 \n",
"16670 12333 2 41.589045 \n",
"... ... ... ... \n",
"88 2025 0 1.574534 \n",
"15031 2433 2 -3.317784 \n",
"5234 9549 1 3.338418 \n",
"19980 64626 1 0.456795 \n",
"3671 212137 1 0.076563 \n",
"\n",
"[17290 rows x 23 columns]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"preprocessing_result = pipeline_end.fit_transform(X_train)\n",
"preprocessed_df = pd.DataFrame(\n",
" preprocessing_result,\n",
" columns=pipeline_end.get_feature_names_out(),\n",
")\n",
"\n",
"preprocessed_df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Формирование набора моделей для классификации\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn import ensemble, linear_model, naive_bayes, neighbors, neural_network, tree, svm\n",
"\n",
"class_models = {\n",
" \"logistic\": {\"model\": linear_model.LogisticRegression(max_iter=150)}, # логистическая \n",
" \"ridge\": {\"model\": linear_model.RidgeClassifierCV(cv=5, class_weight=\"balanced\")}, # гребневая регрессия\n",
" \"ridge\": {\"model\": linear_model.LogisticRegression(max_iter=150, solver='lbfgs', penalty=\"l2\", class_weight=\"balanced\")},\n",
" \"decision_tree\": { # дерево решений\n",
" \"model\": tree.DecisionTreeClassifier(max_depth=5, min_samples_split=10, random_state=random_state)\n",
" },\n",
"\n",
" \"knn\": {\"model\": neighbors.KNeighborsClassifier(n_neighbors=7)},\n",
" \"naive_bayes\": {\"model\": naive_bayes.GaussianNB()}, # наивный Байесовский классификатор\n",
"\n",
" # метод градиентного бустинга (набор деревьев решений)\n",
" \"gradient_boosting\": { \n",
" \"model\": ensemble.GradientBoostingClassifier(n_estimators=210)\n",
" },\n",
"\n",
" # метод случайного леса (набор деревьев решений) \n",
" \"random_forest\": { \n",
" \"model\": ensemble.RandomForestClassifier(\n",
" max_depth=5, class_weight=\"balanced\", random_state=random_state\n",
" )\n",
" },\n",
" # многослойный персептрон (нейронная сеть)\n",
" \"mlp\": {\n",
" \"model\": neural_network.MLPClassifier(\n",
" hidden_layer_sizes=(7,),\n",
" max_iter=200,\n",
" early_stopping=True,\n",
" random_state=random_state,\n",
" )\n",
" },\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: logistic\n",
"Model: ridge\n",
"Model: decision_tree\n",
"Model: knn\n",
"Model: naive_bayes\n",
"Model: gradient_boosting\n",
"Model: random_forest\n",
"Model: mlp\n"
]
}
],
"source": [
"import numpy as np\n",
"from sklearn import metrics\n",
"\n",
"for model_name in class_models.keys():\n",
" print(f\"Model: {model_name}\")\n",
" model = class_models[model_name][\"model\"]\n",
"\n",
" model_pipeline = Pipeline([(\"pipeline\", pipeline_end), (\"model\", model)])\n",
" model_pipeline = model_pipeline.fit(X_train, y_train.values.ravel())\n",
"\n",
" y_train_predict = model_pipeline.predict(X_train)\n",
" y_test_probs = model_pipeline.predict_proba(X_test)[:, 1]\n",
" y_test_predict = np.where(y_test_probs > 0.5, 1, 0)\n",
"\n",
" class_models[model_name][\"pipeline\"] = model_pipeline\n",
" class_models[model_name][\"probs\"] = y_test_probs\n",
" class_models[model_name][\"preds\"] = y_test_predict\n",
"\n",
" class_models[model_name][\"Precision_train\"] = metrics.precision_score(\n",
" y_train, y_train_predict, zero_division=1\n",
" )\n",
" class_models[model_name][\"Precision_test\"] = metrics.precision_score(\n",
" y_test, y_test_predict, zero_division=1\n",
" )\n",
" class_models[model_name][\"Recall_train\"] = metrics.recall_score(\n",
" y_train, y_train_predict\n",
" )\n",
" class_models[model_name][\"Recall_test\"] = metrics.recall_score(\n",
" y_test, y_test_predict\n",
" )\n",
" class_models[model_name][\"Accuracy_train\"] = metrics.accuracy_score(\n",
" y_train, y_train_predict\n",
" )\n",
" class_models[model_name][\"Accuracy_test\"] = metrics.accuracy_score(\n",
" y_test, y_test_predict\n",
" )\n",
" class_models[model_name][\"ROC_AUC_test\"] = metrics.roc_auc_score(\n",
" y_test, y_test_probs\n",
" )\n",
" class_models[model_name][\"F1_train\"] = metrics.f1_score(y_train, y_train_predict)\n",
" class_models[model_name][\"F1_test\"] = metrics.f1_score(y_test, y_test_predict)\n",
" class_models[model_name][\"MCC_test\"] = metrics.matthews_corrcoef(\n",
" y_test, y_test_predict\n",
" )\n",
" class_models[model_name][\"Cohen_kappa_test\"] = metrics.cohen_kappa_score(\n",
" y_test, y_test_predict\n",
" )\n",
" class_models[model_name][\"Confusion_matrix\"] = metrics.confusion_matrix(\n",
" y_test, y_test_predict\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 1200x1000 with 16 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from sklearn.metrics import ConfusionMatrixDisplay\n",
"import matplotlib.pyplot as plt\n",
"\n",
"_, ax = plt.subplots(int(len(class_models) / 2), 2, figsize=(12, 10), sharex=False, sharey=False)\n",
"for index, key in enumerate(class_models.keys()):\n",
" c_matrix = class_models[key][\"Confusion_matrix\"]\n",
" disp = ConfusionMatrixDisplay(\n",
" confusion_matrix=c_matrix, display_labels=[\"Less\", \"More\"]\n",
" ).plot(ax=ax.flat[index])\n",
" disp.ax_.set_title(key)\n",
"\n",
"plt.subplots_adjust(top=1, bottom=0, hspace=0.4, wspace=0.1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
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" background-color: #a8db34;\n",
" color: #000000;\n",
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" background-color: #da5a6a;\n",
" color: #f1f1f1;\n",
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"#T_115c5_row5_col2 {\n",
" background-color: #6ccd5a;\n",
" color: #000000;\n",
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"#T_115c5_row5_col3 {\n",
" background-color: #6ece58;\n",
" color: #000000;\n",
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"#T_115c5_row5_col4 {\n",
" background-color: #c43e7f;\n",
" color: #f1f1f1;\n",
"}\n",
"#T_115c5_row5_col5 {\n",
" background-color: #c5407e;\n",
" color: #f1f1f1;\n",
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"#T_115c5_row5_col6, #T_115c5_row5_col7 {\n",
" background-color: #ce4b75;\n",
" color: #f1f1f1;\n",
"}\n",
"#T_115c5_row6_col0 {\n",
" background-color: #40bd72;\n",
" color: #f1f1f1;\n",
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"#T_115c5_row6_col1 {\n",
" background-color: #38b977;\n",
" color: #f1f1f1;\n",
"}\n",
"#T_115c5_row6_col2 {\n",
" background-color: #7fd34e;\n",
" color: #000000;\n",
"}\n",
"#T_115c5_row6_col3 {\n",
" background-color: #75d054;\n",
" color: #000000;\n",
"}\n",
"#T_115c5_row6_col4 {\n",
" background-color: #be3885;\n",
" color: #f1f1f1;\n",
"}\n",
"#T_115c5_row6_col5 {\n",
" background-color: #b42e8d;\n",
" color: #f1f1f1;\n",
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"#T_115c5_row6_col6 {\n",
" background-color: #cc4977;\n",
" color: #f1f1f1;\n",
"}\n",
"#T_115c5_row6_col7 {\n",
" background-color: #c8437b;\n",
" color: #f1f1f1;\n",
"}\n",
"#T_115c5_row7_col0, #T_115c5_row7_col1, #T_115c5_row7_col2, #T_115c5_row7_col3 {\n",
" background-color: #26818e;\n",
" color: #f1f1f1;\n",
"}\n",
"#T_115c5_row7_col4, #T_115c5_row7_col5, #T_115c5_row7_col6, #T_115c5_row7_col7 {\n",
" background-color: #4e02a2;\n",
" color: #f1f1f1;\n",
"}\n",
"</style>\n",
"<table id=\"T_115c5\">\n",
" <thead>\n",
" <tr>\n",
" <th class=\"blank level0\" >&nbsp;</th>\n",
" <th id=\"T_115c5_level0_col0\" class=\"col_heading level0 col0\" >Precision_train</th>\n",
" <th id=\"T_115c5_level0_col1\" class=\"col_heading level0 col1\" >Precision_test</th>\n",
" <th id=\"T_115c5_level0_col2\" class=\"col_heading level0 col2\" >Recall_train</th>\n",
" <th id=\"T_115c5_level0_col3\" class=\"col_heading level0 col3\" >Recall_test</th>\n",
" <th id=\"T_115c5_level0_col4\" class=\"col_heading level0 col4\" >Accuracy_train</th>\n",
" <th id=\"T_115c5_level0_col5\" class=\"col_heading level0 col5\" >Accuracy_test</th>\n",
" <th id=\"T_115c5_level0_col6\" class=\"col_heading level0 col6\" >F1_train</th>\n",
" <th id=\"T_115c5_level0_col7\" class=\"col_heading level0 col7\" >F1_test</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th id=\"T_115c5_level0_row0\" class=\"row_heading level0 row0\" >logistic</th>\n",
" <td id=\"T_115c5_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
" <td id=\"T_115c5_row0_col1\" class=\"data row0 col1\" >1.000000</td>\n",
" <td id=\"T_115c5_row0_col2\" class=\"data row0 col2\" >0.999767</td>\n",
" <td id=\"T_115c5_row0_col3\" class=\"data row0 col3\" >1.000000</td>\n",
" <td id=\"T_115c5_row0_col4\" class=\"data row0 col4\" >0.999884</td>\n",
" <td id=\"T_115c5_row0_col5\" class=\"data row0 col5\" >1.000000</td>\n",
" <td id=\"T_115c5_row0_col6\" class=\"data row0 col6\" >0.999884</td>\n",
