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lab_2/lab_2.ipynb
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lab_2/lab_2.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### DataSet - \"Gaming Laptop Specs and Price\"\n",
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"Данный датасет содержит данные о игровых ноутбуках."
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]
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},
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{
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"cell_type": "code",
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"<class 'pandas.core.frame.DataFrame'>\n",
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"RangeIndex: 532 entries, 0 to 531\n",
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"Data columns (total 18 columns):\n",
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" # Column Non-Null Count Dtype \n",
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"--- ------ -------------- ----- \n",
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" 0 brand_name 532 non-null object \n",
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" 1 price 532 non-null int64 \n",
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" 2 rating 532 non-null int64 \n",
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" 3 processor_gen 520 non-null object \n",
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" 4 processor_brand 532 non-null object \n",
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" 5 processor_segment 528 non-null object \n",
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" 6 CPU_mark 532 non-null object \n",
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" 7 CPU_performance 532 non-null object \n",
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" 8 Graphic_card_memory 530 non-null object \n",
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" 9 graphic_card_name 530 non-null object \n",
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" 10 graphic_card_num 532 non-null object \n",
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" 11 Core 530 non-null float64\n",
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" 12 threads 514 non-null float64\n",
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" 13 display_inches 532 non-null object \n",
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" 14 ram_storage 532 non-null int64 \n",
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" 15 ram_type 532 non-null object \n",
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" 16 operating_system 502 non-null float64\n",
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" 17 SSD_storage 532 non-null object \n",
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"dtypes: float64(3), int64(3), object(12)\n",
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"memory usage: 74.9+ KB\n"
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]
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},
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>brand_name</th>\n",
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" <th>price</th>\n",
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" <th>rating</th>\n",
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" <th>processor_gen</th>\n",
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" <th>processor_brand</th>\n",
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" <th>processor_segment</th>\n",
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" <th>CPU_mark</th>\n",
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" <th>CPU_performance</th>\n",
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" <th>Graphic_card_memory</th>\n",
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" <th>graphic_card_name</th>\n",
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" <th>graphic_card_num</th>\n",
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" <th>Core</th>\n",
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" <th>threads</th>\n",
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" <th>display_inches</th>\n",
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" <th>ram_storage</th>\n",
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" <th>ram_type</th>\n",
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" <th>operating_system</th>\n",
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" <th>SSD_storage</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>hp</td>\n",
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" <td>49490</td>\n",
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" <td>70</td>\n",
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" <td>5th</td>\n",
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" <td>amd</td>\n",
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" <td>5</td>\n",
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" <td>5600H</td>\n",
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" <td>maximum performance</td>\n",
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" <td>4 GB</td>\n",
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" <td>amd radeon</td>\n",
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" <td>other</td>\n",
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" <td>6.0</td>\n",
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" <td>12.0</td>\n",
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" <td>15.6</td>\n",
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" <td>8</td>\n",
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" <td>DDR4</td>\n",
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" <td>11.0</td>\n",
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" <td>512 GB SSD</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>xiaomi</td>\n",
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" <td>102990</td>\n",
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" <td>78</td>\n",
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" <td>14th</td>\n",
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" <td>intel</td>\n",
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" <td>i9</td>\n",
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" <td>14900HX</td>\n",
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" <td>maximum performance</td>\n",
