Logistic regression predicts blood stasis and collateral syndrome||LogRegression binary classification python3|50-fold cross-validation

Require

Divide the data set into training set and test set and use logistic regression training and prediction to obtain the corresponding classification index accuracy rate, precision rate precision, recall rate recall, F1-score, and draw the final ROC curve to obtain AUC value.

Data Format

 664 samples, each with 103 attributes, and the last column is the label

 result

 

the code

import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import classification_report, roc_curve, auc
import matplotlib.pyplot as plt
 
#根据标签和预测概率结果画出ROC图，并计算AUC值
def acu_curve(y, prob):
    fpr, tpr, threshold = roc_curve(y, prob)  ###计算真正率和假正率
    roc_auc = auc(fpr, tpr)  ###计算auc的值
    plt.figure()
    lw = 2
    plt.figure(figsize=(6, 6))
    plt.plot(fpr, tpr, color='darkorange',
             lw=lw, label='ROC curve (area = %0.3f)' % roc_auc)  ###假正率为横坐标，真正率为纵坐标做曲线
    plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')
    plt.xlim([0.0, 1.0])
    plt.ylim([0.0, 1.05])
    plt.xlabel('False Positive Rate')
    plt.ylabel('True Positive Rate')
    plt.title('Receiver operating characteristic')
    plt.legend(loc="lower right")
    plt.show()
 
 
# 二分类，逻辑回归 瘀血阻络证预测
 
# 读取数据  (如果不指定标签名，会默认把第一行数据当成标签名)
data = pd.read_excel(
    "症状_瘀血阻络证_data.xlsx"
)
# print(data)
 
# 缺失值进行处理  (原始数据中的?表示缺失值)  本样本无缺失值
# data = data.replace(to_replace='?', value=np.nan)
# data = data.dropna()  # 删除有缺少值的行
 
# 分割数据集 划分为训练集和测试集
x_train, x_test, y_train, y_test = train_test_split(data.iloc[:, 0:103], data.iloc[:, 103:104],
                                                    test_size=0.125)  # 前103为属性；最后一列是目标值
# 7:1划分；83条测试、581条训练
# 进行标准化处理   因为目标结果经过sigmoid函数转换成了[0,1]之间的概率，所以目标值不需要进行标准化。
std = StandardScaler()
x_train = std.fit_transform(x_train)
x_test = std.transform(x_test)
 
# 逻辑回归预测
lg = LogisticRegression(C=1.0)  # 默认使用L2正则化避免过拟合，C=1.0表示正则力度(超参数，可以调参调优)
lg.fit(x_train, y_train.values.ravel())
 
# 回归系数
# print(lg.coef_)
 
# 进行预测
y_predict_p = lg.predict_proba(x_test)#结果用类别概率表示 方便画ROC图
#print(y_predict_p)
y_predict = lg.predict(x_test)#结果用标签值表示 方便利用classification_report()函数 输出模型评估报告
#print(y_predict)
 
# 用classification_report()函数 输出模型评估报告
# 原始数据中的目标值：0表示非瘀血阻络证，1表示瘀血阻络证
print(classification_report(y_test,y_predict))
 
#调用函数画出ROC图，并计算AUC值
acu_curve(y_test,y_predict_p[:,1:2])

Using 5-fold cross-validation

Result display

 

update it

import pandas as pd
import warnings
from sklearn.metrics import roc_curve
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import cross_val_predict
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score
import matplotlib.pyplot as plt

# 二分类  五折验证  逻辑回归 瘀血阻络证预测

# 读取数据
data = pd.read_excel(
    "症状_瘀血阻络证_data.xlsx"
)
# print(data)

x_train = data.iloc[:, :-1]  # 特征
y_train = data.iloc[:, -1]  # 标签
# 进行标准化处理   因为目标结果经过sigmoid函数转换成了[0,1]之间的概率，所以目标值不需要进行标准化。
std = StandardScaler()
x_train = std.fit_transform(x_train)

lr = LogisticRegression(random_state=填入你的随机数, tol=1e-6, C=0.01)
lr_result = lr.fit(x_train, y_train.ravel())
'''estimator:估计方法对象(分类器)
X：数据特征(Features)
y：数据标签(Labels)
cv：几折交叉验证
scoring='accuracy'：准确率为结果的度量指标'''


def muti_score(model):
    warnings.filterwarnings('ignore')
    accuracy = cross_val_score(model, x_train, y_train, scoring='accuracy', cv=5)
    precision = cross_val_score(model, x_train, y_train, scoring='precision', cv=5)
    recall = cross_val_score(model, x_train, y_train, scoring='recall', cv=5)
    f1_score = cross_val_score(model, x_train, y_train, scoring='f1', cv=5)
    auc = cross_val_score(model, x_train, y_train, scoring='roc_auc', cv=5)
    print("准确率:", accuracy.mean())
    print("精确率:", precision.mean())
    print("召回率:", recall.mean())
    print("F1_score:", f1_score.mean())
    print("AUC:", auc.mean())
    #画ROC
    y_scores = cross_val_predict(lr, x_train, y_train, cv=5, method='decision_function')
    fpr, tpr, thresholds = roc_curve(y_train, y_scores)
    plt.plot(fpr, tpr, linewidth=2, label='ROC(AUC=%0.3f)' % cross_val_score(lr, x_train, y_train, cv=5, scoring='roc_auc').mean(),
             color='darkorange')
    plt.xlabel('FPR')  # False Positive Rate,假阳性率
    plt.ylabel('TPR')  # True Positive Rate,真阳性率
    plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
    plt.ylim(0, 1.05)
    plt.xlim(0, 1.05)
    plt.legend(loc=4)
    plt.show()


model = eval("lr")
muti_score(model)

important point

1. How to use iloc function (1 message) How to use iloc function

2. y_train.values.ravel() (1 message) DataConversionWarning: A column-vector y was passed when a 1d array was expected. Problem solving_weixin_39223665's blog-CSDN blog https://blog.csdn.net/weixin_39223665 /article/details/81268741 3. classification_report() (1 message) Python machine learning classification_report() function outputs model evaluation report_Hou Xiaojiu's Blog-CSDN Blog_classification_report function https://blog.csdn.net/weixin_48964486 /article/details/122881350

4. ROC graph (1 message) There is only one point when the ROC curve is drawn