問題

加載加州房價數據時出現 403 錯誤 HTTP Error 403: Forbidden

from sklearn.datasets import fetch_california_housingcalifornia = fetch_california_housing()
print(california.target.shape) 

解決方案

運行下述代碼，然后再運行上述的 fetch_california_housing() 可成功運行

import requests
import os
import tarfile
import numpy as np
from types import SimpleNamespacefrom sklearn import datasets
# 參考: 
# https://blog.csdn.net/getalong/article/details/141201658
# https://inria.github.io/scikit-learn-mooc/python_scripts/datasets_california_housing.htmlfetch_california_housing_manual_desc = '''
.. _california_housing_dataset:California Housing dataset
--------------------------**Data Set Characteristics:**:Number of Instances: 20640:Number of Attributes: 8 numeric, predictive attributes and the target:Attribute Information:- MedInc        median income in block group- HouseAge      median house age in block group- AveRooms      average number of rooms per household- AveBedrms     average number of bedrooms per household- Population    block group population- AveOccup      average number of household members- Latitude      block group latitude- Longitude     block group longitude:Missing Attribute Values: NoneThis dataset was obtained from the StatLib repository.
https://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.htmlThe target variable is the median house value for California districts,
expressed in hundreds of thousands of dollars ($100,000).This dataset was derived from the 1990 U.S. census, using one row per census
block group. A block group is the smallest geographical unit for which the U.S.
Census Bureau publishes sample data (a block group typically has a population
of 600 to 3,000 people).A household is a group of people residing within a home. Since the average
number of rooms and bedrooms in this dataset are provided per household, these
columns may take surprisingly large values for block groups with few households
and many empty houses, such as vacation resorts.It can be downloaded/loaded using the
:func:`sklearn.datasets.fetch_california_housing` function... rubric:: References- Pace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions,Statistics and Probability Letters, 33 (1997) 291-297
'''def download_file(url, directory, filename):# 確保目錄存在os.makedirs(directory, exist_ok=True)# 完整文件路徑filepath = os.path.join(directory, filename)# 下載文件response = requests.get(url, stream=True)response.raise_for_status()  # 檢查請求是否成功# 將內容寫入文件with open(filepath, 'wb') as file:for chunk in response.iter_content(chunk_size=8192):file.write(chunk)print(f"文件已下載到: {filepath}")def fetch_california_housing_manual():data_home = datasets.get_data_home()archive_path = os.path.join(data_home, 'cal_housing.tgz')if not os.path.exists(archive_path):download_file("https://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.tgz", data_home, 'cal_housing.tgz')with tarfile.open(mode="r:gz", name=archive_path) as f:cal_housing = np.loadtxt(f.extractfile("CaliforniaHousing/cal_housing.data"), delimiter=",")# Columns are not in the same order compared to the previous# URL resource on lib.stat.cmu.educolumns_index = [8, 7, 2, 3, 4, 5, 6, 1, 0]cal_housing = cal_housing[:, columns_index]feature_names = ["MedInc","HouseAge","AveRooms","AveBedrms","Population","AveOccup","Latitude","Longitude",]target_names = ['MedHouseVal']target, data = cal_housing[:, 0], cal_housing[:, 1:]# avg rooms = total rooms / householdsdata[:, 2] /= data[:, 5]# avg bed rooms = total bed rooms / householdsdata[:, 3] /= data[:, 5]# avg occupancy = population / householdsdata[:, 5] = data[:, 4] / data[:, 5]# target in units of 100,000target = target / 100000.0result = {'data': data,'target': target,'feature_names': feature_names,'target_names': target_names,'DESCR': fetch_california_housing_manual_desc,}obj = SimpleNamespace(**result)return objcalifornia = fetch_california_housing_manual()
print(california.data)