import pandas as pd import numpy as np import scipy.stats as stats import seaborn as sns from sklearn.metrics import RocCurveDisplay from sklearn.model_selection import train_test_split from sklearn.metrics import confusion_matrix,accuracy_score from sklearn.preprocessing import StandardScaler from sklearn.linear_model import LogisticRegression from sklearn import tree from sklearn.decomposition import PCA import matplotlib.pyplot as plt from sklearn.tree import DecisionTreeClassifier data = pd.read_csv("C:\\Users\\sa'y\\Desktop\\framinghamData(1).csv") X = data[["sysBP", "diaBP", "age", "totChol", "BMI", "heartRate", "glucose"]] y = data[['TenYearCHD']] new_data = pd.concat([X, y], axis=1) print(new_data.head()) scaler = StandardScaler() X_scaled = scaler.fit_transform(X) print(X_scaled) corr = new_data.corr(method='pearson') sns.heatmap(corr, annot=True, cmap='coolwarm') plt.show() #对数据进行主成分分析，展示每个主成分的贡献率 pca = PCA(n_components=7) newX = pca.fit_transform(X) x_data = ['PC1','PC2','PC3','PC4','PC5','PC6','PC7'] y_data = np.around(pca.explained_variance_ratio_, 2) plt.bar(x=x_data, height=y_data,color='steelblue', alpha=0.8) plt.show() 在上述代码基础上，请给出下一步的代码，要求是： 计算所有主成分之间的皮尔逊相关系数，并用热图Heatmap的形式展示出来

这段代码使用Python语言编写，主要是为了对一个人群的生理指标进行分析并探索这些指标与其心血管疾病患病风险之间的关系。具体的步骤包括数据导入、数据预处理、特征工程、探索性数据分析、模型选择与评估等。主要依赖于一些机器学习算法库和数据科学工具包，如pandas、numpy、scipy、seaborn和matplotlib等。其中，Logistic回归、决策树和PCA等算法被用来对特征进行筛选和预测建模，而皮尔逊相关系数和热力图则用来表现各个特征之间的相关性。最终分析结果可供医生或者保险公司等相关机构做出风险评估和风险管理决策。

相关问题

import pandas as pd import numpy as np import scipy.stats as stats import seaborn as sns from sklearn.metrics import RocCurveDisplay from sklearn.model_selection import train_test_split from sklearn.metrics import confusion_matrix,accuracy_score from sklearn.preprocessing import StandardScaler from sklearn.linear_model import LogisticRegression from sklearn import tree from sklearn.decomposition import PCA import matplotlib.pyplot as plt from sklearn.tree import DecisionTreeClassifier请在此基础上续写代码块，要求是（1） 读入数据后，选取自变量"sysBP", "diaBP","age","totChol","BMI", "heartRate", "glucose"记为X，因变量"TenYearCHD"记为y，组成新的数据集。¶

import pandas as pd
import numpy as np
import scipy.stats as stats
import seaborn as sns
from sklearn.metrics import RocCurveDisplay
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix,accuracy_score
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn import tree
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
from sklearn.tree import DecisionTreeClassifier

# 读入数据
data = pd.read_csv("heart.csv")

# 选取自变量
X = data[["sysBP", "diaBP", "age", "totChol", "BMI", "heartRate", "glucose"]]
# 因变量
y = data["TenYearCHD"]

# 组成新的数据集
new_data = pd.concat([X, y], axis=1)

# 打印新的数据集
print(new_data.head())

解释这段代码import numpy as np import pandas as pd from datetime import datetime from scipy.stats import skew from scipy.special import boxcox1p from scipy.stats import boxcox_normmax from sklearn.linear_model import ElasticNetCV, LassoCV, RidgeCV, Ridge from sklearn.ensemble import GradientBoostingRegressor from sklearn.svm import SVR from sklearn.pipeline import make_pipeline from sklearn.preprocessing import RobustScaler, StandardScaler from sklearn.model_selection import KFold, cross_val_score from sklearn.metrics import mean_squared_error as mse from sklearn.metrics import make_scorer from sklearn.neighbors import LocalOutlierFactor from sklearn.linear_model import LinearRegression from mlxtend.regressor import StackingCVRegressor # from xgboost import XGBRegressor # from lightgbm import LGBMRegressor import matplotlib.pyplot as plt import seaborn as sns

这段代码是在Python中导入所需要的库和模块。具体解释如下：

- `import numpy as np`：导入NumPy库，并将其简写为np，以方便使用。
- `import pandas as pd`：导入Pandas库，并将其简写为pd，以方便使用。
- `from datetime import datetime`：从datetime模块中导入datetime函数，用于处理时间数据。
- `from scipy.stats import skew`：从scipy.stats模块中导入skew函数，用于计算数据的偏度。
- `from scipy.special import boxcox1p`：从scipy.special模块中导入boxcox1p函数，用于进行Box-Cox变换。
- `from scipy.stats import boxcox_normmax`：从scipy.stats模块中导入boxcox_normmax函数，用于计算Box-Cox变换的参数。
- `from sklearn.linear_model import ElasticNetCV, LassoCV, RidgeCV, Ridge`：从sklearn.linear_model模块中导入ElasticNetCV、LassoCV、RidgeCV、Ridge等函数，用于进行线性回归。
- `from sklearn.ensemble import GradientBoostingRegressor`：从sklearn.ensemble模块中导入GradientBoostingRegressor函数，用于进行梯度提升回归。
- `from sklearn.svm import SVR`：从sklearn.svm模块中导入SVR函数，用于进行支持向量回归。
- `from sklearn.pipeline import make_pipeline`：从sklearn.pipeline模块中导入make_pipeline函数，用于构建机器学习管道。
- `from sklearn.preprocessing import RobustScaler, StandardScaler`：从sklearn.preprocessing模块中导入RobustScaler、StandardScaler函数，用于进行特征缩放。
- `from sklearn.model_selection import KFold, cross_val_score`：从sklearn.model_selection模块中导入KFold、cross_val_score函数，用于进行交叉验证。
- `from sklearn.metrics import mean_squared_error as mse`：从sklearn.metrics模块中导入mean_squared_error函数，并将其简写为mse，用于计算均方误差。
- `from sklearn.metrics import make_scorer`：从sklearn.metrics模块中导入make_scorer函数，用于创建自定义评分函数。
- `from sklearn.neighbors import LocalOutlierFactor`：从sklearn.neighbors模块中导入LocalOutlierFactor函数，用于检测异常值。
- `from sklearn.linear_model import LinearRegression`：从sklearn.linear_model模块中导入LinearRegression函数，用于进行线性回归。
- `from mlxtend.regressor import StackingCVRegressor`：从mlxtend.regressor模块中导入StackingCVRegressor函数，用于进行交叉验证的堆叠模型。
- `import matplotlib.pyplot as plt`：导入matplotlib库，并将其简写为plt，用于绘制图形。
- `import seaborn as sns`：导入seaborn库，并将其简写为sns，用于绘制图形。