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" <td>8 GB</td>\n",
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" <td>nvidia geforce</td>\n",
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" <td>4060</td>\n",
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" <td>24.0</td>\n",
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" <td>32.0</td>\n",
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" <td>other</td>\n",
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" <td>16</td>\n",
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" <td>DDR5</td>\n",
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" <td>11.0</td>\n",
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" <td>1 TB SSD</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>hp</td>\n",
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" <td>81490</td>\n",
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" <td>73</td>\n",
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" <td>7th</td>\n",
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" <td>amd</td>\n",
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" <td>7</td>\n",
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" <td>7840HS</td>\n",
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" <td>high efficiency</td>\n",
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" <td>6 GB</td>\n",
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" <td>nvidia geforce</td>\n",
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" <td>3050</td>\n",
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" <td>8.0</td>\n",
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" <td>16.0</td>\n",
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" <td>other</td>\n",
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" <td>16</td>\n",
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" <td>DDR5</td>\n",
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" <td>11.0</td>\n",
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" <td>1 TB SSD</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>asus</td>\n",
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" <td>49990</td>\n",
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" <td>64</td>\n",
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" <td>11th</td>\n",
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" <td>intel</td>\n",
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" <td>i5</td>\n",
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" <td>11400H</td>\n",
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" <td>maximum performance</td>\n",
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" <td>4 GB</td>\n",
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" <td>nvidia geforce</td>\n",
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" <td>2050</td>\n",
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" <td>6.0</td>\n",
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" <td>12.0</td>\n",
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" <td>15.6</td>\n",
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" <td>8</td>\n",
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" <td>DDR4</td>\n",
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" <td>11.0</td>\n",
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" <td>512 GB SSD</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>asus</td>\n",
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" <td>52990</td>\n",
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" <td>66</td>\n",
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" <td>11th</td>\n",
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" <td>intel</td>\n",
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" <td>i5</td>\n",
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" <td>11400H</td>\n",
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" <td>maximum performance</td>\n",
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" <td>4 GB</td>\n",
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" <td>nvidia geforce</td>\n",
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" <td>2050</td>\n",
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" <td>6.0</td>\n",
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" <td>12.0</td>\n",
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" <td>15.6</td>\n",
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" <td>16</td>\n",
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" <td>DDR4</td>\n",
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" <td>11.0</td>\n",
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" <td>512 GB SSD</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" brand_name price rating processor_gen processor_brand processor_segment \\\n",
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"0 hp 49490 70 5th amd 5 \n",
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"1 xiaomi 102990 78 14th intel i9 \n",
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"2 hp 81490 73 7th amd 7 \n",
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"3 asus 49990 64 11th intel i5 \n",
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"4 asus 52990 66 11th intel i5 \n",
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"\n",
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" CPU_mark CPU_performance Graphic_card_memory graphic_card_name \\\n",
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"0 5600H maximum performance 4 GB amd radeon \n",
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"1 14900HX maximum performance 8 GB nvidia geforce \n",
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"2 7840HS high efficiency 6 GB nvidia geforce \n",
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"3 11400H maximum performance 4 GB nvidia geforce \n",
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"4 11400H maximum performance 4 GB nvidia geforce \n",
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"\n",
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" graphic_card_num Core threads display_inches ram_storage ram_type \\\n",
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"0 other 6.0 12.0 15.6 8 DDR4 \n",
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"1 4060 24.0 32.0 other 16 DDR5 \n",
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"2 3050 8.0 16.0 other 16 DDR5 \n",
