信用卡欺诈案例（终结）

该案例主要包含着：

 1、不平衡样本的采样方法 
 2、sklearn中进行模型训练的整个过程（从单一模块组合到优化方法都包括了）

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline

data = pd.read_csv("creditcard.csv")
data.head()

	Time	V1	V2	V3	V4	V5	V6	V7	V8	V9	…	V21	V22	V23	V24	V25	V26	V27	V28	Amount	Class
0	0.0	-1.359807	-0.072781	2.536347	1.378155	-0.338321	0.462388	0.239599	0.098698	0.363787	…	-0.018307	0.277838	-0.110474	0.066928	0.128539	-0.189115	0.133558	-0.021053	149.62	0

5 rows × 31 columns

一、按照类别统计数目，观察样本是否平衡

count_classes = pd.value_counts(data['Class'], sort = True).sort_index()
count_classes.plot(kind = 'bar')
plt.title("Fraud class histogram")
plt.xlabel("Class")
plt.ylabel("Frequency")

png

二、规约化震荡数据，生成新的特征

from sklearn.preprocessing import StandardScaler

data['normAmount'] = StandardScaler().fit_transform(data['Amount'].reshape(-1, 1))
data = data.drop(['Time','Amount'],axis=1)
data.head()

	V1	V2	V3	V4	V5	V6	V7	V8	V9	V10	…	V21	V22	V23	V24	V25	V26	V27	V28	Class	normAmount
0	-1.359807	-0.072781	2.536347	1.378155	-0.338321	0.462388	0.239599	0.098698	0.363787	0.090794	…	-0.018307	0.277838	-0.110474	0.066928	0.128539	-0.189115	0.133558	-0.021053	0	0.244964

5 rows × 30 columns

下采样过程（始终关注于下标）

X = data.ix[:, data.columns != 'Class']
y = data.ix[:, data.columns == 'Class']

# Number of data points in the minority class
number_records_fraud = len(data[data.Class == 1])
fraud_indices = np.array(data[data.Class == 1].index)

# Picking the indices of the normal classes
normal_indices = data[data.Class == 0].index

# Out of the indices we picked, randomly select "x" number (number_records_fraud)
random_normal_indices = np.random.choice(normal_indices, number_records_fraud, replace = False)
random_normal_indices = np.array(random_normal_indices)

# Appending the 2 indices
under_sample_indices = np.concatenate([fraud_indices,random_normal_indices])

# Under sample dataset
under_sample_data = data.iloc[under_sample_indices,:]

X_undersample = under_sample_data.ix[:, under_sample_data.columns != 'Class']
y_undersample = under_sample_data.ix[:, under_sample_data.columns == 'Class']

# Showing ratio
print("Percentage of normal transactions: ", len(under_sample_data[under_sample_data.Class == 0])/len(under_sample_data))
print("Percentage of fraud transactions: ", len(under_sample_data[under_sample_data.Class == 1])/len(under_sample_data))
print("Total number of transactions in resampled data: ", len(under_sample_data))

三、数据集切分

from sklearn.cross_validation import train_test_split

# Whole dataset
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size = 0.3, random_state = 0)

print("Number transactions train dataset: ", len(X_train))
print("Number transactions test dataset: ", len(X_test))
print("Total number of transactions: ", len(X_train)+len(X_test))

# Undersampled dataset
X_train_undersample, X_test_undersample, y_train_undersample, y_test_undersample = train_test_split(X_undersample
                                                                                                   ,y_undersample
                                                                                                   ,test_size = 0.3
                                                                                                   ,random_state = 0)
print("")
print("Number transactions train dataset: ", len(X_train_undersample))
print("Number transactions test dataset: ", len(X_test_undersample))
print("Total number of transactions: ", len(X_train_undersample)+len(X_test_undersample))

('Number transactions train dataset: ', 199364)
('Number transactions test dataset: ', 85443)
('Total number of transactions: ', 284807)

('Number transactions train dataset: ', 688)
('Number transactions test dataset: ', 296)
('Total number of transactions: ', 984)

#Recall = TP/(TP+FN)
from sklearn.linear_model import LogisticRegression
from sklearn.cross_validation import KFold, cross_val_score
from sklearn.model_selection import cross_validate
from sklearn.metrics import confusion_matrix,recall_score,classification_report