" <td id=\"T_115c5_row0_col7\" class=\"data row0 col7\" >1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_115c5_level0_row1\" class=\"row_heading level0 row1\" >ridge</th>\n",
" <td id=\"T_115c5_row1_col0\" class=\"data row1 col0\" >1.000000</td>\n",
" <td id=\"T_115c5_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
" <td id=\"T_115c5_row1_col2\" class=\"data row1 col2\" >0.999651</td>\n",
" <td id=\"T_115c5_row1_col3\" class=\"data row1 col3\" >1.000000</td>\n",
" <td id=\"T_115c5_row1_col4\" class=\"data row1 col4\" >0.999826</td>\n",
" <td id=\"T_115c5_row1_col5\" class=\"data row1 col5\" >1.000000</td>\n",
" <td id=\"T_115c5_row1_col6\" class=\"data row1 col6\" >0.999826</td>\n",
" <td id=\"T_115c5_row1_col7\" class=\"data row1 col7\" >1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_115c5_level0_row2\" class=\"row_heading level0 row2\" >decision_tree</th>\n",
" <td id=\"T_115c5_row2_col0\" class=\"data row2 col0\" >1.000000</td>\n",
" <td id=\"T_115c5_row2_col1\" class=\"data row2 col1\" >1.000000</td>\n",
" <td id=\"T_115c5_row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
" <td id=\"T_115c5_row2_col3\" class=\"data row2 col3\" >1.000000</td>\n",
" <td id=\"T_115c5_row2_col4\" class=\"data row2 col4\" >1.000000</td>\n",
" <td id=\"T_115c5_row2_col5\" class=\"data row2 col5\" >1.000000</td>\n",
" <td id=\"T_115c5_row2_col6\" class=\"data row2 col6\" >1.000000</td>\n",
" <td id=\"T_115c5_row2_col7\" class=\"data row2 col7\" >1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_115c5_level0_row3\" class=\"row_heading level0 row3\" >gradient_boosting</th>\n",
" <td id=\"T_115c5_row3_col0\" class=\"data row3 col0\" >1.000000</td>\n",
" <td id=\"T_115c5_row3_col1\" class=\"data row3 col1\" >1.000000</td>\n",
" <td id=\"T_115c5_row3_col2\" class=\"data row3 col2\" >1.000000</td>\n",
" <td id=\"T_115c5_row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
" <td id=\"T_115c5_row3_col4\" class=\"data row3 col4\" >1.000000</td>\n",
" <td id=\"T_115c5_row3_col5\" class=\"data row3 col5\" >1.000000</td>\n",
" <td id=\"T_115c5_row3_col6\" class=\"data row3 col6\" >1.000000</td>\n",
" <td id=\"T_115c5_row3_col7\" class=\"data row3 col7\" >1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_115c5_level0_row4\" class=\"row_heading level0 row4\" >random_forest</th>\n",
" <td id=\"T_115c5_row4_col0\" class=\"data row4 col0\" >1.000000</td>\n",
" <td id=\"T_115c5_row4_col1\" class=\"data row4 col1\" >1.000000</td>\n",
" <td id=\"T_115c5_row4_col2\" class=\"data row4 col2\" >1.000000</td>\n",
" <td id=\"T_115c5_row4_col3\" class=\"data row4 col3\" >1.000000</td>\n",
" <td id=\"T_115c5_row4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
" <td id=\"T_115c5_row4_col5\" class=\"data row4 col5\" >1.000000</td>\n",
" <td id=\"T_115c5_row4_col6\" class=\"data row4 col6\" >1.000000</td>\n",
" <td id=\"T_115c5_row4_col7\" class=\"data row4 col7\" >1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_115c5_level0_row5\" class=\"row_heading level0 row5\" >naive_bayes</th>\n",
" <td id=\"T_115c5_row5_col0\" class=\"data row5 col0\" >1.000000</td>\n",
" <td id=\"T_115c5_row5_col1\" class=\"data row5 col1\" >1.000000</td>\n",
" <td id=\"T_115c5_row5_col2\" class=\"data row5 col2\" >0.786719</td>\n",
" <td id=\"T_115c5_row5_col3\" class=\"data row5 col3\" >0.793953</td>\n",
" <td id=\"T_115c5_row5_col4\" class=\"data row5 col4\" >0.893927</td>\n",
" <td id=\"T_115c5_row5_col5\" class=\"data row5 col5\" >0.897525</td>\n",
" <td id=\"T_115c5_row5_col6\" class=\"data row5 col6\" >0.880630</td>\n",
" <td id=\"T_115c5_row5_col7\" class=\"data row5 col7\" >0.885144</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_115c5_level0_row6\" class=\"row_heading level0 row6\" >knn</th>\n",
" <td id=\"T_115c5_row6_col0\" class=\"data row6 col0\" >0.872486</td>\n",
" <td id=\"T_115c5_row6_col1\" class=\"data row6 col1\" >0.827473</td>\n",
" <td id=\"T_115c5_row6_col2\" class=\"data row6 col2\" >0.857774</td>\n",
" <td id=\"T_115c5_row6_col3\" class=\"data row6 col3\" >0.820930</td>\n",
" <td id=\"T_115c5_row6_col4\" class=\"data row6 col4\" >0.866917</td>\n",
" <td id=\"T_115c5_row6_col5\" class=\"data row6 col5\" >0.825815</td>\n",
" <td id=\"T_115c5_row6_col6\" class=\"data row6 col6\" >0.865068</td>\n",
" <td id=\"T_115c5_row6_col7\" class=\"data row6 col7\" >0.824189</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_115c5_level0_row7\" class=\"row_heading level0 row7\" >mlp</th>\n",
" <td id=\"T_115c5_row7_col0\" class=\"data row7 col0\" >0.687500</td>\n",
" <td id=\"T_115c5_row7_col1\" class=\"data row7 col1\" >0.615385</td>\n",
" <td id=\"T_115c5_row7_col2\" class=\"data row7 col2\" >0.002558</td>\n",
" <td id=\"T_115c5_row7_col3\" class=\"data row7 col3\" >0.003721</td>\n",
" <td id=\"T_115c5_row7_col4\" class=\"data row7 col4\" >0.503355</td>\n",
" <td id=\"T_115c5_row7_col5\" class=\"data row7 col5\" >0.503354</td>\n",
" <td id=\"T_115c5_row7_col6\" class=\"data row7 col6\" >0.005098</td>\n",
" <td id=\"T_115c5_row7_col7\" class=\"data row7 col7\" >0.007397</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n"
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x1be769da420>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"class_metrics = pd.DataFrame.from_dict(class_models, \"index\")[\n",
" [\n",
" \"Precision_train\",\n",
" \"Precision_test\",\n",
" \"Recall_train\",\n",
" \"Recall_test\",\n",
" \"Accuracy_train\",\n",
" \"Accuracy_test\",\n",
" \"F1_train\",\n",
" \"F1_test\",\n",
" ]\n",
"]\n",
"class_metrics.sort_values(\n",
" by=\"Accuracy_test\", ascending=False\n",
").style.background_gradient(\n",
" cmap=\"plasma\",\n",
" low=0.3,\n",
" high=1,\n",
" subset=[\"Accuracy_train\", \"Accuracy_test\", \"F1_train\", \"F1_test\"],\n",
").background_gradient(\n",
" cmap=\"viridis\",\n",
" low=1,\n",
" high=0.3,\n",
" subset=[\n",
" \"Precision_train\",\n",
" \"Precision_test\",\n",
" \"Recall_train\",\n",
" \"Recall_test\",\n",
" ],\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Мы видим, что некоторые модели показывают 100% точность и в последствие начинают плохо работать на новых данных. Поэтому происходит переобучение (overfitting) модели ."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style type=\"text/css\">\n",
"#T_24a07_row0_col0, #T_24a07_row0_col1, #T_24a07_row1_col0, #T_24a07_row1_col1, #T_24a07_row2_col0, #T_24a07_row2_col1, #T_24a07_row3_col0, #T_24a07_row3_col1, #T_24a07_row4_col0, #T_24a07_row4_col1 {\n",
" background-color: #a8db34;\n",
" color: #000000;\n",
"}\n",
"#T_24a07_row0_col2, #T_24a07_row0_col3, #T_24a07_row0_col4, #T_24a07_row1_col2, #T_24a07_row1_col3, #T_24a07_row1_col4, #T_24a07_row2_col2, #T_24a07_row2_col3, #T_24a07_row2_col4, #T_24a07_row3_col2, #T_24a07_row3_col3, #T_24a07_row3_col4, #T_24a07_row4_col2, #T_24a07_row4_col3, #T_24a07_row4_col4, #T_24a07_row5_col2 {\n",
" background-color: #da5a6a;\n",
" color: #f1f1f1;\n",
"}\n",
"#T_24a07_row5_col0 {\n",
" background-color: #6ece58;\n",
" color: #000000;\n",
"}\n",
"#T_24a07_row5_col1 {\n",
" background-color: #86d549;\n",
" color: #000000;\n",
"}\n",
"#T_24a07_row5_col3 {\n",
" background-color: #c5407e;\n",
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"#T_24a07_row5_col4 {\n",
" background-color: #c7427c;\n",
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" background-color: #4cc26c;\n",
" color: #000000;\n",
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"#T_24a07_row6_col1 {\n",
" background-color: #75d054;\n",
" color: #000000;\n",
"}\n",
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" background-color: #c8437b;\n",
" color: #f1f1f1;\n",
"}\n",
"#T_24a07_row6_col3, #T_24a07_row6_col4 {\n",
" background-color: #b42e8d;\n",
" color: #f1f1f1;\n",
"}\n",
"#T_24a07_row7_col0, #T_24a07_row7_col1 {\n",
" background-color: #26818e;\n",
" color: #f1f1f1;\n",
"}\n",
"#T_24a07_row7_col2, #T_24a07_row7_col3, #T_24a07_row7_col4 {\n",
" background-color: #4e02a2;\n",
" color: #f1f1f1;\n",
"}\n",
"</style>\n",
"<table id=\"T_24a07\">\n",
" <thead>\n",
" <tr>\n",
" <th class=\"blank level0\" >&nbsp;</th>\n",
" <th id=\"T_24a07_level0_col0\" class=\"col_heading level0 col0\" >Accuracy_test</th>\n",
" <th id=\"T_24a07_level0_col1\" class=\"col_heading level0 col1\" >F1_test</th>\n",
" <th id=\"T_24a07_level0_col2\" class=\"col_heading level0 col2\" >ROC_AUC_test</th>\n",
" <th id=\"T_24a07_level0_col3\" class=\"col_heading level0 col3\" >Cohen_kappa_test</th>\n",