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"3 2050 6.0 12.0 15.6 8 DDR4 \n",
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"4 2050 6.0 12.0 15.6 16 DDR4 \n",
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"\n",
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" operating_system SSD_storage \n",
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"0 11.0 512 GB SSD \n",
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"1 11.0 1 TB SSD \n",
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"2 11.0 1 TB SSD \n",
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"3 11.0 512 GB SSD \n",
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"4 11.0 512 GB SSD "
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]
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},
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"execution_count": 1,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import pandas as pd\n",
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"\n",
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"df = pd.read_csv(\"datasets/laptop.csv\")\n",
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"df.info()\n",
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"df.head()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Проблемная область\n",
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"Данный датасет позволяет проанализировать данные, и понять какие ноутбуки имеют превосходство на рынке техники, и какие чаще всего выбирают пользователи.\n",
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"#### Анализ набора данных\n",
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"Объекты наблюдения - игровые ноутбуки\n",
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"Атрибуты - Имя бренда, цена, рейтинг, поколение процессора, марка процессора, сегмент процессора, специальная метка, производительность процессора, видеокарта, видеопамять, номер видеокарты, кол-во потоков видеокарты, размер дисплея, оперативная память, тип оперативной памяти, ОС, SSD.\n",
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"Связи между объектами - нет\n",
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"#### Бизнес-цели\n",
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"Данный набор данных может помочь определить лидеров на рынке игровых ноутбуков.\n",
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"В свою очередь определение лидеров поможет определить:\n",
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"1. Какие модели игровых ноутбуков более выгодно продавать магазинам техники.\n",
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"2. Какие комплектующие наиболее популярны у производителей и покупателей, для дальнейшего увеличения производства данных комплектующих.\n",
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"3. Определение популярных комлпектующих, для дальнейшей сборки других игровых ноутбуков новых версий.\n",
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"#### Примеры целей технического проекта. Что поступает на вход, что является целевым признаком?????????\n",
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"#### Проблемы набора данных и их решения\n",
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"1. Возможны устаревшие данные, т.к. новые комплектующие выходят довольно часто. Для решения данной проблемы требуется удаление самых старых записей о ноутбуках, и добавление более новых моделей.\n",
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"2. Возможны выбросы, какие-то \"сверхестественные сборки\". Решить эту проблему помогает удаление таких выбросов. В маленькой выборке не рекомендуется удалять выбросы. В большой выборке выбросы усредняются.\n",
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"#### Качество набора данных\n",
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"Набор данных содержит достаточно примеров и признаков для обучения модели. Учтены различные ситуации проблемной области. Данные соответствуют данным, которые будут\n",
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"подаваться в производственной среде. Все метки согласованы.\n",
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"#### Проблема пропущенных данных"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"processor_gen процент пустых значений: %2.26\n",
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"processor_segment процент пустых значений: %0.75\n",
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"Graphic_card_memory процент пустых значений: %0.38\n",
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"graphic_card_name процент пустых значений: %0.38\n",
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"Core процент пустых значений: %0.38\n",
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"threads процент пустых значений: %3.38\n",
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"operating_system процент пустых значений: %5.64\n"
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]
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}
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],
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"source": [
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"for i in df.columns:\n",
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" null_rate = df[i].isnull().sum() / len(df)*100\n",
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" if null_rate > 0:\n",
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" print(f\"{i} процент пустых значений: %{null_rate:.2f}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"brand_name False\n",
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"price False\n",
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"rating False\n",
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"processor_gen False\n",
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"processor_brand False\n",
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"processor_segment False\n",
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"CPU_mark False\n",
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"CPU_performance False\n",
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"Graphic_card_memory False\n",
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"graphic_card_name False\n",
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"graphic_card_num False\n",
