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# This is a sample Python script.
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# Press Shift+F10 to execute it or replace it with your code.
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# Press Double Shift to search everywhere for classes, files, tool windows, actions, and settings.
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# # 任务：是否患有糖尿病（二分类）
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# 模型：LR
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# 数据集：皮马人糖尿病数据集
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import pandas as pd
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import matplotlib.pyplot as plt
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import seaborn as sns
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from sklearn.model_selection import train_test_split
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from sklearn.linear_model import LogisticRegression
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from sklearn.metrics import accuracy_score
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from sklearn.metrics import precision_score
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from sklearn.metrics import recall_score
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from sklearn.metrics import classification_report
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from sklearn.metrics import confusion_matrix
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from sklearn.metrics import roc_curve
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from sklearn.metrics import roc_auc_score
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from sklearn.preprocessing import StandardScaler
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import csv
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def run(csvf):
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# 切分数据集
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df = pd.read_csv(csvf)
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print(df)
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isDetct = df.pop("isDetect")
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target = df.pop("flag")
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data = df.values
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X = data
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Y = target
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X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=7)
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# 数据标准化
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ss = StandardScaler()
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X_train_s = ss.fit_transform(X_train)
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X_test_s = ss.transform(X_test)
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# 输出下原数据的标准差和平均数
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print(ss.scale_)
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print(ss.mean_)
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# LR模型预测
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lr = LogisticRegression() #初始化LogisticRegression
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lr.fit(X_train_s, Y_train) # 调用LogisticRegression中的fit函数训练模型参数
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lr_pres = lr.predict(X_test_s) # 使用训练好的模型lr对X_test进行预测
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print(lr.coef_)
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print(lr.intercept_)
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print('准确率：', accuracy_score(Y_test, lr_pres))
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print('精确率：', precision_score(Y_test, lr_pres))
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print('召回率：', recall_score(Y_test, lr_pres))
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# 混淆矩阵热点图
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labels = [0, 1]
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cm = confusion_matrix(Y_test, lr_pres, labels=labels)
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sns.heatmap(cm, annot=True, annot_kws={'size': 20,'weight': 'bold', 'color': 'blue'})
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#plt.rc('font', family='Arial Unicode MS', size=14)
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plt.rcParams['font.sans-serif'] = ['KaiTi']
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plt.title('混淆矩阵', fontsize=20)
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plt.xlabel('Actual', fontsize=14)
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plt.ylabel('Predict', fontsize=14)
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plt.show()
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# ROC曲线和AUC
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lr_pres_proba = lr.predict_proba(X_test)[::, 1]
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fpr, tpr, thresholds = roc_curve(Y_test, lr_pres_proba)
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auc = roc_auc_score(Y_test, lr_pres_proba)
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plt.figure(figsize=(5, 3), dpi=100)
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plt.plot(fpr, tpr, label="AUC={:.2f}" .format(auc))
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plt.legend(loc=4, fontsize=10)
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plt.title('信号干扰检测LR分类的ROC和AUC', fontsize=10)
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plt.xlabel('FPR', fontsize=14)
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plt.ylabel('TPR', fontsize=14)
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plt.show()
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# Press the green button in the gutter to run the script.
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if __name__ == '__main__':
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csvf = '20221003-17_47-00-model.csv'
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run(csvf)
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# See PyCharm help at https://www.jetbrains.com/help/pycharm/
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