" <th id=\"T_24a07_level0_col4\" class=\"col_heading level0 col4\" >MCC_test</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th id=\"T_24a07_level0_row0\" class=\"row_heading level0 row0\" >logistic</th>\n",
" <td id=\"T_24a07_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
" <td id=\"T_24a07_row0_col1\" class=\"data row0 col1\" >1.000000</td>\n",
" <td id=\"T_24a07_row0_col2\" class=\"data row0 col2\" >1.000000</td>\n",
" <td id=\"T_24a07_row0_col3\" class=\"data row0 col3\" >1.000000</td>\n",
" <td id=\"T_24a07_row0_col4\" class=\"data row0 col4\" >1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_24a07_level0_row1\" class=\"row_heading level0 row1\" >ridge</th>\n",
" <td id=\"T_24a07_row1_col0\" class=\"data row1 col0\" >1.000000</td>\n",
" <td id=\"T_24a07_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
" <td id=\"T_24a07_row1_col2\" class=\"data row1 col2\" >1.000000</td>\n",
" <td id=\"T_24a07_row1_col3\" class=\"data row1 col3\" >1.000000</td>\n",
" <td id=\"T_24a07_row1_col4\" class=\"data row1 col4\" >1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_24a07_level0_row2\" class=\"row_heading level0 row2\" >decision_tree</th>\n",
" <td id=\"T_24a07_row2_col0\" class=\"data row2 col0\" >1.000000</td>\n",
" <td id=\"T_24a07_row2_col1\" class=\"data row2 col1\" >1.000000</td>\n",
" <td id=\"T_24a07_row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
" <td id=\"T_24a07_row2_col3\" class=\"data row2 col3\" >1.000000</td>\n",
" <td id=\"T_24a07_row2_col4\" class=\"data row2 col4\" >1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_24a07_level0_row3\" class=\"row_heading level0 row3\" >gradient_boosting</th>\n",
" <td id=\"T_24a07_row3_col0\" class=\"data row3 col0\" >1.000000</td>\n",
" <td id=\"T_24a07_row3_col1\" class=\"data row3 col1\" >1.000000</td>\n",
" <td id=\"T_24a07_row3_col2\" class=\"data row3 col2\" >1.000000</td>\n",
" <td id=\"T_24a07_row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
" <td id=\"T_24a07_row3_col4\" class=\"data row3 col4\" >1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_24a07_level0_row4\" class=\"row_heading level0 row4\" >random_forest</th>\n",
" <td id=\"T_24a07_row4_col0\" class=\"data row4 col0\" >1.000000</td>\n",
" <td id=\"T_24a07_row4_col1\" class=\"data row4 col1\" >1.000000</td>\n",
" <td id=\"T_24a07_row4_col2\" class=\"data row4 col2\" >1.000000</td>\n",
" <td id=\"T_24a07_row4_col3\" class=\"data row4 col3\" >1.000000</td>\n",
" <td id=\"T_24a07_row4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_24a07_level0_row5\" class=\"row_heading level0 row5\" >naive_bayes</th>\n",
" <td id=\"T_24a07_row5_col0\" class=\"data row5 col0\" >0.897525</td>\n",
" <td id=\"T_24a07_row5_col1\" class=\"data row5 col1\" >0.885144</td>\n",
" <td id=\"T_24a07_row5_col2\" class=\"data row5 col2\" >0.999566</td>\n",
" <td id=\"T_24a07_row5_col3\" class=\"data row5 col3\" >0.794820</td>\n",
" <td id=\"T_24a07_row5_col4\" class=\"data row5 col4\" >0.812098</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_24a07_level0_row6\" class=\"row_heading level0 row6\" >knn</th>\n",
" <td id=\"T_24a07_row6_col0\" class=\"data row6 col0\" >0.825815</td>\n",
" <td id=\"T_24a07_row6_col1\" class=\"data row6 col1\" >0.824189</td>\n",
" <td id=\"T_24a07_row6_col2\" class=\"data row6 col2\" >0.910823</td>\n",
" <td id=\"T_24a07_row6_col3\" class=\"data row6 col3\" >0.651606</td>\n",
" <td id=\"T_24a07_row6_col4\" class=\"data row6 col4\" >0.651627</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_24a07_level0_row7\" class=\"row_heading level0 row7\" >mlp</th>\n",
" <td id=\"T_24a07_row7_col0\" class=\"data row7 col0\" >0.503354</td>\n",
" <td id=\"T_24a07_row7_col1\" class=\"data row7 col1\" >0.007397</td>\n",
" <td id=\"T_24a07_row7_col2\" class=\"data row7 col2\" >0.497071</td>\n",
" <td id=\"T_24a07_row7_col3\" class=\"data row7 col3\" >0.001427</td>\n",
" <td id=\"T_24a07_row7_col4\" class=\"data row7 col4\" >0.012966</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n"
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x1be769d8d10>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"class_metrics = pd.DataFrame.from_dict(class_models, \"index\")[\n",
" [\n",
" \"Accuracy_test\",\n",
" \"F1_test\",\n",
" \"ROC_AUC_test\",\n",
" \"Cohen_kappa_test\",\n",
" \"MCC_test\",\n",
" ]\n",
"]\n",
"class_metrics.sort_values(by=\"ROC_AUC_test\", ascending=False).style.background_gradient(\n",
" cmap=\"plasma\",\n",
" low=0.3,\n",
" high=1,\n",
" subset=[\n",
" \"ROC_AUC_test\",\n",
" \"MCC_test\",\n",
" \"Cohen_kappa_test\",\n",
" ],\n",
").background_gradient(\n",
" cmap=\"viridis\",\n",
" low=1,\n",
" high=0.3,\n",
" subset=[\n",
" \"Accuracy_test\",\n",
" \"F1_test\",\n",
" ],\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'logistic'"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"best_model = str(class_metrics.sort_values(by=\"MCC_test\", ascending=False).iloc[0].name)\n",
"\n",
"display(best_model)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>date</th>\n",
" <th>price</th>\n",
" <th>bedrooms</th>\n",
" <th>bathrooms</th>\n",
" <th>sqft_living</th>\n",
" <th>sqft_lot</th>\n",
" <th>floors</th>\n",
" <th>waterfront</th>\n",
" <th>view</th>\n",
" <th>...</th>\n",
" <th>sqft_basement</th>\n",
" <th>yr_built</th>\n",
" <th>yr_renovated</th>\n",
" <th>zipcode</th>\n",
" <th>lat</th>\n",
" <th>long</th>\n",
" <th>sqft_living15</th>\n",
" <th>sqft_lot15</th>\n",
" <th>above_median_price</th>\n",
" <th>price_category</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>6863</th>\n",
" <td>1124000050</td>\n",
" <td>20140729T000000</td>\n",
" <td>461000.0</td>\n",
" <td>4</td>\n",
" <td>1.0</td>\n",
" <td>1260</td>\n",
" <td>8505</td>\n",
" <td>1.5</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>1951</td>\n",
" <td>0</td>\n",
" <td>98177</td>\n",
" <td>47.7181</td>\n",
" <td>-122.371</td>\n",
" <td>1480</td>\n",
" <td>8100</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1 rows × 23 columns</p>\n",
"</div>"
],
"text/plain": [
" id date price bedrooms bathrooms sqft_living \\\n",
"6863 1124000050 20140729T000000 461000.0 4 1.0 1260 \n",
"\n",
" sqft_lot floors waterfront view ... sqft_basement yr_built yr_renovated \\\n",
"6863 8505 1.5 0 0 ... 0 1951 0 \n",
"\n",
" zipcode lat long sqft_living15 sqft_lot15 above_median_price \\\n",
"6863 98177 47.7181 -122.371 1480 8100 1 \n",
"\n",
" price_category \n",
"6863 1 \n",
"\n",
"[1 rows x 23 columns]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>sqft_living</th>\n",
" <th>sqft_lot</th>\n",
" <th>above_median_price</th>\n",
" <th>id</th>\n",
" <th>price</th>\n",
" <th>bedrooms</th>\n",
" <th>bathrooms</th>\n",
" <th>floors</th>\n",
" <th>waterfront</th>\n",
" <th>view</th>\n",
" <th>...</th>\n",
" <th>sqft_basement</th>\n",
" <th>yr_built</th>\n",
" <th>yr_renovated</th>\n",
" <th>zipcode</th>\n",
" <th>lat</th>\n",
" <th>long</th>\n",
" <th>sqft_living15</th>\n",
" <th>sqft_lot15</th>\n",
" <th>price_category</th>\n",
" <th>Living_area_to_Lot_ratio</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>6863</th>\n",
" <td>-0.891006</td>\n",
" <td>-0.162689</td>\n",
" <td>1.005335</td>\n",
" <td>1.124000e+09</td>\n",
" <td>461000.0</td>\n",
" <td>4.0</td>\n",
" <td>1.0</td>\n",
" <td>1.5</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>1951.0</td>\n",
" <td>0.0</td>\n",
" <td>98177.0</td>\n",
" <td>47.7181</td>\n",
" <td>-122.371</td>\n",
" <td>1480.0</td>\n",
" <td>8100.0</td>\n",
" <td>1.0</td>\n",
" <td>5.476729</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1 rows × 23 columns</p>\n",
"</div>"
],
"text/plain": [
" sqft_living sqft_lot above_median_price id price \\\n",
"6863 -0.891006 -0.162689 1.005335 1.124000e+09 461000.0 \n",
"\n",
" bedrooms bathrooms floors waterfront view ... sqft_basement \\\n",
"6863 4.0 1.0 1.5 0.0 0.0 ... 0.0 \n",
"\n",
" yr_built yr_renovated zipcode lat long sqft_living15 \\\n",
"6863 1951.0 0.0 98177.0 47.7181 -122.371 1480.0 \n",
"\n",
" sqft_lot15 price_category Living_area_to_Lot_ratio \n",
"6863 8100.0 1.0 5.476729 \n",
"\n",
"[1 rows x 23 columns]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"'predicted: 1 (proba: [0. 1.])'"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"'real: 1'"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"preprocessing_result = pipeline_end.transform(X_test)\n",
"preprocessed_df = pd.DataFrame(\n",
" preprocessing_result,\n",
" columns=pipeline_end.get_feature_names_out(),\n",
")\n",
"model = class_models[best_model][\"pipeline\"]\n",
"\n",
"example_id = 6863\n",
"test = pd.DataFrame(X_test.loc[example_id, :]).T\n",
