cannot import name 'get_coordinate' from 'pyecharts.datasets' (C:\ProgramData\Anaconda3\lib\site-packages\pyecharts\datasets\__init__.py)

很抱歉，最新版的 pyecharts.datasets 模块中已经没有 get_coordinate 方法了，所以无法导入该方法。可以使用以下方式获取城市的经纬度坐标：

1. 安装 geopy 包

pip install geopy

2. 通过 geopy 包获取城市的经纬度坐标

from geopy.geocoders import Nominatim

geolocator = Nominatim(user_agent="my-application")
location = geolocator.geocode("北京")
print(location.latitude, location.longitude)

3. 将获取到的经纬度坐标添加到 pyecharts.geo 组件的坐标列表中

geo.add_coordinate("北京", 116.405285, 39.904989)

以上是一个简单的示例，您可以根据需要将代码进行修改。

相关问题

D:\Python311\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1807.7527351657197, tolerance: 1598.3259543826698 model = cd_fast.enet_coordinate_descent_gram( D:\Python311\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1808.192864873145, tolerance: 1598.3259543826698 model = cd_fast.enet_coordinate_descent_gram( D:\Python311\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1808.489093720222, tolerance: 1598.3259543826698 model = cd_fast.enet_coordinate_descent_gram(

这是一个 Python 中使用 scikit-learn 库进行线性回归时出现的警告信息，提示模型在迭代过程中未达到收敛，建议增加迭代次数。其中“duality gap”表示对偶间隙，是优化问题的一个指标，表示当前解与最优解之间的差距。而“tolerance”则表示收敛容忍度，即算法停止迭代的误差容忍度。

D:\龚致远的Pythoncharm文件夹\机器学习与资产定价\venv\lib\site-packages\sklearn\base.py:1151: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator return fit_method(estimator, *args, **kwargs) D:\龚致远的Pythoncharm文件夹\机器学习与资产定价\venv\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:628: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged. model = cd_fast.enet_coordinate_descent( D:\龚致远的Pythoncharm文件夹\机器学习与资产定价\venv\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.827e+00, tolerance: 4.400e-03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead. model = cd_fast.enet_coordinate_descent(

这是一些警告和提示信息，指出在使用线性回归模型时可能遇到的问题。警告中提到，当alpha参数为0时，该算法可能无法很好地收敛，建议使用LinearRegression估计器。另外，使用没有正则化的坐标下降方法可能导致意外的结果，并且不被推荐。还有一个收敛警告，建议增加迭代次数、检查特征的规模或者考虑增加正则化。最后，警告提到在l1正则化项的权重为空时，可以使用sklearn.linear_model.Ridge/RidgeCV中实现的求解器更高效地拟合线性回归模型。