版權宣告：本套技術專欄是作者（秦凱新）平時工作的總結和昇華，通過從真實商業環境抽取案例進行總結和分享，並給出商業應用的調優建議和叢集環境容量規劃等內容，請持續關注本套部落格。QQ郵箱地址：1120746959@qq.com，如有任何學術交流，可隨時聯絡。
1 信用卡欺詐行為案例集預處理
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
data = pd.read_csv("creditcard.csv")
data.head()
複製程式碼from sklearn.preprocessing import StandardScaler
data['normAmount'] = StandardScaler().fit_transform(data['Amount'].values.reshape(-1, 1))
data = data.drop(['Time','Amount'],axis=1)
data.head()
複製程式碼2 K折交叉驗證(驗證通過)
def printing_Kfold_scores(x_train_data, y_train_data):
fold = KFold(5,shuffle=False)
# Different C parameters
# 0.01 倒數其實是100
# 0.1其實是10
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'])
results_table['C_parameter'] = c_param_range
# 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 = []
for iteration, indices in enumerate(fold.split(x_train_data)):
# Call the logistic regression model with a certain C parameter
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.ix[j,'Mean recall score'] = np.mean(recall_accs)
j += 1
print('')
print('Mean recall score ', np.mean(recall_accs))
print('')
best_c = results_table.iloc[results_table['Mean recall score'].astype('float64').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 = printing_Kfold_scores(X_train_undersample,y_train_undersample)
-------------------------------------------
C parameter: 0.01
-------------------------------------------
Iteration 0 : recall score = 0.8082191780821918
Iteration 1 : recall score = 0.8356164383561644
Iteration 2 : recall score = 0.8983050847457628
Iteration 3 : recall score = 0.8918918918918919
Iteration 4 : recall score = 0.8939393939393939
Mean recall score 0.8655943974030809
-------------------------------------------
C parameter: 0.1
-------------------------------------------
Iteration 0 : recall score = 0.863013698630137
Iteration 1 : recall score = 0.8767123287671232
Iteration 2 : recall score = 0.9830508474576272
Iteration 3 : recall score = 0.918918918918919
Iteration 4 : recall score = 0.9090909090909091
Mean recall score 0.9101573405729431
-------------------------------------------
C parameter: 1
-------------------------------------------
Iteration 0 : recall score = 0.8767123287671232
Iteration 1 : recall score = 0.9041095890410958
Iteration 2 : recall score = 0.9830508474576272
Iteration 3 : recall score = 0.9459459459459459
Iteration 4 : recall score = 0.9242424242424242
Mean recall score 0.9268122270908433
-------------------------------------------
C parameter: 10
-------------------------------------------
Iteration 0 : recall score = 0.8904109589041096
Iteration 1 : recall score = 0.9041095890410958
C:\ProgramData\Anaconda3\lib\site-packages\ipykernel_launcher.py:39: DeprecationWarning:
.ix is deprecated. Please use
.loc for label based indexing or
.iloc for positional indexing
See the documentation here:
http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated
Iteration 2 : recall score = 0.9830508474576272
Iteration 3 : recall score = 0.9324324324324325
Iteration 4 : recall score = 0.9393939393939394
Mean recall score 0.9298795534458411
-------------------------------------------
C parameter: 100
-------------------------------------------
Iteration 0 : recall score = 0.9041095890410958
Iteration 1 : recall score = 0.9041095890410958
Iteration 2 : recall score = 0.9830508474576272
Iteration 3 : recall score = 0.9459459459459459
Iteration 4 : recall score = 0.9545454545454546
Mean recall score 0.9383522852062439
*********************************************************************************
Best model to choose from cross validation is with C parameter = 100.0
********************************************************************************
複製程式碼版權宣告：本套技術專欄是作者（秦凱新）平時工作的總結和昇華，通過從真實商業環境抽取案例進行總結和分享，並給出商業應用的調優建議和叢集環境容量規劃等內容，請持續關注本套部落格。QQ郵箱地址：1120746959@qq.com，如有任何學術交流，可隨時聯絡。
3 不均衡問題處理策略（OverSample與UnderSample）
# 找出非class列
X = data.ix[:, data.columns != 'Class']
# 找出class列
y = data.ix[:, data.columns == 'Class']
# 找出欺詐的個數和索引492
number_records_fraud = len(data[data.Class == 1])
fraud_indices = np.array(data[data.Class == 1].index)
# Picking the indices of the normal classes（找出正常的索引）
normal_indices = data[data.Class == 0].index
# Out of the indices we picked, randomly select "x" number (number_records_fraud)（從正常的行為中選擇接近欺詐的樣本索引）492
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(索引組合) 892
under_sample_indices = np.concatenate([fraud_indices,random_normal_indices])
# iloc通過行號獲取行資料
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))
Percentage of normal transactions: 0.5