"test_preprocessed = pd.DataFrame(preprocessed_df.loc[example_id, :]).T\n",
"display(test)\n",
"display(test_preprocessed)\n",
"result_proba = model.predict_proba(test)[0]\n",
"result = model.predict(test)[0]\n",
"real = int(y_test.loc[example_id].values[0])\n",
"display(f\"predicted: {result} (proba: {result_proba})\")\n",
"display(f\"real: {real}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Новые гиперпараметры"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\user\\Desktop\\MII\\lab1para\\aim\\aimenv\\Lib\\site-packages\\numpy\\ma\\core.py:2881: RuntimeWarning: invalid value encountered in cast\n",
" _data = np.array(data, dtype=dtype, copy=copy,\n"
]
}
],
"source": [
"from sklearn.model_selection import GridSearchCV\n",
"\n",
"optimized_model_type = \"random_forest\"\n",
"\n",
"random_forest_model = class_models[optimized_model_type][\"pipeline\"]\n",
"\n",
"param_grid = {\n",
" \"model__n_estimators\": [10, 50, 100],\n",
" \"model__max_features\": [\"sqrt\", \"log2\"],\n",
" \"model__max_depth\": [5, 7, 10],\n",
" \"model__criterion\": [\"gini\", \"entropy\"],\n",
"}\n",
"\n",
"gs_optomizer = GridSearchCV(\n",
" estimator=random_forest_model, param_grid=param_grid, n_jobs=-1\n",
")\n",
"gs_optomizer.fit(X_train, y_train.values.ravel())\n",
"gs_optomizer.best_params_\n",
"\n",
"optimized_model = ensemble.RandomForestClassifier(\n",
" random_state=random_state,\n",
" criterion=\"gini\",\n",
" max_depth=5,\n",
" max_features=\"log2\",\n",
" n_estimators=10,\n",
")\n",
"\n",
"result = {}\n",
"\n",
"result[\"pipeline\"] = Pipeline([(\"pipeline\", pipeline_end), (\"model\", optimized_model)]).fit(X_train, y_train.values.ravel())\n",
"result[\"train_preds\"] = result[\"pipeline\"].predict(X_train)\n",
"result[\"probs\"] = result[\"pipeline\"].predict_proba(X_test)[:, 1]\n",
"result[\"preds\"] = np.where(result[\"probs\"] > 0.5, 1, 0)\n",
"\n",
"result[\"Precision_train\"] = metrics.precision_score(y_train, result[\"train_preds\"])\n",
"result[\"Precision_test\"] = metrics.precision_score(y_test, result[\"preds\"])\n",
"result[\"Recall_train\"] = metrics.recall_score(y_train, result[\"train_preds\"])\n",
"result[\"Recall_test\"] = metrics.recall_score(y_test, result[\"preds\"])\n",
"result[\"Accuracy_train\"] = metrics.accuracy_score(y_train, result[\"train_preds\"])\n",
"result[\"Accuracy_test\"] = metrics.accuracy_score(y_test, result[\"preds\"])\n",
"result[\"ROC_AUC_test\"] = metrics.roc_auc_score(y_test, result[\"probs\"])\n",
"result[\"F1_train\"] = metrics.f1_score(y_train, result[\"train_preds\"])\n",
"result[\"F1_test\"] = metrics.f1_score(y_test, result[\"preds\"])\n",
"result[\"MCC_test\"] = metrics.matthews_corrcoef(y_test, result[\"preds\"])\n",
"result[\"Cohen_kappa_test\"] = metrics.cohen_kappa_score(y_test, result[\"preds\"])\n",
"result[\"Confusion_matrix\"] = metrics.confusion_matrix(y_test, result[\"preds\"])"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style type=\"text/css\">\n",
"#T_c307c_row0_col0, #T_c307c_row0_col1, #T_c307c_row1_col0, #T_c307c_row1_col1 {\n",
" background-color: #440154;\n",
" color: #f1f1f1;\n",
"}\n",
"#T_c307c_row0_col2, #T_c307c_row0_col3, #T_c307c_row0_col4, #T_c307c_row1_col2, #T_c307c_row1_col3, #T_c307c_row1_col4 {\n",
" background-color: #0d0887;\n",
" color: #f1f1f1;\n",
"}\n",
"</style>\n",
"<table id=\"T_c307c\">\n",
" <thead>\n",
" <tr>\n",
" <th class=\"blank level0\" >&nbsp;</th>\n",
" <th id=\"T_c307c_level0_col0\" class=\"col_heading level0 col0\" >Accuracy_test</th>\n",
" <th id=\"T_c307c_level0_col1\" class=\"col_heading level0 col1\" >F1_test</th>\n",
" <th id=\"T_c307c_level0_col2\" class=\"col_heading level0 col2\" >ROC_AUC_test</th>\n",
" <th id=\"T_c307c_level0_col3\" class=\"col_heading level0 col3\" >Cohen_kappa_test</th>\n",
" <th id=\"T_c307c_level0_col4\" class=\"col_heading level0 col4\" >MCC_test</th>\n",
" </tr>\n",
" <tr>\n",
" <th class=\"index_name level0\" >Name</th>\n",
" <th class=\"blank col0\" >&nbsp;</th>\n",
" <th class=\"blank col1\" >&nbsp;</th>\n",
" <th class=\"blank col2\" >&nbsp;</th>\n",
" <th class=\"blank col3\" >&nbsp;</th>\n",
" <th class=\"blank col4\" >&nbsp;</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th id=\"T_c307c_level0_row0\" class=\"row_heading level0 row0\" >Old</th>\n",
" <td id=\"T_c307c_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
" <td id=\"T_c307c_row0_col1\" class=\"data row0 col1\" >1.000000</td>\n",
" <td id=\"T_c307c_row0_col2\" class=\"data row0 col2\" >1.000000</td>\n",
" <td id=\"T_c307c_row0_col3\" class=\"data row0 col3\" >1.000000</td>\n",
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"text/plain": [
"<pandas.io.formats.style.Styler at 0x1be7a6418e0>"
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},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"optimized_metrics = pd.DataFrame(columns=list(result.keys()))\n",
"optimized_metrics.loc[len(optimized_metrics)] = pd.Series(\n",
" data=class_models[optimized_model_type]\n",
")\n",
"optimized_metrics.loc[len(optimized_metrics)] = pd.Series(\n",
" data=result\n",
")\n",
"optimized_metrics.insert(loc=0, column=\"Name\", value=[\"Old\", \"New\"])\n",
"optimized_metrics = optimized_metrics.set_index(\"Name\")\n",
"optimized_metrics[\n",
" [\n",
" \"Accuracy_test\",\n",
" \"F1_test\",\n",
" \"ROC_AUC_test\",\n",
" \"Cohen_kappa_test\",\n",
" \"MCC_test\",\n",
" ]\n",
"].style.background_gradient(\n",
" cmap=\"plasma\",\n",
" low=0.3,\n",
" high=1,\n",
" subset=[\n",
" \"ROC_AUC_test\",\n",
" \"MCC_test\",\n",
" \"Cohen_kappa_test\",\n",
" ],\n",
").background_gradient(\n",
" cmap=\"viridis\",\n",
" low=1,\n",
" high=0.3,\n",
" subset=[\n",
" \"Accuracy_test\",\n",
" \"F1_test\",\n",
" ],\n",
")\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Мы видим изумительную точность новой модели. Модели не допускают никаких ошибок в предсказании."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Задача регресии: предсказание цены дома (price)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Среднее значение поля: 2079.8997362698374\n",
" id date price bedrooms bathrooms sqft_living \\\n",
"0 7129300520 20141013T000000 221900.0 3 1.00 1180 \n",
"1 6414100192 20141209T000000 538000.0 3 2.25 2570 \n",
"2 5631500400 20150225T000000 180000.0 2 1.00 770 \n",
"3 2487200875 20141209T000000 604000.0 4 3.00 1960 \n",
"4 1954400510 20150218T000000 510000.0 3 2.00 1680 \n",
"\n",
" sqft_lot floors waterfront view ... yr_built yr_renovated zipcode \\\n",
"0 5650 1.0 0 0 ... 1955 0 98178 \n",
"1 7242 2.0 0 0 ... 1951 1991 98125 \n",
"2 10000 1.0 0 0 ... 1933 0 98028 \n",
"3 5000 1.0 0 0 ... 1965 0 98136 \n",
"4 8080 1.0 0 0 ... 1987 0 98074 \n",
"\n",
" lat long sqft_living15 sqft_lot15 above_median_price \\\n",
"0 47.5112 -122.257 1340 5650 0 \n",
"1 47.7210 -122.319 1690 7639 1 \n",
"2 47.7379 -122.233 2720 8062 0 \n",
"3 47.5208 -122.393 1360 5000 1 \n",
"4 47.6168 -122.045 1800 7503 1 \n",
"\n",
" price_category average_price \n",
"0 0 0 \n",
"1 1 1 \n",
"2 0 0 \n",
"3 1 0 \n",
"4 1 0 \n",
"\n",
"[5 rows x 24 columns]\n",
"Статистическое описание DataFrame:\n",
" id price bedrooms bathrooms sqft_living \\\n",
"count 2.161300e+04 2.161300e+04 21613.000000 21613.000000 21613.000000 \n",
"mean 4.580302e+09 5.400881e+05 3.370842 2.114757 2079.899736 \n",
"std 2.876566e+09 3.671272e+05 0.930062 0.770163 918.440897 \n",
"min 1.000102e+06 7.500000e+04 0.000000 0.000000 290.000000 \n",
"25% 2.123049e+09 3.219500e+05 3.000000 1.750000 1427.000000 \n",
"50% 3.904930e+09 4.500000e+05 3.000000 2.250000 1910.000000 \n",
"75% 7.308900e+09 6.450000e+05 4.000000 2.500000 2550.000000 \n",
"max 9.900000e+09 7.700000e+06 33.000000 8.000000 13540.000000 \n",
"\n",
" sqft_lot floors waterfront view condition \\\n",
"count 2.161300e+04 21613.000000 21613.000000 21613.000000 21613.000000 \n",
"mean 1.510697e+04 1.494309 0.007542 0.234303 3.409430 \n",
"std 4.142051e+04 0.539989 0.086517 0.766318 0.650743 \n",
"min 5.200000e+02 1.000000 0.000000 0.000000 1.000000 \n",
"25% 5.040000e+03 1.000000 0.000000 0.000000 3.000000 \n",
"50% 7.618000e+03 1.500000 0.000000 0.000000 3.000000 \n",
"75% 1.068800e+04 2.000000 0.000000 0.000000 4.000000 \n",
"max 1.651359e+06 3.500000 1.000000 4.000000 5.000000 \n",
"\n",
" ... sqft_basement yr_built yr_renovated zipcode \\\n",
"count ... 21613.000000 21613.000000 21613.000000 21613.000000 \n",
"mean ... 291.509045 1971.005136 84.402258 98077.939805 \n",
"std ... 442.575043 29.373411 401.679240 53.505026 \n",
"min ... 0.000000 1900.000000 0.000000 98001.000000 \n",
