安装lazypredict

点击 Anaconda Prompt

1.创建虚拟环境

conda create -n py3.9 python=3.9

2.激活虚拟环境

conda activate py3.9

3.安装lazypredict

pip3 install lazypredict==0.2.7 numpy pandas tqdm scikit-learn xgboost lightgbm

4.安装ipykernel （第一次导入虚拟环境的要下载）

pip install -i https://pypi.tuna.tsinghua.edu.cn/simple ipykernel

5.将虚拟环境py3.9导入jupyter的kernel中(自己设置显示的名字为python3.9)

python -m ipykernel install --name py3.9 --display-name python3.9

6.查看kernel

jupyter kernelspec list

7.修改配置文件

进入 D:\mystudysoft\Anaconda3\envs\py3.9\Lib\site-packages\lazypredict 目录下

修改Supervised.py文件

修改import部分

# from sklearn.utils.testing import all_estimators
from sklearn.utils import all_estimators

在 removed_classifiers中修改如下内容

# sklearn.ensemble.gradient_boosting.GradientBoostingClassifier
sklearn.ensemble.GradientBoostingClassifier

# sklearn.gaussian_process.gpc.GaussianProcessClassifier
sklearn.gaussian_process._gpc.GaussianProcessClassifier

# sklearn.neural_network.multilayer_perceptron.MLPClassifier
sklearn.neural_network.MLPClassifier

# sklearn.linear_model.logistic.LogisticRegressionCV
sklearn.linear_model.LogisticRegressionCV

# sklearn.neighbors.classification.RadiusNeighborsClassifier
sklearn.neighbors.RadiusNeighborsClassifier

# sklearn.ensemble.voting.VotingClassifier
sklearn.ensemble.VotingClassifier

在removed_regressors中修改如下内容

# removed_regressors = [('TheilSenRegressor', sklearn.linear_model.theil_sen.TheilSenRegressor),
removed_regressors = [('TheilSenRegressor', sklearn.linear_model.TheilSenRegressor),

# 去掉这一行
('_SigmoidCalibration', sklearn.calibration._SigmoidCalibration)

保存退出

8.在jupyter中测试是否安装成功

选择python3.9

import lazypredict
from lazypredict.Supervised import LazyRegressor
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
data = load_breast_cancer()
x = data.data
y = data.target
x_train,x_test,y_train, y_test = train_test_split(x,y,test_size =.5,random_state =123)
clf = LazyRegressor(verbose=0,ignore_warnings=True, custom_metric=None)
models,predictions = clf.fit(x_train, x_test, y_train, y_test)
print(models)

其他的命令

1.退出当前虚拟环境（anaconda返回至base）：

conda deactivate

2.查看当前环境中有哪些包

conda list

3.查看当前有哪些虚拟环境

conda info --envs

4.删除虚拟环境（py3.9是虚拟环境名）

conda remove -n py3.9 --all

5.删除内核（python3.9是环境名）

jupyter kernelspec remove python3.9