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"Core False\n",
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"threads False\n",
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"display_inches False\n",
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"ram_storage False\n",
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"ram_type False\n",
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"operating_system False\n",
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"SSD_storage False\n",
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"dtype: bool\n"
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]
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},
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{
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"data": {
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"text/html": [
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"<div>\n",
|
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"<style scoped>\n",
|
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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||||
" }\n",
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"\n",
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||||
" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
|
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" .dataframe thead th {\n",
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" text-align: right;\n",
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||||
" }\n",
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"</style>\n",
|
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>brand_name</th>\n",
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" <th>price</th>\n",
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" <th>rating</th>\n",
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" <th>processor_gen</th>\n",
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" <th>processor_brand</th>\n",
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" <th>processor_segment</th>\n",
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" <th>CPU_mark</th>\n",
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" <th>CPU_performance</th>\n",
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" <th>Graphic_card_memory</th>\n",
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" <th>graphic_card_name</th>\n",
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" <th>graphic_card_num</th>\n",
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" <th>Core</th>\n",
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" <th>threads</th>\n",
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" <th>display_inches</th>\n",
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" <th>ram_storage</th>\n",
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" <th>ram_type</th>\n",
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" <th>operating_system</th>\n",
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" <th>SSD_storage</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>527</th>\n",
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" <td>dell</td>\n",
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" <td>75500</td>\n",
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" <td>63</td>\n",
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" <td>4th</td>\n",
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" <td>amd</td>\n",
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" <td>5</td>\n",
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" <td>4600H</td>\n",
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" <td>maximum performance</td>\n",
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" <td>6 GB</td>\n",
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" <td>amd radeon</td>\n",
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" <td>other</td>\n",
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" <td>6.0</td>\n",
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" <td>12.0</td>\n",
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" <td>15.6</td>\n",
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" <td>8</td>\n",
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" <td>DDR4</td>\n",
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" <td>10.0</td>\n",
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" <td>512 GB SSD</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>528</th>\n",
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||||
" <td>lenovo</td>\n",
|
||||
" <td>151990</td>\n",
|
||||
" <td>75</td>\n",
|
||||
" <td>10th</td>\n",
|
||||
" <td>intel</td>\n",
|
||||
" <td>i7</td>\n",
|
||||
" <td>10875H</td>\n",
|
||||
" <td>maximum performance</td>\n",
|
||||
" <td>8 GB</td>\n",
|
||||
" <td>nvidia geforce</td>\n",
|
||||
" <td>other</td>\n",
|
||||
" <td>8.0</td>\n",
|
||||
" <td>16.0</td>\n",
|
||||
" <td>15.6</td>\n",
|
||||
" <td>16</td>\n",
|
||||
" <td>DDR4</td>\n",
|
||||
" <td>10.0</td>\n",
|
||||
" <td>1 TB SSD</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>529</th>\n",
|
||||
" <td>lenovo</td>\n",
|
||||
" <td>46500</td>\n",
|
||||
" <td>48</td>\n",
|
||||
" <td>8th</td>\n",
|
||||
" <td>intel</td>\n",
|
||||
" <td>i5</td>\n",
|
||||
" <td>8250U</td>\n",
|
||||
" <td>ultra-low power</td>\n",
|
||||
" <td>Integrated</td>\n",
|
||||
" <td>Intel Integrated</td>\n",
|
||||
" <td>other</td>\n",
|
||||
" <td>4.0</td>\n",
|
||||
" <td>8.0</td>\n",
|
||||
" <td>other</td>\n",
|
||||
" <td>4</td>\n",
|
||||
" <td>DDR4</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>other</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>530</th>\n",
|
||||
" <td>msi</td>\n",
|
||||
" <td>109990</td>\n",
|
||||
" <td>61</td>\n",
|
||||
" <td>9th</td>\n",
|
||||
" <td>intel</td>\n",
|
||||
" <td>i7</td>\n",
|
||||
" <td>9750H</td>\n",
|
||||
" <td>maximum performance</td>\n",
|
||||
" <td>6 GB</td>\n",
|
||||
" <td>nvidia geforce</td>\n",
|
||||
" <td>other</td>\n",
|
||||
" <td>6.0</td>\n",
|
||||
" <td>12.0</td>\n",
|
||||
" <td>other</td>\n",
|
||||
" <td>8</td>\n",
|
||||
" <td>other</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>other</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>531</th>\n",
|
||||
" <td>hp</td>\n",
|
||||
" <td>95800</td>\n",
|
||||
" <td>70</td>\n",
|
||||
" <td>9th</td>\n",
|
||||
" <td>intel</td>\n",
|
||||
" <td>i7</td>\n",
