python评分卡之LR及混淆矩阵、ROC

import pandas as pd

import numpy as np

from sklearn import linear_model

# 读取数据

sports = pd.read_csv(r'C:\Users\Administrator\Desktop\Run or Walk.csv')

# 提取出所有自变量名称

predictors = sports.columns[4:]

# 构建自变量矩阵

X = sports.ix[:,predictors]

# 提取y变量值

y = sports.activity

# 将数据集拆分为训练集和测试集

X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size = 0.25, random_state = 1234)

# 利用训练集建模

sklearn_logistic = linear_model.LogisticRegression()

sklearn_logistic.fit(X_train, y_train)

# 返回模型的各个参数

print(sklearn_logistic.intercept_, sklearn_logistic.coef_)

# 模型预测

sklearn_predict = sklearn_logistic.predict(X_test)

# 预测结果统计

pd.Series(sklearn_predict).value_counts()

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# 导入第三方模块

from sklearn import metrics

# 混淆矩阵

cm = metrics.confusion_matrix(y_test, sklearn_predict, labels = [0,1])

cm

Accuracy = metrics.scorer.accuracy_score(y_test, sklearn_predict)

Sensitivity = metrics.scorer.recall_score(y_test, sklearn_predict)

Specificity = metrics.scorer.recall_score(y_test, sklearn_predict, pos_label=0)

print('模型准确率为%.2f%%:' %(Accuracy*100))

print('正例覆盖率为%.2f%%' %(Sensitivity*100))

print('负例覆盖率为%.2f%%' %(Specificity*100))

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# 混淆矩阵的可视化

# 导入第三方模块

import seaborn as sns

import matplotlib.pyplot as plt

# 绘制热力图

sns.heatmap(cm, annot = True, fmt = '.2e',cmap = 'GnBu')

plt.show()

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# 绘制ROC曲线

# 计算真正率和假正率

fpr,tpr,threshold = metrics.roc_curve(y_test, sm_y_probability)

# 计算auc的值 

roc_auc = metrics.auc(fpr,tpr)

# 绘制面积图

plt.stackplot(fpr, tpr, color='steelblue', alpha = 0.5, edgecolor = 'black')

# 添加边际线

plt.plot(fpr, tpr, color='black', lw = 1)

# 添加对角线

plt.plot([0,1],[0,1], color = 'red', linestyle = '--')

# 添加文本信息

plt.text(0.5,0.3,'ROC curve (area = %0.2f)' % roc_auc)

# 添加x轴与y轴标签

plt.xlabel('1-Specificity')

plt.ylabel('Sensitivity')

plt.show()

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#ks曲线   链接：https://www.jianshu.com/p/b1b1344bd99f 风控数据分析学习笔记（二）Python建立信用评分卡 - 简书

fig, ax = plt.subplots()

ax.plot(1 - threshold, tpr, label='tpr')# ks曲线要按照预测概率降序排列，所以需要1-threshold镜像

ax.plot(1 - threshold, fpr, label='fpr')

ax.plot(1 - threshold, tpr-fpr,label='KS')

plt.xlabel('score')

plt.title('KS Curve')

plt.ylim([0.0, 1.0])

plt.figure(figsize=(20,20))

legend = ax.legend(loc='upper left')

plt.show()