from scikit-learn.linear_model import LinearRegression from scikit-learn.metrics import r2_score from scikit-learn.ensemble import RandomForestRegressor from spsspro.algorithm import statistical_model_analysis X=pd.get_dummies(data_merge[['纹饰','类型','颜色','表面风化']]) for i in ['二氧化硅(SiO2)','氧化钠(Na2O)', '氧化钾(K2O)','氧化钙(CaO)','氧化镁(MgO)','氧化铝(Al2O3)','氧化铁(Fe2O3)', '氧化铜(CuO)','氧化铅(PbO)','氧化钡(BaO)','五氧化二磷(P2O5)','氧化锶(SrO)', '氧化锡(SnO2)','二氧化硫(SO2)']: y=data_merge[i] result=statistical_model_analysis.linear_regression(data_y=y,data_x1=X) print('\n') print(i) print(result['linear_regression_form']['r2']) print(result['linear_regression_analysis_description'])
时间: 2023-08-20 19:00:34 浏览: 72
这段代码使用了scikit-learn(sklearn)库中的线性回归(LinearRegression)和随机森林回归(RandomForestRegressor)模型,以及评估指标r2_score。另外,代码还调用了一个名为'spsspro'的自定义模块,其中包含了一个名为'statistical_model_analysis'的函数。
在这段代码中,首先使用了pandas库中的'pd'别名来导入数据并进行预处理。然后,对于给定的一组特征(纹饰、类型、颜色、表面风化),使用pd.get_dummies函数进行独热编码处理。
接下来,对于每个目标变量('二氧化硅(SiO2)'、'氧化钠(Na2O)'等等),将其赋值给变量y,并调用statistical_model_analysis.linear_regression函数进行线性回归分析。然后打印出回归模型的R^2值(result['linear_regression_form']['r2'])以及分析描述(result['linear_regression_analysis_description'])。最后,逐个处理完所有目标变量。
请注意,这段代码中的'spsspro'模块是自定义模块,您可能需要确保在您的环境中正确安装并导入该模块才能运行成功。
相关问题
Input In [211] from scikit-learn.linear_model import LinearRegression ^ SyntaxError: invalid syntax
这个错误是由于`from scikit-learn.linear_model import LinearRegression`这行代码中的语法错误导致的。正确的导入语句应该是`from sklearn.linear_model import LinearRegression`。
请注意,在导入模块时,模块名是`sklearn`而不是`scikit-learn`。您可以使用以下代码进行修正:
```python
from sklearn.linear_model import LinearRegression
```
同样的修改也适用于其他导入语句,如`from scikit-learn.metrics import r2_score`和`from scikit-learn.ensemble import RandomForestRegressor`。
请确保在所有导入语句中使用正确的模块名,并重新运行代码。
在Python中想运行from sklearn.model_selection import train_test_split from sklearn.decomposition import PCA import pandas as pd from sklearn import svm import numpy as np import math import matplotlib.pyplot as plt import matplotlib as mpl from matplotlib import colors from sklearn.model_selection import train_test_split from sklearn import datasets from matplotlib.colors import ListedColormap from sklearn.svm import SVC from sklearn.preprocessing import StandardScaler from sklearn.model_selection import StratifiedShuffleSplit,StratifiedKFold from sklearn.model_selection import GridSearchCV from sklearn.model_selection import GridSearchCV, LeaveOneOut, cross_val_predict from sklearn.model_selection import KFold from sklearn.linear_model import LogisticRegression from sklearn.naive_bayes import GaussianNB from sklearn.neighbors import KNeighborsClassifier from sklearn import svm from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import roc_auc_score from sklearn.metrics import roc_auc_score import math import datetime import multiprocessing as mp from sklearn.ensemble import StackingClassifier from sklearn.pipeline import make_pipeline from sklearn.svm import LinearSVC import random怎么操作
要在Python中运行这些代码,你需要确保已经安装了相应的包。大多数包都可以使用`pip`进行安装。
首先,在命令行中运行以下命令来安装所需的包:
```bash
pip install scikit-learn pandas matplotlib numpy
```
然后,在你的Python脚本中导入所需的库:
```python
from sklearn.model_selection import train_test_split
from sklearn.decomposition import PCA
import pandas as pd
from sklearn import svm
import numpy as np
import math
import matplotlib.pyplot as plt
import matplotlib as mpl
from matplotlib import colors
from sklearn.model_selection import train_test_split
from sklearn import datasets
from matplotlib.colors import ListedColormap
from sklearn.svm import SVC
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import StratifiedShuffleSplit, StratifiedKFold
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import GridSearchCV, LeaveOneOut, cross_val_predict
from sklearn.model_selection import KFold
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import roc_auc_score
import datetime
import multiprocessing as mp
from sklearn.ensemble import StackingClassifier
from sklearn.pipeline import make_pipeline
from sklearn.svm import LinearSVC
import random
```
请确保在运行这些代码之前,已经安装了所需的Python库。如果遇到任何问题,请确保已正确安装这些库,并且版本与代码兼容。