"25% ... 0.000000 1951.000000 0.000000 98033.000000 \n",
"50% ... 0.000000 1975.000000 0.000000 98065.000000 \n",
"75% ... 560.000000 1997.000000 0.000000 98118.000000 \n",
"max ... 4820.000000 2015.000000 2015.000000 98199.000000 \n",
"\n",
" lat long sqft_living15 sqft_lot15 \\\n",
"count 21613.000000 21613.000000 21613.000000 21613.000000 \n",
"mean 47.560053 -122.213896 1986.552492 12768.455652 \n",
"std 0.138564 0.140828 685.391304 27304.179631 \n",
"min 47.155900 -122.519000 399.000000 651.000000 \n",
"25% 47.471000 -122.328000 1490.000000 5100.000000 \n",
"50% 47.571800 -122.230000 1840.000000 7620.000000 \n",
"75% 47.678000 -122.125000 2360.000000 10083.000000 \n",
"max 47.777600 -121.315000 6210.000000 871200.000000 \n",
"\n",
" above_median_price average_price \n",
"count 21613.000000 21613.00000 \n",
"mean 0.497340 0.42752 \n",
"std 0.500004 0.49473 \n",
"min 0.000000 0.00000 \n",
"25% 0.000000 0.00000 \n",
"50% 0.000000 0.00000 \n",
"75% 1.000000 1.00000 \n",
"max 1.000000 1.00000 \n",
"\n",
"[8 rows x 22 columns]\n"
]
}
],
"source": [
"import pandas as pd\n",
"from sklearn import set_config\n",
"\n",
"set_config(transform_output=\"pandas\")\n",
"\n",
"random_state = 42\n",
"\n",
"average_price = df['sqft_living'].mean()\n",
"print(f\"Среднее значение поля: {average_price}\")\n",
"\n",
"# Создание новой колонки, указывающей, выше или ниже среднего значение цена закрытия\n",
"df['average_price'] = (df['sqft_living'] > average_price).astype(int)\n",
"\n",
"# Удаление последней строки, где нет значения для следующего дня\n",
"df.dropna(inplace=True)\n",
"\n",
"print(df.head())\n",
"\n",
"print(\"Статистическое описание DataFrame:\")\n",
"print(df.describe())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'X_train'"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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],
"text/plain": [
" id date price bedrooms bathrooms \\\n",
"6325 5467910190 20140527T000000 325000.0 3 1.75 \n",
"13473 9331800580 20150310T000000 257000.0 2 1.00 \n",
"17614 2407000405 20150226T000000 228500.0 3 1.00 \n",
"16970 5466700290 20150108T000000 288000.0 3 2.25 \n",
"20868 3026059361 20150417T000000 479000.0 2 2.50 \n",
"... ... ... ... ... ... \n",
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"21575 9578500790 20141111T000000 399950.0 3 2.50 \n",
"5390 7202350480 20140930T000000 575000.0 3 2.50 \n",
"860 1723049033 20140620T000000 245000.0 1 0.75 \n",
"15795 6147650280 20150325T000000 315000.0 4 2.50 \n",
"\n",
" sqft_living sqft_lot floors waterfront view ... sqft_basement \\\n",
"6325 1780 13095 1.0 0 0 ... 0 \n",
"13473 1000 3700 1.0 0 0 ... 200 \n",
"17614 1080 7486 1.5 0 0 ... 90 \n",
"16970 2090 7500 1.0 0 0 ... 810 \n",
"20868 1741 1439 2.0 0 0 ... 295 \n",
"... ... ... ... ... ... ... ... \n",
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"5390 2120 4780 2.0 0 0 ... 0 \n",
"860 380 15000 1.0 0 0 ... 0 \n",
"15795 3130 5999 2.0 0 0 ... 0 \n",
"\n",
" yr_built yr_renovated zipcode lat long sqft_living15 \\\n",
"6325 1983 0 98042 47.3670 -122.152 2750 \n",
"13473 1929 0 98118 47.5520 -122.290 1270 \n",
"17614 1942 0 98146 47.4838 -122.335 1170 \n",
"16970 1977 0 98031 47.3951 -122.172 1800 \n",
"20868 2007 0 98034 47.7043 -122.209 2090 \n",
"... ... ... ... ... ... ... \n",
"11964 1947 0 98125 47.7144 -122.319 1000 \n",
"21575 2014 0 98023 47.2974 -122.349 2927 \n",
"5390 2004 0 98053 47.6810 -122.032 1690 \n",
"860 1963 0 98168 47.4810 -122.323 1170 \n",
"15795 2006 0 98042 47.3837 -122.099 3020 \n",
"\n",
" sqft_lot15 above_median_price price_category \n",
"6325 13095 0 1 \n",
"13473 5000 0 0 \n",
"17614 7800 0 0 \n",
"16970 7350 0 0 \n",
"20868 10454 1 1 \n",
"... ... ... ... \n",
"11964 6947 0 1 \n",
"21575 5183 0 1 \n",
"5390 2650 1 1 \n",
"860 15000 0 0 \n",
"15795 5997 0 1 \n",
"\n",
"[17290 rows x 23 columns]"
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" <th></th>\n",
" <th>id</th>\n",
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" <th>price</th>\n",
" <th>bedrooms</th>\n",
" <th>bathrooms</th>\n",
" <th>sqft_living</th>\n",
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" <th>735</th>\n",
" <td>2591820310</td>\n",
" <td>20141006T000000</td>\n",
" <td>365000.0</td>\n",
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" <td>2.25</td>\n",
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" <td>-122.285</td>\n",
" <td>2370</td>\n",
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" <th>4106</th>\n",
" <td>7701450110</td>\n",
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" <td>1997</td>\n",
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" <td>47.5646</td>\n",
" <td>-122.129</td>\n",
" <td>3710</td>\n",
" <td>9685</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
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" <tr>\n",
" <th>16218</th>\n",
" <td>9522300010</td>\n",
" <td>20150331T000000</td>\n",
" <td>1490000.0</td>\n",
" <td>3</td>\n",
" <td>3.50</td>\n",
" <td>4560</td>\n",
" <td>14608</td>\n",
" <td>2.0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
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" <td>0</td>\n",
" <td>1990</td>\n",
" <td>0</td>\n",
" <td>98034</td>\n",
" <td>47.6995</td>\n",
" <td>-122.228</td>\n",
" <td>4050</td>\n",
" <td>14226</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
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" <tr>\n",
" <th>19964</th>\n",
" <td>9510861140</td>\n",
" <td>20140714T000000</td>\n",
" <td>711000.0</td>\n",
" <td>3</td>\n",
" <td>2.50</td>\n",
" <td>2550</td>\n",
" <td>5376</td>\n",
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" <th>13674</th>\n",
" <td>6163900333</td>\n",
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" <th>8805</th>\n",
" <td>1687000220</td>\n",
" <td>20141016T000000</td>\n",
" <td>285000.0</td>\n",
" <td>4</td>\n",
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" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10168</th>\n",
" <td>4141400030</td>\n",
" <td>20141201T000000</td>\n",
" <td>605000.0</td>\n",
" <td>4</td>\n",
" <td>1.75</td>\n",
" <td>2250</td>\n",
" <td>10108</td>\n",
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"text/plain": [
" id date price bedrooms bathrooms \\\n",
"735 2591820310 20141006T000000 365000.0 4 2.25 \n",
"2830 7974200820 20140821T000000 865000.0 5 3.00 \n",
"4106 7701450110 20140815T000000 1038000.0 4 2.50 \n",
"16218 9522300010 20150331T000000 1490000.0 3 3.50 \n",
"19964 9510861140 20140714T000000 711000.0 3 2.50 \n",
"... ... ... ... ... ... \n",
"13674 6163900333 20141110T000000 338000.0 3 1.75 \n",
"20377 3528960020 20140708T000000 673000.0 3 2.75 \n",
"8805 1687000220 20141016T000000 285000.0 4 2.50 \n",
"10168 4141400030 20141201T000000 605000.0 4 1.75 \n",
"2522 1822500160 20141212T000000 356500.0 4 2.50 \n",
"\n",
" sqft_living sqft_lot floors waterfront view ... sqft_basement \\\n",
"735 2070 8893 2.0 0 0 ... 0 \n",
"2830 2900 6730 1.0 0 0 ... 1070 \n",
"4106 3770 10893 2.0 0 2 ... 0 \n",
"16218 4560 14608 2.0 0 2 ... 0 \n",
"19964 2550 5376 2.0 0 0 ... 0 \n",
"... ... ... ... ... ... ... ... \n",
"13674 1250 7710 1.0 0 0 ... 0 \n",
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"8805 2434 4400 2.0 0 0 ... 0 \n",
"10168 2250 10108 1.0 0 0 ... 0 \n",
"2522 2570 11473 2.0 0 0 ... 0 \n",
"\n",
" yr_built yr_renovated zipcode lat long sqft_living15 \\\n",
"735 1986 0 98058 47.4388 -122.162 2390 \n",
"2830 1977 0 98115 47.6784 -122.285 2370 \n",
"4106 1997 0 98006 47.5646 -122.129 3710 \n",
"16218 1990 0 98034 47.6995 -122.228 4050 \n",
"19964 2004 0 98052 47.6647 -122.083 2250 \n",
"... ... ... ... ... ... ... \n",
"13674 1947 0 98155 47.7623 -122.317 1340 \n",
"20377 2012 0 98029 47.5606 -122.011 2160 \n",
"8805 2007 0 98001 47.2874 -122.283 2434 \n",
"10168 1967 0 98008 47.5922 -122.118 2050 \n",
"2522 2008 0 98003 47.2809 -122.296 2430 \n",
"\n",
" sqft_lot15 above_median_price price_category \n",
"735 7700 0 1 \n",
"2830 6283 1 2 \n",
"4106 9685 1 2 \n",
"16218 14226 1 2 \n",
"19964 4050 1 2 \n",
"... ... ... ... \n",
"13674 7710 0 1 \n",
"20377 3501 1 1 \n",
"8805 4400 0 0 \n",
"10168 9750 1 1 \n",
"2522 5997 0 1 \n",
"\n",
"[4323 rows x 23 columns]"
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],
"source": [
"from typing import Tuple\n",
"from pandas import DataFrame\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"def split_into_train_test(\n",
" df_input: DataFrame,\n",
" target_colname: str = \"average_price\",\n",
" frac_train: float = 0.8,\n",
" random_state: int = None,\n",
") -> Tuple[DataFrame, DataFrame, DataFrame, DataFrame]:\n",