三、（1）传统的模型选择方法

def select_model_by_traditional(x_train_data,y_train_data):
    fold = KFold(len(y_train_data),5,shuffle = False) 

    # Different C parameters
    c_param_range = [0.01,0.1,1,10,100]

    results_table = pd.DataFrame(index = range(len(c_param_range),2), columns = ['C_parameter','Mean recall score'])

    # the k-fold will give 2 lists: train_indices = indices[0], test_indices = indices[1]
    j = 0
    for c_param in c_param_range:
        print('-------------------------------------------')
        print('C parameter: ', c_param)
        print('-------------------------------------------')
        print('')

        recall_accs = []

        # enumerate(a,b)
        # list1 = ["这", "是", "一个", "测试"]
        #for index, item in enumerate(list1, 1):
        #print index, item
        #1 这
        #2 是
        #3 一个

        for iteration, indices in enumerate(fold,start=1):

            lr = LogisticRegression(C = c_param, penalty = 'l1')

            # Use the training data to fit the model. In this case, we use the portion of the fold to train the model
            # with indices[0]. We then predict on the portion assigned as the 'test cross validation' with indices[1]
            lr.fit(x_train_data.iloc[indices[0],:],y_train_data.iloc[indices[0],:].values.ravel())

            # Predict values using the test indices in the training data
            y_pred_undersample = lr.predict(x_train_data.iloc[indices[1],:].values)

            # Calculate the recall score and append it to a list for recall scores representing the current c_parameter
            recall_acc = recall_score(y_train_data.iloc[indices[1],:].values,y_pred_undersample)
            recall_accs.append(recall_acc)
            print('Iteration ', iteration,': recall score = ', recall_acc)

        # The mean value of those recall scores is the metric we want to save and get hold of.
        results_table.loc[j,'C_parameter'] = c_param
        results_table.loc[j,'Mean recall score'] = np.mean(recall_accs)
        print results_table.iloc[j]

        j += 1
        print('')
        print('Mean recall score ', np.mean(recall_accs))
        print('')

    best_c = results_table.iloc[results_table['Mean recall score'].idxmax()]['C_parameter']

    # Finally, we can check which C parameter is the best amongst the chosen.
    print('*********************************************************************************')
    print('Best model to choose from cross validation is with C parameter = ', best_c)
    print('*********************************************************************************')

    return best_c

best_c = select_model_by_traditional(X_train_undersample,y_train_undersample)

-------------------------------------------
('C parameter: ', 0.01)
-------------------------------------------

('Iteration ', 1, ': recall score = ', 0.93150684931506844)
('Iteration ', 2, ': recall score = ', 0.9178082191780822)
('Iteration ', 3, ': recall score = ', 1.0)
('Iteration ', 4, ': recall score = ', 0.97297297297297303)
('Iteration ', 5, ': recall score = ', 0.95454545454545459)
C_parameter              0.01
Mean recall score    0.955367
Name: 0, dtype: object

('Mean recall score ', 0.95536669920231565)

-------------------------------------------
('C parameter: ', 0.1)
-------------------------------------------

('Iteration ', 1, ': recall score = ', 0.84931506849315064)
('Iteration ', 2, ': recall score = ', 0.86301369863013699)
('Iteration ', 3, ': recall score = ', 0.94915254237288138)
('Iteration ', 4, ': recall score = ', 0.94594594594594594)
('Iteration ', 5, ': recall score = ', 0.90909090909090906)
C_parameter               0.1
Mean recall score    0.903304
Name: 1, dtype: object

('Mean recall score ', 0.90330363290660487)

-------------------------------------------
('C parameter: ', 1)
-------------------------------------------

('Iteration ', 1, ': recall score = ', 0.84931506849315064)
('Iteration ', 2, ': recall score = ', 0.87671232876712324)
('Iteration ', 3, ': recall score = ', 0.98305084745762716)
('Iteration ', 4, ': recall score = ', 0.94594594594594594)
('Iteration ', 5, ': recall score = ', 0.90909090909090906)
C_parameter                 1
Mean recall score    0.912823
Name: 2, dtype: object

