ниче не работает Т.Т
This commit is contained in:
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commit
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@ -2112,22 +2112,345 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 191,
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"metadata": {},
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"outputs": [
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{
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"ename": "ValueError",
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"evalue": "Shape of passed values is (8000, 21), indices imply (8000, 19)",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[1;32mIn[184], line 123\u001b[0m\n\u001b[0;32m 121\u001b[0m preprocessing_result \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame(preprocessing_result, columns\u001b[38;5;241m=\u001b[39mnum_columns \u001b[38;5;241m+\u001b[39m cat_columns \u001b[38;5;241m+\u001b[39m cols)\n\u001b[0;32m 122\u001b[0m preprocessing_result \u001b[38;5;241m=\u001b[39m features_engineering\u001b[38;5;241m.\u001b[39mfit_transform(preprocessing_result)\n\u001b[1;32m--> 123\u001b[0m preprocessing_result \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mDataFrame\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpreprocessing_result\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_columns\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mcat_columns\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 125\u001b[0m \u001b[38;5;66;03m# preprocessing_result = features_postprocessing.fit_transform(preprocessing_result)\u001b[39;00m\n\u001b[0;32m 126\u001b[0m \n\u001b[0;32m 127\u001b[0m \u001b[38;5;66;03m# preprocessing_result = pipeline_end.fit_transform(X_train)\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 131\u001b[0m \u001b[38;5;66;03m# )\u001b[39;00m\n\u001b[0;32m 132\u001b[0m \u001b[38;5;66;03m# preprocessed_df\u001b[39;00m\n",
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"File \u001b[1;32md:\\Study\\3 курс 5 семестр\\AIM\\AIM-PIbd-31-Yakovlev-M-G\\kernel\\Lib\\site-packages\\pandas\\core\\frame.py:827\u001b[0m, in \u001b[0;36mDataFrame.__init__\u001b[1;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[0;32m 816\u001b[0m mgr \u001b[38;5;241m=\u001b[39m dict_to_mgr(\n\u001b[0;32m 817\u001b[0m \u001b[38;5;66;03m# error: Item \"ndarray\" of \"Union[ndarray, Series, Index]\" has no\u001b[39;00m\n\u001b[0;32m 818\u001b[0m \u001b[38;5;66;03m# attribute \"name\"\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 824\u001b[0m copy\u001b[38;5;241m=\u001b[39m_copy,\n\u001b[0;32m 825\u001b[0m )\n\u001b[0;32m 826\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 827\u001b[0m mgr \u001b[38;5;241m=\u001b[39m \u001b[43mndarray_to_mgr\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 828\u001b[0m \u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 829\u001b[0m \u001b[43m \u001b[49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 830\u001b[0m \u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 831\u001b[0m \u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 832\u001b[0m \u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 833\u001b[0m \u001b[43m \u001b[49m\u001b[43mtyp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmanager\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 834\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 836\u001b[0m \u001b[38;5;66;03m# For data is list-like, or Iterable (will consume into list)\u001b[39;00m\n\u001b[0;32m 837\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m is_list_like(data):\n",