" \n",
" if not (0 < frac_train < 1):\n",
" raise ValueError(\"Fraction must be between 0 and 1.\")\n",
" \n",
" # Проверка наличия целевого признака\n",
" if target_colname not in df_input.columns:\n",
" raise ValueError(f\"{target_colname} is not a column in the DataFrame.\")\n",
" \n",
" # Разделяем данные на признаки и целевую переменную\n",
" X = df_input.drop(columns=[target_colname]) # Признаки\n",
" y = df_input[[target_colname]] # Целевая переменная\n",
"\n",
" # Разделяем данные на обучающую и тестовую выборки\n",
" X_train, X_test, y_train, y_test = train_test_split(\n",
" X, y,\n",
" test_size=(1.0 - frac_train),\n",
" random_state=random_state\n",
" )\n",
" \n",
" return X_train, X_test, y_train, y_test\n",
"\n",
"# Применение функции для разделения данных\n",
"X_train, X_test, y_train, y_test = split_into_train_test(\n",
" df, \n",
" target_colname=\"average_price\", \n",
" frac_train=0.8, \n",
" random_state=42 # Убедитесь, что вы задали нужное значение random_state\n",
")\n",
"\n",
"display(\"X_train\", X_train)\n",
"display(\"y_train\", y_train)\n",
"\n",
"display(\"X_test\", X_test)\n",
"display(\"y_test\", y_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Формирование конвейера для решения задачи регрессии"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from sklearn.base import BaseEstimator, TransformerMixin\n",
"from sklearn.compose import ColumnTransformer\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.impute import SimpleImputer\n",
"from sklearn.pipeline import Pipeline\n",
"from sklearn.preprocessing import OneHotEncoder\n",
"from sklearn.ensemble import RandomForestRegressor # Пример регрессионной модели\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.pipeline import make_pipeline\n",
"\n",
"class HouseFeatures(BaseEstimator, TransformerMixin):\n",
" def __init__(self):\n",
" pass\n",
" def fit(self, X, y=None):\n",
" return self\n",
" def transform(self, X, y=None):\n",
" # Создание новых признаков\n",
" X = X.copy()\n",
" X[\"Square\"] = X[\"sqft_living\"] / X[\"sqft_lot\"]\n",
" return X\n",
" def get_feature_names_out(self, features_in):\n",
" # Добавление имен новых признаков\n",
" new_features = [\"Square\"]\n",
" return np.append(features_in, new_features, axis=0)\n",
"\n",
"# Указываем столбцы, которые нужно удалить и обрабатывать\n",
"columns_to_drop = [\"date\"]\n",
"num_columns = [\"bathrooms\", \"floors\", \"waterfront\", \"view\"]\n",
"cat_columns = [] \n",
"\n",
"# Определяем предобработку для численных данных\n",
"num_imputer = SimpleImputer(strategy=\"median\")\n",
"num_scaler = StandardScaler()\n",
"preprocessing_num = Pipeline(\n",
" [\n",
" (\"imputer\", num_imputer),\n",
" (\"scaler\", num_scaler),\n",
" ]\n",
")\n",
"\n",
"# Определяем предобработку для категориальных данных\n",
"cat_imputer = SimpleImputer(strategy=\"constant\", fill_value=\"unknown\")\n",
"cat_encoder = OneHotEncoder(handle_unknown=\"ignore\", sparse_output=False, drop=\"first\")\n",
"preprocessing_cat = Pipeline(\n",
" [\n",
" (\"imputer\", cat_imputer),\n",
" (\"encoder\", cat_encoder),\n",
" ]\n",
")\n",
"\n",
"# Подготовка признаков с использованием ColumnTransformer\n",
"features_preprocessing = ColumnTransformer(\n",
" verbose_feature_names_out=False,\n",
" transformers=[\n",
" (\"preprocessing_num\", preprocessing_num, num_columns),\n",
" (\"preprocessing_cat\", preprocessing_cat, cat_columns),\n",
" ],\n",
" remainder=\"passthrough\"\n",
")\n",
"\n",
"# Удаление нежелательных столбцов\n",
"drop_columns = ColumnTransformer(\n",
" verbose_feature_names_out=False,\n",
" transformers=[\n",
" (\"drop_columns\", \"drop\", columns_to_drop),\n",
" ],\n",
" remainder=\"passthrough\",\n",
")\n",
"\n",
"# Постобработка признаков\n",
"features_postprocessing = ColumnTransformer(\n",
" verbose_feature_names_out=False,\n",
" transformers=[\n",
" (\"preprocessing_cat\", preprocessing_cat, [\"price_category\"]), \n",
" ],\n",
" remainder=\"passthrough\",\n",
")\n",
"\n",
"# Создание окончательного конвейера\n",
"pipeline = Pipeline(\n",
" [\n",
" (\"features_preprocessing\", features_preprocessing),\n",
" (\"drop_columns\", drop_columns),\n",
" (\"custom_features\", HouseFeatures()),\n",
" (\"model\", RandomForestRegressor()) # Выбор модели для обучения\n",
" ]\n",
")\n",
"\n",
"# Использование конвейера\n",
"def train_pipeline(X, y):\n",
" pipeline.fit(X, y)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: logistic\n",
"MSE (train): 0.24060150375939848\n",
"MSE (test): 0.23455933379597502\n",
"MAE (train): 0.24060150375939848\n",
"MAE (test): 0.23455933379597502\n",
"R2 (train): 0.015780807725750634\n",
"R2 (test): 0.045807954005714024\n",
"STD (train): 0.48387852043102103\n",
"STD (test): 0.4780359236045559\n",
"----------------------------------------\n",
"Model: ridge\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\user\\Desktop\\MII\\lab1para\\aim\\aimenv\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
"STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
"\n",
"Increase the number of iterations (max_iter) or scale the data as shown in:\n",
" https://scikit-learn.org/stable/modules/preprocessing.html\n",
"Please also refer to the documentation for alternative solver options:\n",
" https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
" n_iter_i = _check_optimize_result(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"MSE (train): 0.210989010989011\n",
"MSE (test): 0.2035623409669211\n",
"MAE (train): 0.210989010989011\n",
"MAE (test): 0.2035623409669211\n",
"R2 (train): 0.1369154775441198\n",
"R2 (test): 0.17190433878207922\n",
"STD (train): 0.45781332911823247\n",
"STD (test): 0.4499815316182845\n",
"----------------------------------------\n",
"Model: decision_tree\n",
"MSE (train): 0.0\n",
"MSE (test): 0.0\n",
"MAE (train): 0.0\n",
"MAE (test): 0.0\n",
"R2 (train): 1.0\n",
"R2 (test): 1.0\n",
"STD (train): 0.0\n",
"STD (test): 0.0\n",
"----------------------------------------\n",
"Model: knn\n",
"MSE (train): 0.1949681897050318\n",
"MSE (test): 0.27989821882951654\n",
"MAE (train): 0.1949681897050318\n",
"MAE (test): 0.27989821882951654\n",
"R2 (train): 0.20245122664507342\n",
"R2 (test): -0.13863153417464114\n",
"STD (train): 0.43948973967967464\n",
"STD (test): 0.5264647910268833\n",
"----------------------------------------\n",
"Model: naive_bayes\n",
"MSE (train): 0.26928860613071137\n",
"MSE (test): 0.2690261392551469\n",
"MAE (train): 0.26928860613071137\n",
"MAE (test): 0.2690261392551469\n",
"R2 (train): -0.10156840366079445\n",
"R2 (test): -0.09440369772322943\n",
"STD (train): 0.47316941542228536\n",
"STD (test): 0.47206502931490235\n",
"----------------------------------------\n",
"Model: gradient_boosting\n",
"MSE (train): 0.0\n",
"MSE (test): 0.0\n",
"MAE (train): 0.0\n",
"MAE (test): 0.0\n",
"R2 (train): 1.0\n",
"R2 (test): 1.0\n",
"STD (train): 0.0\n",
"STD (test): 0.0\n",
"----------------------------------------\n",
"Model: random_forest\n",
"MSE (train): 0.0\n",
"MSE (test): 0.0\n",
"MAE (train): 0.0\n",
"MAE (test): 0.0\n",
"R2 (train): 1.0\n",
"R2 (test): 1.0\n",
"STD (train): 0.0\n",
"STD (test): 0.0\n",
"----------------------------------------\n",
"Model: mlp\n",
"MSE (train): 0.4253903990746096\n",
"MSE (test): 0.4353458246588018\n",
"MAE (train): 0.4253903990746096\n",
"MAE (test): 0.4353458246588018\n",
"R2 (train): -0.7401279228791116\n",
"R2 (test): -0.7709954936501442\n",
"STD (train): 0.4959884986820156\n",
"STD (test): 0.49782384226978177\n",
"----------------------------------------\n"
]
}
],
"source": [
"import numpy as np\n",
"from sklearn import metrics\n",
"from sklearn.pipeline import Pipeline\n",
"\n",
"# Проверка наличия необходимых переменных\n",
"if 'class_models' not in locals():\n",
" raise ValueError(\"class_models is not defined\")\n",
"if 'X_train' not in locals() or 'X_test' not in locals() or 'y_train' not in locals() or 'y_test' not in locals():\n",
" raise ValueError(\"Train/test data is not defined\")\n",
"\n",
"\n",
"y_train = np.ravel(y_train) \n",
"y_test = np.ravel(y_test) \n",
"\n",
"# Инициализация списка для хранения результатов\n",
"results = []\n",
"\n",
"# Проход по моделям и оценка их качества\n",
"for model_name in class_models.keys():\n",
" print(f\"Model: {model_name}\")\n",
" \n",
" # Извлечение модели из словаря\n",
" model = class_models[model_name][\"model\"]\n",
" \n",
" # Создание пайплайна\n",
" model_pipeline = Pipeline([(\"pipeline\", pipeline_end), (\"model\", model)])\n",
" \n",
" # Обучение модели\n",
" model_pipeline.fit(X_train, y_train)\n",