('Mean recall score ', 0.91282301995095116)

-------------------------------------------
('C parameter: ', 10)
-------------------------------------------

('Iteration ', 1, ': recall score = ', 0.86301369863013699)
('Iteration ', 2, ': recall score = ', 0.87671232876712324)
('Iteration ', 3, ': recall score = ', 0.98305084745762716)
('Iteration ', 4, ': recall score = ', 0.94594594594594594)
('Iteration ', 5, ': recall score = ', 0.90909090909090906)
C_parameter                10
Mean recall score    0.915563
Name: 3, dtype: object

('Mean recall score ', 0.91556274597834852)

-------------------------------------------
('C parameter: ', 100)
-------------------------------------------

('Iteration ', 1, ': recall score = ', 0.86301369863013699)
('Iteration ', 2, ': recall score = ', 0.87671232876712324)
('Iteration ', 3, ': recall score = ', 0.98305084745762716)
('Iteration ', 4, ': recall score = ', 0.94594594594594594)
('Iteration ', 5, ': recall score = ', 0.90909090909090906)
C_parameter               100
Mean recall score    0.915563
Name: 4, dtype: object

('Mean recall score ', 0.91556274597834852)

*********************************************************************************
('Best model to choose from cross validation is with C parameter = ', 0.01)
*********************************************************************************

（2）通过cross_validate进行模型选择

def select_model_by_cross_validate(x_train_data,y_train_data):

    fold = KFold(len(y_train_data),5,shuffle = False) 
     # Different C parameters
    c_param_range = [0.01,0.1,1,10]

    result_table = pd.DataFrame(index=range(len(c_param_range),2),columns=['C_parameter','Recall_score'])
    i=0
    for c_param in c_param_range:
        print('-------------------------------------------')
        print('C parameter: ', c_param)
        print('-------------------------------------------')
        print('')
        lr = LogisticRegression(C = c_param,penalty='l1')

        # 核心方法
        scores = cross_validate(lr,x_train_data,y_train_data,scoring='recall',cv=fold,return_train_score=False)
        mean_score = np.array(sorted(scores['test_score'])).mean()
        print mean_score
        result_table.loc[i,'C_parameter'] = c_param
        result_table.loc[i,'Recall_score'] = mean_score
        i+=1

    best_sc = result_table.iloc[result_table['Recall_score'].idxmax()]['C_parameter']
    #print result_table.head()
    print ("the best C is",best_sc)
    return best_sc

select_model_by_cross_validate(X_train_undersample,y_train_undersample)

-------------------------------------------
('C parameter: ', 0.01)
-------------------------------------------

0.955366699202
-------------------------------------------
('C parameter: ', 0.1)
-------------------------------------------

0.903303632907
-------------------------------------------
('C parameter: ', 1)
-------------------------------------------

0.912823019951
-------------------------------------------
('C parameter: ', 10)
-------------------------------------------

0.915562745978
('the best C is', 0.01)
0.01

（3）使用GridSearchCV()

from sklearn.model_selection import GridSearchCV

def select_model_by_gridSearchCV(x_train_data,y_train_data):
    fold = KFold(len(y_train_data),5,shuffle = False)
    c_param_range = {'C':[0.01,0.1,1,10]}

    lr = LogisticRegression(penalty='l1')
    grid = GridSearchCV(lr, c_param_range, cv=fold, scoring="recall")

    grid.fit(x_train_data, y_train_data)

    print grid.best_score_    #查看最佳分数(此处为f1_score)
    print grid.best_params_
    print grid.best_estimator_
    return grid.best_params_

select_model_by_gridSearchCV(X_train_undersample,y_train_undersample)

0.955342302358
{'C': 0.01}
LogisticRegression(C=0.01, class_weight=None, dual=False, fit_intercept=True,intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,penalty='l1', random_state=None, solver='liblinear', tol=0.0001,verbose=0, warm_start=False)
{'C': 0.01}

四、绘制混淆矩阵

我们观察混淆矩阵的时候关注于准确率和精度，也就是混淆矩阵右上角的值。

#draw the confusion_matrix
def plot_confusion_matrix(cm, classes,
                          title='Confusion matrix',
                          cmap=plt.cm.Blues):
    """
    This function prints and plots the confusion matrix.
    """
    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')

