信用卡欺诈案例
数据集下载地址
https://storage.googleapis.com/download.tensorflow.org/data/creditcard.csv
参考不平衡数据的分类

只进行特征衍生，未进行数据标准化、上才样处理数据不平衡问题，得到的准确率和召回率居然很高

import pandas as pd 
pd.set_option('display.float_format',lambda x: '%.2f' %x)
data = pd.read_csv('C:/Users/Administrator/Downloads/creditcard.csv')
data.head()

如果不处理数据不平衡问题，进行过采样，召回率只有0.64了结果如下

import pandas as pd 
pd.set_option('display.float_format',lambda x: '%.2f' %x)
data = pd.read_csv('C:/Users/Administrator/Downloads/creditcard.csv')
data['Hour'] = data['Time'].apply(lambda x : divmod(x,3600)[0])
feature = list(data.columns)
feature.remove('Class')
feature.remove('Time')
X = data[feature]
y = data['Class']
display(X.head(),y.head())
from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model.fit(X,y)
y_ = model.predict(X)
from sklearn.metrics import auc,roc_auc_score,roc_curve,recall_score,accuracy_score,classification_report,confusion_matrix
print('LogisticRegression准确率是：',accuracy_score(y,y_))
cm = confusion_matrix(y,y_)
recall = cm[1,1]/(cm[1,1]+cm[1,0])
print('LogisticRegression召回率率是：',recall)

proba_ = model.predict_proba(X)[:,1] #表示获取类别1的样本阳性，行用卡盗刷
fpr,tpr,thresholds = roc_curve(y,proba_)
roc_auc = auc(fpr,tpr) #曲线下的面积

import matplotlib.pyplot as plt
plt.title=('Receiver Operating Characteristic')
plt.plot(fpr,tpr,'b',label='AUC = %0.5f'% roc_auc)
plt.legend(loc='lower right')
plt.plot([0,1],[0,1],'r--')
plt.xlim([-0.1,1.0])
plt.ylim([-0.1,1.0])
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')

使用上采样处理数据不平衡问题，数据标准化处理，得到的准确率反而没那么高了

import pandas as pd 
pd.set_option('display.float_format',lambda x: '%.2f' %x)
data = pd.read_csv('C:/Users/Administrator/Downloads/creditcard.csv')
data['Hour'] = data['Time'].apply(lambda x : divmod(x,3600)[0]) #特征衍生

#标准化
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
col = ['Amount','Hour']
data[col] = sc.fit_transform(data[col])

feature = list(data.columns)
feature.remove('Class') #剔除标签列
feature.remove('Time') #特征清洗
X = data[feature]  #特征列
y = data['Class']  #目标值列

display(X.head(),y.head())

#过采样或者称上才采样，使用 最近邻插值（Nearest Neighbor Interpolation）：直接使用最接近的像素值作为新的像素值
from imblearn.over_sampling import SMOTE
smote = SMOTE()
X,y = smote.fit_resample(X,y)
y.value_counts()

#模型训练
from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model.fit(X,y)
y_ = model.predict(X)
from sklearn.metrics import auc,roc_auc_score,roc_curve,recall_score,accuracy_score,classification_report,confusion_matrix
print('LogisticRegression准确率是：',accuracy_score(y,y_))
cm = confusion_matrix(y,y_) #获得混淆矩阵
recall = cm[1,1]/(cm[1,1]+cm[1,0])
print('LogisticRegression召回率率是：',recall)

proba_ = model.predict_proba(X)[:,1] #表示获取类别1的样本阳性，行用卡盗刷
fpr,tpr,thresholds = roc_curve(y,proba_)
roc_auc = auc(fpr,tpr) #曲线下的面积

import matplotlib.pyplot as plt
plt.title=('Receiver Operating Characteristic')
plt.plot(fpr,tpr,'b',label='AUC = %0.5f'% roc_auc)
plt.legend(loc='lower right')
plt.plot([0,1],[0,1],'r--')
plt.xlim([-0.1,1.0])
plt.ylim([-0.1,1.0])
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')

交叉验证，接着以上代码

%%time
from sklearn.model_selection import GridSearchCV, train_test_split
#交叉验证， 筛选合适的参数
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size = 0.2)
#构建参数组合
param_grid = {'C': [0.01,0.1,1,10,100,1000,], 'penalty': ['l2']}
grid_search = GridSearchCV(LogisticRegression(),param_grid,cv=10)
grid_search.fit(X_train,y_train) #使用训练集学习散发

报以下错误

将l1去掉，重跑一次，运行无报错，结果如下

#查看最佳参数
results = pd.DataFrame(grid_search.cv_results_)
display(results)
print("Best parammeters:{}".format(grid_search.best_params_))
print("Best cross-validation score: {:.5f}".format(grid_search.best_score_))

测评数据的评估

混淆矩阵和召回率

def plot_confusion_matrix(cm,classes,title, cmap=plt.cm.Blues):
    plt.imshow(cm,interpolation='nearest', cmap=cmap)
   # plt.title(title)
    plt.colorbar()
    tick_marks = np.arange(len(classes))
    plt.xticks(tick_marks, classes, rotation=0)
    plt.yticks(tick_marks, classes)

    thresh = cm.max()/2.
    for i, j in itertools.product(range(cm.shape[0]),range(cm.shape[1])):
        plt.text(j, i , cm[i, j],
                 horizontalalignment="center",
                 color="white" if cm[i,j] > thresh else "black")
    plt.tight_layout()
    plt.ylabel('True label')
    plt.xlabel('Predicted label')

import itertools
cnf_matrix = confusion_matrix(y_test,y_pred) #获得混淆矩阵
recall1 = cnf_matrix[1,1]/(cnf_matrix[1,1]+cnf_matrix[1,0])
print('LogisticRegression召回率率是：',recall1)
#绘制模型优化后的混淆矩阵
class_names=[0,1]
plt.figure()
plot_confusion_matrix(cnf_matrix
                      , classes=class_names
                      , title = 'Confusion matrix'
                      )