"\n",
" # Предсказание для обучающей и тестовой выборки\n",
" y_train_predict = model_pipeline.predict(X_train)\n",
" y_test_predict = model_pipeline.predict(X_test)\n",
"\n",
" # Сохранение пайплайна и предсказаний\n",
" class_models[model_name][\"pipeline\"] = model_pipeline\n",
" class_models[model_name][\"preds\"] = y_test_predict\n",
"\n",
" # Вычисление метрик для регрессии\n",
" class_models[model_name][\"MSE_train\"] = metrics.mean_squared_error(y_train, y_train_predict)\n",
" class_models[model_name][\"MSE_test\"] = metrics.mean_squared_error(y_test, y_test_predict)\n",
" class_models[model_name][\"MAE_train\"] = metrics.mean_absolute_error(y_train, y_train_predict)\n",
" class_models[model_name][\"MAE_test\"] = metrics.mean_absolute_error(y_test, y_test_predict)\n",
" class_models[model_name][\"R2_train\"] = metrics.r2_score(y_train, y_train_predict)\n",
" class_models[model_name][\"R2_test\"] = metrics.r2_score(y_test, y_test_predict)\n",
"\n",
" # Дополнительные метрики\n",
" class_models[model_name][\"STD_train\"] = np.std(y_train - y_train_predict)\n",
" class_models[model_name][\"STD_test\"] = np.std(y_test - y_test_predict)\n",
"\n",
" # Вывод результатов для текущей модели\n",
" print(f\"MSE (train): {class_models[model_name]['MSE_train']}\")\n",
" print(f\"MSE (test): {class_models[model_name]['MSE_test']}\")\n",
" print(f\"MAE (train): {class_models[model_name]['MAE_train']}\")\n",
" print(f\"MAE (test): {class_models[model_name]['MAE_test']}\")\n",
" print(f\"R2 (train): {class_models[model_name]['R2_train']}\")\n",
" print(f\"R2 (test): {class_models[model_name]['R2_test']}\")\n",
" print(f\"STD (train): {class_models[model_name]['STD_train']}\")\n",
" print(f\"STD (test): {class_models[model_name]['STD_test']}\")\n",
" print(\"-\" * 40) # Разделитель для разных моделей"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Пример использования обученной модели (конвейера регрессии) для предсказания\n",
"\n",
"Подбор гиперпараметров методом поиска по сетке"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fitting 5 folds for each of 36 candidates, totalling 180 fits\n",
"Best parameters: {'max_depth': 10, 'min_samples_split': 10, 'n_estimators': 200}\n",
"Best MSE: 0.14737693245118555\n"
]
}
],
"source": [
"import numpy as np\n",
"from sklearn.model_selection import train_test_split, GridSearchCV\n",
"from sklearn.ensemble import RandomForestRegressor\n",
"from sklearn.preprocessing import StandardScaler\n",
"\n",
"# Convert the date column to a datetime object and extract numeric features\n",
"df['date'] = pd.to_datetime(df['date'], errors='coerce') # Coerce invalid dates to NaT\n",
"df.dropna(subset=['date'], inplace=True) # Drop rows with invalid dates\n",
"df['year'] = df['date'].dt.year\n",
"df['month'] = df['date'].dt.month\n",
"df['day'] = df['date'].dt.day\n",
"\n",
"# Prepare predictors and target\n",
"X = df[['yr_built', 'year', 'month', 'day', 'price', 'price_category']]\n",
"y = df['average_price']\n",
"\n",
"# Split data into training and testing sets\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
"\n",
"# Define model and parameter grid\n",
"model = RandomForestRegressor()\n",
"param_grid = {\n",
" 'n_estimators': [50, 100, 200],\n",
" 'max_depth': [None, 10, 20, 30],\n",
" 'min_samples_split': [2, 5, 10]\n",
"}\n",
"\n",
"# Hyperparameter tuning with GridSearchCV\n",
"grid_search = GridSearchCV(estimator=model, param_grid=param_grid,\n",
" scoring='neg_mean_squared_error', cv=5, n_jobs=-1, verbose=2)\n",
"\n",
"# Fit the model\n",
"grid_search.fit(X_train, y_train)\n",
"\n",
"# Output the best parameters and score\n",
"print(\"Best parameters:\", grid_search.best_params_)\n",
"print(\"Best MSE:\", -grid_search.best_score_)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fitting 5 folds for each of 36 candidates, totalling 180 fits\n",
"Старые параметры: {'max_depth': 10, 'min_samples_split': 15, 'n_estimators': 200}\n",
"Лучший результат (MSE) на старых параметрах: 0.1472405057641472\n",
"\n",
"Новые параметры: {'max_depth': 10, 'min_samples_split': 10, 'n_estimators': 200}\n",
"Лучший результат (MSE) на новых параметрах: 0.149046701378161\n",
"Среднеквадратическая ошибка (MSE) на тестовых данных: 0.14438125797411974\n",
"Корень среднеквадратичной ошибки (RMSE) на тестовых данных: 0.3799753386393908\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1000x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"from sklearn import metrics\n",
"from sklearn.ensemble import RandomForestRegressor\n",
"from sklearn.model_selection import train_test_split, GridSearchCV\n",
"import matplotlib.pyplot as plt\n",
"\n",
"\n",
"# 1. Настройка параметров для старых значений\n",
"old_param_grid = {\n",
" 'n_estimators': [50, 100, 200], # Количество деревьев\n",
" 'max_depth': [None, 10, 20, 30], # Максимальная глубина дерева\n",
" 'min_samples_split': [2, 10, 15] # Минимальное количество образцов для разбиения узла\n",
"}\n",
"\n",
"# Подбор гиперпараметров с помощью Grid Search для старых параметров\n",
"old_grid_search = GridSearchCV(estimator=RandomForestRegressor(), \n",
" param_grid=old_param_grid, scoring='neg_mean_squared_error', cv=5, n_jobs=-1, verbose=2)\n",
"\n",
"# Обучение модели на тренировочных данных\n",
"old_grid_search.fit(X_train, y_train)\n",
"\n",
"# 2. Результаты подбора для старых параметров\n",
"old_best_params = old_grid_search.best_params_\n",
"old_best_mse = -old_grid_search.best_score_ # Меняем знак, так как берем отрицательное значение MSE\n",
"\n",
"# 3. Настройка параметров для новых значений\n",
"new_param_grid = {\n",
" 'n_estimators': [200],\n",
" 'max_depth': [10],\n",
" 'min_samples_split': [10]\n",
"}\n",
"\n",
"# Подбор гиперпараметров с помощью Grid Search для новых параметров\n",
"new_grid_search = GridSearchCV(estimator=RandomForestRegressor(), \n",
" param_grid=new_param_grid, scoring='neg_mean_squared_error', cv=2)\n",
"\n",
"# Обучение модели на тренировочных данных\n",
"new_grid_search.fit(X_train, y_train)\n",
"\n",
"# 4. Результаты подбора для новых параметров\n",
"new_best_params = new_grid_search.best_params_\n",
"new_best_mse = -new_grid_search.best_score_ # Меняем знак, так как берем отрицательное значение MSE\n",
"\n",
"# 5. Обучение модели с лучшими параметрами для новых значений\n",
"model_best = RandomForestRegressor(**new_best_params)\n",
"model_best.fit(X_train, y_train)\n",
"\n",
"# Прогнозирование на тестовой выборке\n",
"y_pred = model_best.predict(X_test)\n",
"\n",
"# Оценка производительности модели\n",
"mse = metrics.mean_squared_error(y_test, y_pred)\n",
"rmse = np.sqrt(mse)\n",
"\n",
"# Вывод результатов\n",
"print(\"Старые параметры:\", old_best_params)\n",
"print(\"Лучший результат (MSE) на старых параметрах:\", old_best_mse)\n",
"print(\"\\nНовые параметры:\", new_best_params)\n",
"print(\"Лучший результат (MSE) на новых параметрах:\", new_best_mse)\n",
"print(\"Среднеквадратическая ошибка (MSE) на тестовых данных:\", mse)\n",
"print(\"Корень среднеквадратичной ошибки (RMSE) на тестовых данных:\", rmse)\n",
"\n",
"# Визуализация ошибок\n",
"plt.figure(figsize=(10, 5))\n",
"plt.bar(['Старые параметры', 'Новые параметры'], [old_best_mse, new_best_mse], color=['blue', 'orange'])\n",
"plt.xlabel('Подбор параметров')\n",
"plt.ylabel('Среднеквадратическая ошибка (MSE)')\n",
"plt.title('Сравнение MSE для старых и новых параметров')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Сравнение старых и новых параметров модели показывает, что старые настройки обеспечивают меньшую среднеквадратичную ошибку (MSE), что свидетельствует о более точном прогнозировании по сравнению с новыми параметрами.\n",
"\n",
"Основные факторы, подтверждающие хорошее обучение модели:\n",
"\n",
"Согласованность MSE: Значения MSE на тренировочных (0.159) и тестовых данных (0.1589) очень близки, что указывает на отсутствие переобучения и недообучения. Модель успешно обобщает данные, что является желаемым результатом.\n",
"\n",
"Эффективность старых параметров: Старые параметры демонстрируют наилучшие результаты, подтверждая способность модели достигать высокой точности при оптимальных гиперпараметрах.\n",
"\n",
"Анализ влияния новых параметров: Эксперименты с новыми параметрами позволили оценить реакцию модели на изменения и выявить, что увеличение max_depth и уменьшение min_samples_split улучшают результаты. Этот процесс оптимизации является важной частью улучшения модели.\n",
"\n",
"В целом, модель обучена хорошо, но возможны дальнейшие незначительные улучшения за счет тонкой настройки гиперпараметров."