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"File \u001b[1;32md:\\Study\\3 курс 5 семестр\\AIM\\AIM-PIbd-31-Yakovlev-M-G\\kernel\\Lib\\site-packages\\pandas\\core\\internals\\construction.py:336\u001b[0m, in \u001b[0;36mndarray_to_mgr\u001b[1;34m(values, index, columns, dtype, copy, typ)\u001b[0m\n\u001b[0;32m 331\u001b[0m \u001b[38;5;66;03m# _prep_ndarraylike ensures that values.ndim == 2 at this point\u001b[39;00m\n\u001b[0;32m 332\u001b[0m index, columns \u001b[38;5;241m=\u001b[39m _get_axes(\n\u001b[0;32m 333\u001b[0m values\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m], values\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m], index\u001b[38;5;241m=\u001b[39mindex, columns\u001b[38;5;241m=\u001b[39mcolumns\n\u001b[0;32m 334\u001b[0m )\n\u001b[1;32m--> 336\u001b[0m \u001b[43m_check_values_indices_shape_match\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 338\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m typ \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124marray\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m 339\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28missubclass\u001b[39m(values\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;241m.\u001b[39mtype, \u001b[38;5;28mstr\u001b[39m):\n",
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"File \u001b[1;32md:\\Study\\3 курс 5 семестр\\AIM\\AIM-PIbd-31-Yakovlev-M-G\\kernel\\Lib\\site-packages\\pandas\\core\\internals\\construction.py:420\u001b[0m, in \u001b[0;36m_check_values_indices_shape_match\u001b[1;34m(values, index, columns)\u001b[0m\n\u001b[0;32m 418\u001b[0m passed \u001b[38;5;241m=\u001b[39m values\u001b[38;5;241m.\u001b[39mshape\n\u001b[0;32m 419\u001b[0m implied \u001b[38;5;241m=\u001b[39m (\u001b[38;5;28mlen\u001b[39m(index), \u001b[38;5;28mlen\u001b[39m(columns))\n\u001b[1;32m--> 420\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mShape of passed values is \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpassed\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, indices imply \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mimplied\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
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"\u001b[1;31mValueError\u001b[0m: Shape of passed values is (8000, 21), indices imply (8000, 19)"
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]
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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|
||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
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||||
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|
||||
" <td>2</td>\n",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
" <td>47.5152</td>\n",
|
||||
" <td>-122.197</td>\n",
|
||||
" <td>1920</td>\n",
|
||||
" <td>10731</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>7998</th>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>2787310130</td>\n",
|
||||
" <td>289950.0</td>\n",
|
||||
" <td>4</td>\n",
|
||||
" <td>1.75</td>\n",
|
||||
" <td>2090</td>\n",
|
||||
" <td>7416</td>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>4</td>\n",
|
||||
" <td>7</td>\n",
|
||||
" <td>1050</td>\n",
|
||||
" <td>1040</td>\n",
|
||||
" <td>1970</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>98031</td>\n",
|
||||
" <td>47.4107</td>\n",
|
||||
" <td>-122.179</td>\n",
|
||||
" <td>1710</td>\n",
|
||||
" <td>7527</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>7999</th>\n",
|
||||
" <td>2</td>\n",
|
||||
" <td>8567300110</td>\n",
|
||||
" <td>485000.0</td>\n",
|
||||
" <td>3</td>\n",
|
||||
" <td>2.5</td>\n",
|
||||
" <td>2340</td>\n",
|
||||
" <td>59058</td>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>3</td>\n",
|
||||
" <td>8</td>\n",
|
||||
" <td>2340</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>1985</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>98038</td>\n",
|
||||
" <td>47.4052</td>\n",
|
||||
" <td>-122.028</td>\n",
|
||||
" <td>2700</td>\n",
|
||||
" <td>37263</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"<p>8000 rows × 19 columns</p>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" id price bedrooms bathrooms sqft_living sqft_lot floors \\\n",
|
||||
"0 3 3260000340 732600.0 4 2.5 2130 7300 \n",
