From df2e5b3cbbc0385fbf74641e9ce80d7a4f11e501 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=BC=A0=E5=8D=93=E7=AB=8B?= <13190677+zhang-zhuoli@user.noreply.gitee.com> Date: Sat, 15 Jul 2023 12:04:50 +0000 Subject: [PATCH] =?UTF-8?q?AdaBoost=E5=9B=9E=E5=BD=92?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: 张卓立 <13190677+zhang-zhuoli@user.noreply.gitee.com> --- .../ada_reg.ipynb | 360 ++++++++++++++++++ 1 file changed, 360 insertions(+) create mode 100644 共享民宿平台担保交易房子评分的影响研究/ada_reg.ipynb diff --git a/共享民宿平台担保交易房子评分的影响研究/ada_reg.ipynb b/共享民宿平台担保交易房子评分的影响研究/ada_reg.ipynb new file mode 100644 index 0000000..65c9166 --- /dev/null +++ b/共享民宿平台担保交易房子评分的影响研究/ada_reg.ipynb @@ -0,0 +1,360 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "9e30b402-4ed9-4e45-aa7c-a77a7d68df17", + "metadata": {}, + "source": [ + "# AdaBoost回归" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "1a11e27d-9c4e-47eb-958b-cf9ed1c4c603", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor\n", + "from sklearn.metrics import mean_squared_error\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.model_selection import train_test_split" + ] + }, + { + "cell_type": "markdown", + "id": "7a704744-26f2-40f1-9bc1-0e25608dd40f", + "metadata": {}, + "source": [ + "读入数据，由于样本量很大，直接删除有缺失值的样本。" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "9f2f693d-44fd-432f-b732-7a2117106150", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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host_response_ratehost_acceptance_rateaccommodatespricenumber_of_reviewsreview_scores_rating
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" + ], + "text/plain": [ + " host_response_rate host_acceptance_rate accommodates price \\\n", + "0 1.00 0.33 2.0 120.0 \n", + "1 1.00 0.98 2.0 90.0 \n", + "2 1.00 0.98 2.0 66.0 \n", + "3 1.00 0.98 1.0 33.0 \n", + "5 1.00 1.00 2.0 45.0 \n", + "... ... ... ... ... \n", + "203252 1.00 0.93 4.0 152.0 \n", + "203253 1.00 0.97 2.0 45.0 \n", + "203254 1.00 0.97 2.0 40.0 \n", + "203276 0.99 0.99 2.0 43.0 \n", + "203308 1.00 1.00 3.0 110.0 \n", + "\n", + " number_of_reviews review_scores_rating \n", + "0 90.0 4.50 \n", + "1 351.0 4.58 \n", + "2 67.0 4.52 \n", + "3 297.0 4.70 \n", + "5 42.0 4.98 \n", + "... ... ... \n", + "203252 1.0 4.00 \n", + "203253 1.0 3.00 \n", + "203254 1.0 1.00 \n", + "203276 1.0 5.00 \n", + "203308 1.0 5.00 \n", + "\n", + "[134835 rows x 6 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "variables = ['number_of_reviews', 'price', 'accommodates',\n", + " 'host_response_rate', 'host_acceptance_rate', 'review_scores_rating']\n", + "df = pd.read_csv('../data/2022-01(US_25).csv', usecols=variables)\n", + "df['price'] = df['price'].replace('\\$', '', regex=True)\n", + "df['price'] = df['price'].replace('\\,', '', regex=True).astype(float)\n", + "df[['host_response_rate', 'host_acceptance_rate']] = df[['host_response_rate',\n", + " 'host_acceptance_rate']].replace('\\%', '', regex=True).astype(float)*0.01\n", + "df[['number_of_reviews']] = df[['number_of_reviews']].astype(float)\n", + "for col in variables:\n", + " df[col] = df[col].astype(np.float32)\n", + " df = df[np.isnan(df[col]) != 1]\n", + "df" + ] + }, + { + "cell_type": "markdown", + "id": "16658a50-232f-49c7-ad4c-c93a6a43e118", + "metadata": {}, + "source": [ + "划分测试集和训练集，测试集大小为0.3，并对数据标准化" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "00158193-2ea9-4fb8-91f1-cb5ea4b9fb96", + "metadata": {}, + "outputs": [], + "source": [ + "# 固定划分训练集和测试集\n", + "info = df.iloc[:, :-1].values\n", + "target = df.iloc[:, -1].values\n", + "# 标准化\n", + "stdscaler = StandardScaler()\n", + "info_train, info_test, target_train, target_test = train_test_split(\n", + " info, target, test_size=0.3,shuffle=True, random_state=420)\n", + "info_train = stdscaler.fit_transform(info_train)\n", + "info_test = stdscaler.transform(info_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "91622729-b48b-45bd-9cd6-b33f8d839d7a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.2976891 , 0.44020966, -0.10153121, -0.14794447, -0.62968445],\n", + " [ 0.2976891 , 0.44020966, -0.10153121, -0.27286685, 0.06075969],\n", + " [ 0.2976891 , 0.39323488, -0.8076375 , -0.25845274, -0.62968445],\n", + " ...,\n", + " [ 0.2976891 , 0.5341586 , 0.6045751 , -0.22241743, 0.03610097],\n", + " [ 0.2976891 , 0.25231144, -0.8076375 , -0.3016951 , 1.1457433 ],\n", + " [ 0.2976891 , -0.6871791 , -0.8076375 , -0.3185116 , 0.72654516]],\n", + " dtype=float32)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "info_train" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "3b292dc8-9c79-45e4-8a4a-6bda192a2b64", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([3. , 4.93, 4. , ..., 4.69, 4.85, 4.83], dtype=float32)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "target_train" + ] + }, + { + "cell_type": "markdown", + "id": "47bf67ca-2ede-4bf3-9f77-1f49efd68bc3", + "metadata": {}, + "source": [ + "拟合并预测，计算在测试集上的均方误差" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "81f63b17-e62c-4775-a525-edb8430a0910", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE:0.4843\n" + ] + } + ], + "source": [ + "# AdaBoost回归\n", + "rf = RandomForestRegressor(n_estimators=10, random_state=0, max_depth=2)\n", + "ada = AdaBoostRegressor(estimator=rf,n_estimators=100, loss='square', random_state=0)\n", + "ada.fit(info_train, target_train)\n", + "target_pred = ada.predict(info_test)\n", + "print(\"MSE:%.4f\" % mean_squared_error(target_test, target_pred))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e4e758bb-8599-41c0-a6db-ef367d744505", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}