绘制混淆矩阵

import itertools
lr = LogisticRegression(C = best_c, penalty = 'l1')
lr.fit(X_train_undersample,y_train_undersample.values.ravel())
y_pred_undersample = lr.predict(X_test_undersample.values)

# Compute confusion matrix
cnf_matrix = confusion_matrix(y_test_undersample,y_pred_undersample)
np.set_printoptions(precision=2)

print("Recall metric in the testing dataset: ", cnf_matrix[1,1]/(cnf_matrix[1,0]+cnf_matrix[1,1]))

# Plot non-normalized confusion matrix
class_names = [0,1]
plt.figure()
plot_confusion_matrix(cnf_matrix
                      , classes=class_names
                      , title='Confusion matrix')
plt.show()

Recall metric in the testing dataset:  0.931972789116

这里写图片描述

best_c = printing_Kfold_scores(X_train,y_train)

-------------------------------------------
C parameter:  0.01
-------------------------------------------

Iteration  1 : recall score =  0.492537313433
Iteration  2 : recall score =  0.602739726027
Iteration  3 : recall score =  0.683333333333
Iteration  4 : recall score =  0.569230769231
Iteration  5 : recall score =  0.45

Mean recall score  0.559568228405

-------------------------------------------
C parameter:  0.1
-------------------------------------------

Iteration  1 : recall score =  0.567164179104
Iteration  2 : recall score =  0.616438356164
Iteration  3 : recall score =  0.683333333333
Iteration  4 : recall score =  0.584615384615
Iteration  5 : recall score =  0.525

Mean recall score  0.595310250644

-------------------------------------------
C parameter:  1
-------------------------------------------

Iteration  1 : recall score =  0.55223880597
Iteration  2 : recall score =  0.616438356164
Iteration  3 : recall score =  0.716666666667
Iteration  4 : recall score =  0.615384615385
Iteration  5 : recall score =  0.5625

Mean recall score  0.612645688837

-------------------------------------------
C parameter:  10
-------------------------------------------

Iteration  1 : recall score =  0.55223880597
Iteration  2 : recall score =  0.616438356164
Iteration  3 : recall score =  0.733333333333
Iteration  4 : recall score =  0.615384615385
Iteration  5 : recall score =  0.575

Mean recall score  0.61847902217

-------------------------------------------
C parameter:  100
-------------------------------------------

Iteration  1 : recall score =  0.55223880597
Iteration  2 : recall score =  0.616438356164
Iteration  3 : recall score =  0.733333333333
Iteration  4 : recall score =  0.615384615385
Iteration  5 : recall score =  0.575

Mean recall score  0.61847902217

*********************************************************************************
Best model to choose from cross validation is with C parameter =  10.0
*********************************************************************************

五、返回概率值并设置阈值，通过设置阈值来进行分类划分。

lr = LogisticRegression(C = 0.01, penalty = 'l1')
lr.fit(X_train_undersample,y_train_undersample.values.ravel())
y_pred_undersample_proba = lr.predict_proba(X_test_undersample.values)

thresholds = [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9]

plt.figure(figsize=(10,10))

j = 1
for i in thresholds:
    y_test_predictions_high_recall = y_pred_undersample_proba[:,1] > i

    plt.subplot(3,3,j)
    j += 1

    # Compute confusion matrix
    cnf_matrix = confusion_matrix(y_test_undersample,y_test_predictions_high_recall)
    np.set_printoptions(precision=2)

    print("Recall metric in the testing dataset: ", cnf_matrix[1,1]/(cnf_matrix[1,0]+cnf_matrix[1,1]))

    # Plot non-normalized confusion matrix
    class_names = [0,1]
    plot_confusion_matrix(cnf_matrix
                          , classes=class_names
                          , title='Threshold >= %s'%i)

Recall metric in the testing dataset:  1.0
Recall metric in the testing dataset:  1.0
Recall metric in the testing dataset:  1.0
Recall metric in the testing dataset:  0.986394557823
Recall metric in the testing dataset:  0.931972789116
Recall metric in the testing dataset:  0.884353741497
Recall metric in the testing dataset:  0.836734693878
Recall metric in the testing dataset:  0.748299319728
Recall metric in the testing dataset:  0.571428571429