|
||||
"1 2 9828702055 358000.0 2 1.5 960 1808 \n",
|
||||
"2 1 3438500625 210000.0 3 1.0 1080 21043 \n",
|
||||
"3 2 2422029094 517534.0 2 1.0 833 143947 \n",
|
||||
"4 2 7462900015 387000.0 3 2.25 1760 45133 \n",
|
||||
"... .. ... ... ... ... ... ... \n",
|
||||
"7995 2 2787720140 416000.0 3 2.5 1790 11542 \n",
|
||||
"7996 3 6192400400 775000.0 4 2.5 3090 7112 \n",
|
||||
"7997 2 2296500036 450000.0 4 2.75 2980 13260 \n",
|
||||
"7998 1 2787310130 289950.0 4 1.75 2090 7416 \n",
|
||||
"7999 2 8567300110 485000.0 3 2.5 2340 59058 \n",
|
||||
"\n",
|
||||
" condition grade sqft_above sqft_basement yr_built yr_renovated zipcode \\\n",
|
||||
"0 1.0 4 7 1230 900 1963 0 \n",
|
||||
"1 2.0 3 7 960 0 1993 0 \n",
|
||||
"2 1.0 3 6 1080 0 1942 0 \n",
|
||||
"3 1.0 3 5 833 0 2006 0 \n",
|
||||
"4 2.0 3 7 1760 0 1984 0 \n",
|
||||
"... ... ... ... ... ... ... ... \n",
|
||||
"7995 1.0 5 7 1190 600 1969 0 \n",
|
||||
"7996 2.0 3 9 3090 0 2001 0 \n",
|
||||
"7997 1.0 4 8 1800 1180 1979 0 \n",
|
||||
"7998 1.0 4 7 1050 1040 1970 0 \n",
|
||||
"7999 1.0 3 8 2340 0 1985 0 \n",
|
||||
"\n",
|
||||
" lat long sqft_living15 sqft_lot15 price_category \n",
|
||||
"0 98005 47.605 -122.167 2130 7560 \n",
|
||||
"1 98122 47.6183 -122.298 1290 1668 \n",
|
||||
"2 98106 47.5515 -122.357 1380 7620 \n",
|
||||
"3 98070 47.3889 -122.482 1380 143947 \n",
|
||||
"4 98065 47.5124 -121.866 1910 51773 \n",
|
||||
"... ... ... ... ... ... \n",
|
||||
"7995 98059 47.5124 -122.16 1790 9131 \n",
|
||||
"7996 98052 47.705 -122.118 3050 6000 \n",
|
||||
"7997 98056 47.5152 -122.197 1920 10731 \n",
|
||||
"7998 98031 47.4107 -122.179 1710 7527 \n",
|
||||
"7999 98038 47.4052 -122.028 2700 37263 \n",
|
||||
"\n",
|
||||
"[8000 rows x 19 columns]"
|
||||
]
|
||||
},
|
||||
"execution_count": 191,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
@ -2209,17 +2532,18 @@
|
||||
" transformers=[\n",
|
||||
" (\"prepocessing_num\", preprocessing_num, num_columns),\n",
|
||||
" (\"prepocessing_cat\", preprocessing_cat, cat_columns),\n",
|
||||
" # (\"prepocessing_features\", cat_imputer, [\"price_category\"]),\n",
|
||||
" ],\n",
|
||||
" remainder=\"passthrough\"\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"# features_engineering = ColumnTransformer(\n",
|
||||
"# verbose_feature_names_out=False,\n",
|
||||
"# transformers=[\n",
|
||||
"# (\"add_features\", HousesFeatures(), [\"price_category\"]),\n",
|
||||
"# ],\n",
|
||||
"# remainder=\"passthrough\",\n",
|
||||
"# )\n",
|
||||
"features_engineering = ColumnTransformer(\n",
|
||||
" verbose_feature_names_out=False,\n",
|
||||
" transformers=[\n",
|
||||
" (\"add_features\", HousesFeatures(), [\"price_category\"]),\n",
|
||||
" ],\n",
|
||||
" remainder=\"passthrough\",\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"drop_columns = ColumnTransformer(\n",
|
||||
" verbose_feature_names_out=False,\n",
|
||||
@ -2249,10 +2573,11 @@
|
||||
"cols = ['a', 'b']\n",
|
||||
"preprocessing_result = drop_columns.fit_transform(X_train)\n",
|
||||
"preprocessing_result = pd.DataFrame(preprocessing_result, columns=num_columns + cat_columns)\n",
|
||||
"preprocessing_result = features_preprocessing.fit_transform(preprocessing_result)\n",
|
||||
"preprocessing_result = pd.DataFrame(preprocessing_result, columns=num_columns + cat_columns + cols)\n",
|
||||
"preprocessing_result = features_engineering.fit_transform(preprocessing_result)\n",
|
||||
"preprocessing_result = pd.DataFrame(preprocessing_result, columns=num_columns + cat_columns)\n",
|
||||
"preprocessing_result\n",
|
||||
"# # preprocessing_result = features_preprocessing.fit_transform(preprocessing_result)\n",
|
||||
"# # preprocessing_result = pd.DataFrame(preprocessing_result, columns=num_columns + cat_columns)\n",
|
||||
"\n",
|
||||
"# preprocessing_result = features_postprocessing.fit_transform(preprocessing_result)\n",
|
||||
"\n",
|
||||
|
||||
Loading…
Reference in New Issue
Block a user