张卓立
361 lines
11 KiB
Plaintext
361 lines
11 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "9e30b402-4ed9-4e45-aa7c-a77a7d68df17",
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"metadata": {},
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"source": [
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"# AdaBoost回归"
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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": 1,
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"id": "1a11e27d-9c4e-47eb-958b-cf9ed1c4c603",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pandas as pd\n",
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"from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor\n",
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"from sklearn.metrics import mean_squared_error\n",
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"from sklearn.preprocessing import StandardScaler\n",
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"from sklearn.model_selection import train_test_split"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7a704744-26f2-40f1-9bc1-0e25608dd40f",
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"metadata": {},
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"source": [
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"读入数据，由于样本量很大，直接删除有缺失值的样本。"
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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": 2,
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"id": "9f2f693d-44fd-432f-b732-7a2117106150",
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"metadata": {},
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"outputs": [
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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>host_response_rate</th>\n",
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" <th>host_acceptance_rate</th>\n",
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" <th>accommodates</th>\n",
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" <th>price</th>\n",
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" <th>number_of_reviews</th>\n",
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" <th>review_scores_rating</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>1.00</td>\n",
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" <td>0.33</td>\n",
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" <td>2.0</td>\n",
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" <td>120.0</td>\n",
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" <td>90.0</td>\n",
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" <td>4.50</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>1.00</td>\n",
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" <td>0.98</td>\n",
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" <td>2.0</td>\n",
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" <td>90.0</td>\n",
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" <td>351.0</td>\n",
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" <td>4.58</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>1.00</td>\n",
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" <td>0.98</td>\n",
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" <td>2.0</td>\n",
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" <td>66.0</td>\n",
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" <td>67.0</td>\n",
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" <td>4.52</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>1.00</td>\n",
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" <td>0.98</td>\n",
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" <td>1.0</td>\n",
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" <td>33.0</td>\n",
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" <td>297.0</td>\n",
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" <td>4.70</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>5</th>\n",
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" <td>1.00</td>\n",
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" <td>1.00</td>\n",
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" <td>2.0</td>\n",
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" <td>45.0</td>\n",
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" <td>42.0</td>\n",
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" <td>4.98</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>...</th>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>203252</th>\n",
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" <td>1.00</td>\n",
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" <td>0.93</td>\n",
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" <td>4.0</td>\n",
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" <td>152.0</td>\n",
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" <td>1.0</td>\n",
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" <td>4.00</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>203253</th>\n",
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" <td>1.00</td>\n",
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" <td>0.97</td>\n",
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" <td>2.0</td>\n",
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" <td>45.0</td>\n",
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" <td>1.0</td>\n",
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" <td>3.00</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>203254</th>\n",
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" <td>1.00</td>\n",
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" <td>0.97</td>\n",
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" <td>2.0</td>\n",
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" <td>40.0</td>\n",
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" <td>1.0</td>\n",
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" <td>1.00</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>203276</th>\n",
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" <td>0.99</td>\n",
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" <td>0.99</td>\n",
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" <td>2.0</td>\n",
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" <td>43.0</td>\n",
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" <td>1.0</td>\n",
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" <td>5.00</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>203308</th>\n",
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" <td>1.00</td>\n",
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" <td>1.00</td>\n",
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" <td>3.0</td>\n",
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" <td>110.0</td>\n",
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" <td>1.0</td>\n",
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" <td>5.00</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"<p>134835 rows × 6 columns</p>\n",
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"</div>"
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],
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"text/plain": [
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" host_response_rate host_acceptance_rate accommodates price \\\n",
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"0 1.00 0.33 2.0 120.0 \n",
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"1 1.00 0.98 2.0 90.0 \n",
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"2 1.00 0.98 2.0 66.0 \n",
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"3 1.00 0.98 1.0 33.0 \n",
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"5 1.00 1.00 2.0 45.0 \n",
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"... ... ... ... ... \n",
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"203252 1.00 0.93 4.0 152.0 \n",
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"203253 1.00 0.97 2.0 45.0 \n",
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"203254 1.00 0.97 2.0 40.0 \n",
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"203276 0.99 0.99 2.0 43.0 \n",
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"203308 1.00 1.00 3.0 110.0 \n",
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"\n",
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" number_of_reviews review_scores_rating \n",
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"0 90.0 4.50 \n",