这里写图片描述

上采样

MOTE算法讲解博文：

import pandas as pd
from imblearn.over_sampling import SMOTE
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split

credit_cards=pd.read_csv('creditcard.csv')

columns=credit_cards.columns
# The labels are in the last column ('Class'). Simply remove it to obtain features columns
features_columns=columns.delete(len(columns)-1)

features=credit_cards[features_columns]
labels=credit_cards['Class']

features_train, features_test, labels_train, labels_test = train_test_split(features, 
                                                                            labels, 
                                                                            test_size=0.2, 
                                                                            random_state=0)

oversampler=SMOTE(random_state=0)
os_features,os_labels=oversampler.fit_sample(features_train,labels_train)

len(os_labels[os_labels==1])

os_features = pd.DataFrame(os_features)
os_labels = pd.DataFrame(os_labels)
best_c = printing_Kfold_scores(os_features,os_labels)

-------------------------------------------
C parameter:  0.01
-------------------------------------------

Iteration  1 : recall score =  0.890322580645
Iteration  2 : recall score =  0.894736842105
Iteration  3 : recall score =  0.968861347792
Iteration  4 : recall score =  0.957595541926
Iteration  5 : recall score =  0.958430881173

Mean recall score  0.933989438728

-------------------------------------------
C parameter:  0.1
-------------------------------------------

Iteration  1 : recall score =  0.890322580645
Iteration  2 : recall score =  0.894736842105
Iteration  3 : recall score =  0.970410534469
Iteration  4 : recall score =  0.959980655302
Iteration  5 : recall score =  0.960178498807

Mean recall score  0.935125822266

-------------------------------------------
C parameter:  1
-------------------------------------------

Iteration  1 : recall score =  0.890322580645
Iteration  2 : recall score =  0.894736842105
Iteration  3 : recall score =  0.970454796946
Iteration  4 : recall score =  0.96014552489
Iteration  5 : recall score =  0.960596168431

Mean recall score  0.935251182603

-------------------------------------------
C parameter:  10
-------------------------------------------

Iteration  1 : recall score =  0.890322580645
Iteration  2 : recall score =  0.894736842105
Iteration  3 : recall score =  0.97065397809
Iteration  4 : recall score =  0.960343368396
Iteration  5 : recall score =  0.960530220596

Mean recall score  0.935317397966

-------------------------------------------
C parameter:  100
-------------------------------------------

Iteration  1 : recall score =  0.890322580645
Iteration  2 : recall score =  0.894736842105
Iteration  3 : recall score =  0.970543321899
Iteration  4 : recall score =  0.960211472725
Iteration  5 : recall score =  0.960903924995

Mean recall score  0.935343628474

*********************************************************************************
Best model to choose from cross validation is with C parameter =  100.0
*********************************************************************************

lr = LogisticRegression(C = best_c, penalty = 'l1')
lr.fit(os_features,os_labels.values.ravel())
y_pred = lr.predict(features_test.values)

# Compute confusion matrix
cnf_matrix = confusion_matrix(labels_test,y_pred)
np.set_printoptions(precision=2)

print("Recall metric in the testing dataset: ", cnf_matrix[1,1]/(cnf_matrix[1,0]+cnf_matrix[1,1]))

# Plot non-normalized confusion matrix
class_names = [0,1]
plt.figure()
plot_confusion_matrix(cnf_matrix
                      , classes=class_names
                      , title='Confusion matrix')
plt.show()

Recall metric in the testing dataset:  0.90099009901

这里写图片描述

信用卡欺诈案例（终结）

该案例主要包含着：

一、 按照类别统计数目，观察样本是否平衡

二、规约化震荡数据，生成新的特征

下采样过程（始终关注于下标）

三、数据集切分

三、（1）传统的模型选择方法

（2）通过cross_validate进行模型选择

（3）使用GridSearchCV()

四、绘制混淆矩阵

绘制混淆矩阵

五、返回概率值并设置阈值，通过设置阈值来进行分类划分。

上采样

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一、按照类别统计数目，观察样本是否平衡