|
||||
" <td>9750H</td>\n",
|
||||
" <td>maximum performance</td>\n",
|
||||
" <td>4 GB</td>\n",
|
||||
" <td>nvidia geforce</td>\n",
|
||||
" <td>1650</td>\n",
|
||||
" <td>6.0</td>\n",
|
||||
" <td>12.0</td>\n",
|
||||
" <td>15.6</td>\n",
|
||||
" <td>8</td>\n",
|
||||
" <td>DDR4</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>other</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" brand_name price rating processor_gen processor_brand \\\n",
|
||||
"527 dell 75500 63 4th amd \n",
|
||||
"528 lenovo 151990 75 10th intel \n",
|
||||
"529 lenovo 46500 48 8th intel \n",
|
||||
"530 msi 109990 61 9th intel \n",
|
||||
"531 hp 95800 70 9th intel \n",
|
||||
"\n",
|
||||
" processor_segment CPU_mark CPU_performance Graphic_card_memory \\\n",
|
||||
"527 5 4600H maximum performance 6 GB \n",
|
||||
"528 i7 10875H maximum performance 8 GB \n",
|
||||
"529 i5 8250U ultra-low power Integrated \n",
|
||||
"530 i7 9750H maximum performance 6 GB \n",
|
||||
"531 i7 9750H maximum performance 4 GB \n",
|
||||
"\n",
|
||||
" graphic_card_name graphic_card_num Core threads display_inches \\\n",
|
||||
"527 amd radeon other 6.0 12.0 15.6 \n",
|
||||
"528 nvidia geforce other 8.0 16.0 15.6 \n",
|
||||
"529 Intel Integrated other 4.0 8.0 other \n",
|
||||
"530 nvidia geforce other 6.0 12.0 other \n",
|
||||
"531 nvidia geforce 1650 6.0 12.0 15.6 \n",
|
||||
"\n",
|
||||
" ram_storage ram_type operating_system SSD_storage \n",
|
||||
"527 8 DDR4 10.0 512 GB SSD \n",
|
||||
"528 16 DDR4 10.0 1 TB SSD \n",
|
||||
"529 4 DDR4 0.0 other \n",
|
||||
"530 8 other 0.0 other \n",
|
||||
"531 8 DDR4 0.0 other "
|
||||
]
|
||||
},
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df = df.fillna(0) #Замена пустых значений на 0\n",
|
||||
"print(df.isnull().any())\n",
|
||||
"df.tail()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Разбиение на выборки"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"ename": "ValueError",
|
||||
"evalue": "brand_name is not a column in the dataframe",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
|
||||
"Cell \u001b[1;32mIn[22], line 46\u001b[0m\n\u001b[0;32m 42\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m df_train, df_val, df_test\n\u001b[0;32m 44\u001b[0m data \u001b[38;5;241m=\u001b[39m df[[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrating\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprice\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mram_storage\u001b[39m\u001b[38;5;124m\"\u001b[39m]]\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[1;32m---> 46\u001b[0m df_train, df_val, df_test \u001b[38;5;241m=\u001b[39m \u001b[43msplit_stratified_into_train_val_test\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 47\u001b[0m \u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstratify_colname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mbrand_name\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfrac_train\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.60\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfrac_val\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.20\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfrac_test\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.20\u001b[39;49m\n\u001b[0;32m 48\u001b[0m \u001b[43m)\u001b[49m\n\u001b[0;32m 50\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mОбучающая выборка: \u001b[39m\u001b[38;5;124m\"\u001b[39m, df_train\u001b[38;5;241m.\u001b[39mshape)\n\u001b[0;32m 52\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mКонтрольная выборка: \u001b[39m\u001b[38;5;124m\"\u001b[39m, df_val\u001b[38;5;241m.\u001b[39mshape)\n",
|
||||
"Cell \u001b[1;32mIn[22], line 18\u001b[0m, in \u001b[0;36msplit_stratified_into_train_val_test\u001b[1;34m(df_input, stratify_colname, frac_train, frac_val, frac_test, random_state)\u001b[0m\n\u001b[0;32m 12\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m 13\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfractions \u001b[39m\u001b[38;5;132;01m%f\u001b[39;00m\u001b[38;5;124m, \u001b[39m\u001b[38;5;132;01m%f\u001b[39;00m\u001b[38;5;124m, \u001b[39m\u001b[38;5;132;01m%f\u001b[39;00m\u001b[38;5;124m do not add up to 1.0\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 14\u001b[0m \u001b[38;5;241m%\u001b[39m (frac_train, frac_val, frac_test)\n\u001b[0;32m 15\u001b[0m )\n\u001b[0;32m 17\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m stratify_colname \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m df_input\u001b[38;5;241m.\u001b[39mcolumns:\n\u001b[1;32m---> 18\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m is not a column in the dataframe\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m (stratify_colname))\n\u001b[0;32m 20\u001b[0m X \u001b[38;5;241m=\u001b[39m df_input \u001b[38;5;66;03m# Contains all columns.\u001b[39;00m\n\u001b[0;32m 21\u001b[0m y \u001b[38;5;241m=\u001b[39m df_input[\n\u001b[0;32m 22\u001b[0m [stratify_colname]\n\u001b[0;32m 23\u001b[0m ] \u001b[38;5;66;03m# Dataframe of just the column on which to stratify.\u001b[39;00m\n",
|
||||
"\u001b[1;31mValueError\u001b[0m: brand_name is not a column in the dataframe"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from sklearn.model_selection import train_test_split\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",
|
||||
"):\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",
|
||||
"\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",
|
||||
" # Split the temp dataframe into val and test dataframes.\n",
|
||||
" relative_frac_test = frac_test / (frac_val + frac_test)\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",
|
||||
"\n",
|
||||
" return df_train, df_val, df_test\n",
|
||||
"\n",
|
||||
"data = df[[\"rating\", \"price\", \"ram_storage\"]].copy()\n",
|
||||
"\n",
|
||||
"df_train, df_val, df_test = split_stratified_into_train_val_test(\n",
|
||||
" data, stratify_colname=\"rating\", frac_train=0.60, frac_val=0.20, frac_test=0.20\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"print(\"Обучающая выборка: \", df_train.shape)\n",
|
||||
"\n",
|
||||
"print(\"Контрольная выборка: \", df_val.shape)\n",
|
||||
"\n",
|
||||
"print(\"Тестовая выборка: \", df_test.shape)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "kernel",
|
||||
"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": "3.12.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
Loading…
Reference in New Issue
Block a user