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"1 351.0 4.58 \n",
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"2 67.0 4.52 \n",
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"3 297.0 4.70 \n",
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"5 42.0 4.98 \n",
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"... ... ... \n",
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"203252 1.0 4.00 \n",
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"203253 1.0 3.00 \n",
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"203254 1.0 1.00 \n",
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"203276 1.0 5.00 \n",
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"203308 1.0 5.00 \n",
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"\n",
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"[134835 rows x 6 columns]"
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]
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},
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"execution_count": 2,
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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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"variables = ['number_of_reviews', 'price', 'accommodates',\n",
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" 'host_response_rate', 'host_acceptance_rate', 'review_scores_rating']\n",
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"df = pd.read_csv('../data/2022-01(US_25).csv', usecols=variables)\n",
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"df['price'] = df['price'].replace('\\$', '', regex=True)\n",
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"df['price'] = df['price'].replace('\\,', '', regex=True).astype(float)\n",
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"df[['host_response_rate', 'host_acceptance_rate']] = df[['host_response_rate',\n",
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" 'host_acceptance_rate']].replace('\\%', '', regex=True).astype(float)*0.01\n",
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"df[['number_of_reviews']] = df[['number_of_reviews']].astype(float)\n",
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"for col in variables:\n",
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" df[col] = df[col].astype(np.float32)\n",
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" df = df[np.isnan(df[col]) != 1]\n",
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"df"
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]
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},
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{
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"cell_type": "markdown",
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"id": "16658a50-232f-49c7-ad4c-c93a6a43e118",
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"metadata": {},
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"source": [
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"划分测试集和训练集，测试集大小为0.3，并对数据标准化"
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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": 3,
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"id": "00158193-2ea9-4fb8-91f1-cb5ea4b9fb96",
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"metadata": {},
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"outputs": [],
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"source": [
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"# 固定划分训练集和测试集\n",
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"info = df.iloc[:, :-1].values\n",
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"target = df.iloc[:, -1].values\n",
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"# 标准化\n",
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"stdscaler = StandardScaler()\n",
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"info_train, info_test, target_train, target_test = train_test_split(\n",
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" info, target, test_size=0.3,shuffle=True, random_state=420)\n",
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"info_train = stdscaler.fit_transform(info_train)\n",
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"info_test = stdscaler.transform(info_test)"
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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": 4,
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"id": "91622729-b48b-45bd-9cd6-b33f8d839d7a",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[ 0.2976891 , 0.44020966, -0.10153121, -0.14794447, -0.62968445],\n",
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" [ 0.2976891 , 0.44020966, -0.10153121, -0.27286685, 0.06075969],\n",
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" [ 0.2976891 , 0.39323488, -0.8076375 , -0.25845274, -0.62968445],\n",
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" ...,\n",
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" [ 0.2976891 , 0.5341586 , 0.6045751 , -0.22241743, 0.03610097],\n",
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" [ 0.2976891 , 0.25231144, -0.8076375 , -0.3016951 , 1.1457433 ],\n",
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" [ 0.2976891 , -0.6871791 , -0.8076375 , -0.3185116 , 0.72654516]],\n",
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" dtype=float32)"
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]
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},
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"execution_count": 4,
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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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"info_train"
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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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"id": "3b292dc8-9c79-45e4-8a4a-6bda192a2b64",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([3. , 4.93, 4. , ..., 4.69, 4.85, 4.83], dtype=float32)"
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]
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},
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"execution_count": 5,
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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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"target_train"
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]
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},
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{
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"cell_type": "markdown",
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"id": "47bf67ca-2ede-4bf3-9f77-1f49efd68bc3",
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"metadata": {},
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"source": [
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"拟合并预测，计算在测试集上的均方误差"
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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": 6,
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"id": "81f63b17-e62c-4775-a525-edb8430a0910",
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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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"MSE:0.4843\n"
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]
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}
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],
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"source": [
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"# AdaBoost回归\n",
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"rf = RandomForestRegressor(n_estimators=10, random_state=0, max_depth=2)\n",
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"ada = AdaBoostRegressor(estimator=rf,n_estimators=100, loss='square', random_state=0)\n",
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"ada.fit(info_train, target_train)\n",
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"target_pred = ada.predict(info_test)\n",
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"print(\"MSE:%.4f\" % mean_squared_error(target_test, target_pred))"
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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": null,
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"id": "e4e758bb-8599-41c0-a6db-ef367d744505",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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