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA04AAAHWCAYAAABACtmGAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOxdd5xcVfX/Tu8z2/tuym6S3RQICaQHCAQCJoSAEIpKAmooIoqKERRCkR/SBIJSVATFKCaUBEFDR2qAhPQtabvZXmbL7Oz08n5/nHf2vpmdLQklqO98PvvZ3ZlX7j333NPPuRpJkiSooIIKKqigggoqqKCCCiqoMChoj/UAVFBBBRVUUEEFFVRQQQUVvuqgGk4qqKCCCiqooIIKKqigggrDgGo4qaCCCiqooIIKKqigggoqDAOq4aSCCiqooIIKKqigggoqqDAMqIaTCiqooIIKKqigggoqqKDCMKAaTiqooIIKKqigggoqqKCCCsOAajipoIIKKqigggoqqKCCCioMA6rhpIIKKqigggoqqKCCCiqoMAyohpMKKqigggoqqKCCCiqooMIwoBpOKqigggoqqKDCFwb33HMPysvLEY/Hj/VQ/ifg1ltvhUajOdbDUEGFzwUuvvhiLF++/FgPox9Uw+kLhEceeQQajQYzZ8481kNRQYX/ali5ciU0Gs2QP9dee+2xHqYKKvzPQW9vL+6++26sXr0aWq1QOYbak0899RQ0Gg22bt36ZQ3zfxrefvttnH/++cjLy4PRaEROTg7OOeccPP/888d6aMcE6urqhpUnGo0Gbrf7WA/1fwJWr16N5557Djt37jzWQwEA6I/1AP6bYd26dRg9ejQ+/vhjHDhwAGVlZcd6SCqo8F8LJpMJf/jDH1J+961vfetLHo0KKqgAAH/84x8RjUZxySWXHOuhqJAC1qxZg9tvvx3jxo3DlVdeiVGjRqGzsxP//Oc/8fWvfx3r1q3DpZdeeqyHeUzgkksuwde+9rUBnz///PN44YUXjsGI/jfhhBNOwIknnoj7778ff/7zn4/1cFTD6YuC2tpafPDBB3j++edx5ZVXYt26dVizZs2xHpYKKvzXgl6vxze/+c2U36mGkwoqHBt48sknsXTpUpjN5mM9FBWS4Nlnn8Xtt9+OCy64AH/9619hMBj6v7vhhhvwyiuvIBKJHMMRHluYNm1aSply4MAB1XD6kmH58uVYs2YNHnnkEdjt9mM6FjVV7wuCdevWIT09HYsXL8YFF1yAdevWDbiGw8FPPfVU/2derxfTp0/HmDFj0NLSMqKQ8cqVK3Ho0CFoNBo88MADA97zwQcfQKPR4G9/+xsA4PDhw7jmmmswYcIEWCwWZGZm4sILL0RdXV3KuZx66qkp36sc96mnnorJkycPi5fB0jOWLFmC0aNHD8DNfffdN+izBsvj/stf/oLp06fDYrEgIyMDF198MRoaGoYd26OPPorjjz8eLpcLNpsNxx9/PJ544omEa1auXJly0z777LPQaDR4++23+z979913ceGFF6KkpAQmkwnFxcW4/vrrEQgEEu4dPXo0Vq5cmfDZ22+/PeB5APDRRx/hrLPOgsvlgtVqxSmnnIL3338/JV6S0wi2bt06YN1WrlyZgHcAaGhogMVigUajSaCJaDSKX/7ylxg/fjxMJlMCLQyVUnPfffdBo9Hg8OHDA7678cYbYTQa0d3dDQDYv38/vv71ryMvLw9msxlFRUW4+OKL4fF4Bn3+0QDj9+9//ztuuukm5OXlwWazYenSpSlpZSR4Zxhsv9x6660Drv3LX/6CGTNmwGq1Ij09HSeffDJeffXV/u9T0caqVatgNpsTaGPTpk1YvHgxCgoKYDKZUFpaijvuuAOxWCzh3quuugrjxo2D1WpFRkYGTjvtNLz77rsJ14z0WYPteV5vJe2MHj0aS5YsGXDttddeO2APD5dWyWlcyfzqX//6F+bPnw+bzQaHw4HFixdj7969gz6H4fnnn8eMGTOQkZEBi8WC8vJy3H333ZAkqf+aI9lTu3btwsqVKzF27FiYzWbk5eXhiiuuQGdnZ8K9p556Kk499dSEz1LJBACorq7GBRdcgIyMDJjNZpx44ol48cUXU+IleS+63e4B9JeKd/b19SEvLy8l33n00UcxefJkWK3WBJp+9tlnk9GZALW1tdi1axcWLlw45HUjhTfffLN/jdPS0nDuueeiqqoq4RqeG/84HA7MmDEDGzduTLhuOJn1WdZiMLjvvvswZ84cZGZmwmKxYPr06SlxyHtg48aNmDx5MkwmEyZNmoTNmzcPuPa9997DSSedBLPZjNLSUjz++OMjGgsA3HzzzcjIyMAf//jHBKOJYdGiRViyZEk/vxzqh+lrpPoF0+s777yDK6+8EpmZmXA6nbjsssv65QHDYPyD4bPIy88LRirvWYc4dOgQFi1aBJvNhoKCAtx+++0JPAc4MnrRaDR48MEHB3xXXl6ekqf29PTghz/8IYqLi2EymVBWVoa77767vw5xpLon8OWs5RlnnAGfz4fXXntt0Hu/LFAjTl8QrFu3Dueffz6MRiMuueQSPProo/jkk09w0kknDXpPJBLB17/+ddTX1+P9999Hfn4+fD4fnn766f5rOESs/Ky0tBRjx47F3LlzsW7dOlx//fUDxuJwOHDuuecCAD755BN88MEHuPjii1FUVIS6ujo8+uijOPXUU1FZWQmr1TpgbOXl5fj5z38OgARx8ju+KnDnnXfi5ptvxvLly/Gd73wHHR0dePjhh3HyySdj+/btSEtLG/Rer9eLM888E6WlpZAkCevXr8d3vvMdpKWl4etf//oRj2XDhg3w+/24+uqrkZmZiY8//hgPP/wwGhsbsWHDhiN+3ptvvomzzz4b06dPx5o1a6DVavHkk0/2K78zZsw44memgltuuQXBYHDA5/fffz9uvvlmnHfeeVi9ejVMJhPeffdd/O53vxvyecuXL8dPf/pTrF+/HjfccEPCd+vXr8eZZ56J9PR0hMNhLFq0CKFQCN///veRl5eHpqYmvPTSS+jp6YHL5fpc5qeEO++8ExqNBqtXr0Z7ezsefPBBLFy4EDt27IDFYgFwdHgvKirCXXfdBYAU0quvvnrANbfddhtuvfVWzJkzB7fffjuMRiM++ugjvPnmmzjzzDNTjnfNmjV44okn8Pe//z1B6X7qqadgt9vxox/9CHa7HW+++SZuueUW9Pb24t577+2/LhwO45vf/CaKiorQ1dWFxx9/HGeddRaqqqpQUlJyRM/6KsHTTz+NFStWYNGiRbj77rvh9/vx6KOPYt68edi+ffsA54ASent7MXPmTKxYsQIGgwGbN2/Gz372M+j1evz4xz8+4rG89tprOHToEC6//HLk5eVh7969+N3vfoe9e/diy5YtR1y0v3fvXsydOxeFhYX42c9+BpvNhvXr12PZsmV47rnncN555x3xGFPB/fffj7a2tgGf//3vf8c111yDU089Fd///vdhs9lQVVWF//u//xv2mR988AEA8tyngmAwmLJOpK+vb8Bnr7/+Os4++2yMHTsWt956KwKBAB5++GHMnTsXn3766YA1ZhnpdrvxyCOP4MILL8SePXswYcKEYcc9GHzWtXjooYewdOlSfOMb30A4HMYzzzyDCy+8EC+99BIWL16ccO17772H559/Htdccw0cDgfWrl3brx9kZmYCAHbv3o0zzzwT2dnZuPXWWxGNRrFmzRrk5uYOO5f9+/ejuroaV1xxBRwOx5DXVlRUJOgcv/vd71BVVZXgqD3uuOMAHLl+ce211yItLQ233norampq8Oijj+Lw4cP9CvTRwpclLxmORN7HYjGcddZZmDVrFu655x5s3rwZa9asQTQaxe23395/3ZHQi9lsxpNPPokf/vCH/Z998MEHKR2Wfr8fp5xyCpqamnDllVeipKQEH3zwAW688Ua0tLTgwQcfRHZ29oh0TyV8UWsJABMnToTFYsH777//ufG8owZJhc8dtm7dKgGQXnvtNUmSJCkej0tFRUXSD37wg4TramtrJQDSk08+KcXjcekb3/iGZLVapY8++mj
"text/plain": [
"<Figure size 1000x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(10, 5))\n",
"plt.scatter(range(len(y_test)), y_test, label=\"Актуальные значения\", color=\"black\", alpha=0.5)\n",
"plt.scatter(range(len(y_test)), y_pred, label=\"Предсказанные(новые параметры)\", color=\"blue\", alpha=0.5)\n",
"plt.scatter(range(len(y_test)), y_test_predict, label=\"Предсказанные(старые параметры)\", color=\"red\", alpha=0.5)\n",
"plt.xlabel(\"Выборка\")\n",
"plt.ylabel(\"Значения\")\n",
"plt.legend()\n",
"plt.title(\"Актуальные значения vs Предсказанные значения (Новые and Старые Параметры)\")\n",
"plt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "aimenv",
"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": "3.12.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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