Percentage of fraud transactions: 0.5
Total number of transactions in resampled data: 984
複製程式碼4 訓練集與測試集劃分
from sklearn.cross_validation import train_test_split
X特徵輸入,y表示label,test_size劃分的測試集比例，沒有設定random_state，每次取得的
結果就不一樣，它的隨機數種子與當前系統時間有關。其實就是該組隨機數的編號，在需要重
複試驗的時候，保證得到一組一樣的隨機數。比如你每次都填1，其他引數一樣的情況下你得到
隨機陣列是一樣的。但填0或不填，每次都不一樣。隨機數的產生取決於種子，隨機數和種子之
間的關係遵從以下兩個規則：種子不同，產生不同的隨機數；種子相同，即使例項不同也產生
相同的隨機數。
全部樣本拆分
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))
Number transactions train dataset: 199364
Number transactions test dataset: 85443
Total number of transactions: 284807
# 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: 688
Number transactions test dataset: 296
Total number of transactions: 984
複製程式碼5基於低取樣資料集X_test_undersample模型訓練與測試（均衡資料）
#Recall = TP/(TP+FN)
from sklearn.linear_model import LogisticRegression
from sklearn.cross_validation import KFold, cross_val_score
from sklearn.metrics import confusion_matrix,recall_score,classification_report
函式呼叫
best_c = printing_Kfold_scores(X_train_undersample,y_train_undersample)
-------------------------------------------
C parameter: 0.01
-------------------------------------------
Iteration 1 : recall score = 0.958904109589
Iteration 2 : recall score = 0.917808219178
Iteration 3 : recall score = 1.0
Iteration 4 : recall score = 0.972972972973
Iteration 5 : recall score = 0.954545454545
Mean recall score 0.960846151257
-------------------------------------------
C parameter: 0.1
-------------------------------------------
Iteration 1 : recall score = 0.835616438356
Iteration 2 : recall score = 0.86301369863
Iteration 3 : recall score = 0.915254237288
Iteration 4 : recall score = 0.932432432432
Iteration 5 : recall score = 0.878787878788
Mean recall score 0.885020937099
-------------------------------------------
C parameter: 1
-------------------------------------------
Iteration 1 : recall score = 0.835616438356
Iteration 2 : recall score = 0.86301369863
Iteration 3 : recall score = 0.966101694915
Iteration 4 : recall score = 0.945945945946
Iteration 5 : recall score = 0.893939393939
Mean recall score 0.900923434357
-------------------------------------------
C parameter: 10
-------------------------------------------
Iteration 1 : recall score = 0.849315068493
Iteration 2 : recall score = 0.86301369863
Iteration 3 : recall score = 0.966101694915
Iteration 4 : recall score = 0.959459459459
Iteration 5 : recall score = 0.893939393939
Mean recall score 0.906365863087
-------------------------------------------
C parameter: 100
-------------------------------------------
Iteration 1 : recall score = 0.86301369863
Iteration 2 : recall score = 0.86301369863
Iteration 3 : recall score = 0.966101694915
Iteration 4 : recall score = 0.959459459459
Iteration 5 : recall score = 0.893939393939
Mean recall score 0.909105589115
*********************************************************************************
Best model to choose from cross validation is with C parameter = 0.01
*********************************************************************************
複製程式碼5 混合矩陣
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')
複製程式碼6 混合矩陣作用於低取樣資料集X_test_undersample的展示
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()
複製程式碼7 混合矩陣作用於全資料集X_test.values的展示
版權宣告：本套技術專欄是作者（秦凱新）平時工作的總結和昇華，通過從真實商業環境抽取案例進行總結和分享，並給出商業應用的調優建議和叢集環境容量規劃等內容，請持續關注本套部落格。QQ郵箱地址：1120746959@qq.com，如有任何學術交流，可隨時聯絡。
lr = LogisticRegression(C = best_c, penalty = 'l1')
lr.fit(X_train_undersample,y_train_undersample.values.ravel())
y_pred = lr.predict(X_test.values)
# Compute confusion matrix
cnf_matrix = confusion_matrix(y_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()
複製程式碼8 基於全資料集進行k折交叉驗證（不均衡資料）
8.1 全資料集進行k折交叉驗證
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
*********************************************************************************
複製程式碼8.2 全資料集混合矩陣
# 不均衡樣本偏向於多的樣本，誤傷率低
lr = LogisticRegression(C = best_c, penalty = 'l1')
lr.fit(X_train,y_train.values.ravel())
y_pred_undersample = lr.predict(X_test.values)
# Compute confusion matrix
cnf_matrix = confusion_matrix(y_test,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()
複製程式碼9 邏輯迴歸基於閾值進行判斷（概率）
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
複製程式碼10 基於SMOTE 進行資料預處理
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])
227454
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( ,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()
複製程式碼11 總結
OverSample與UnderSample對比發現，基於SMOTE，資料的準確率和召回率得到了很大程度的提高。
版權宣告：本套技術專欄是作者（秦凱新）平時工作的總結和昇華，通過從真實商業環境抽取案例進行總結和分享，並給出商業應用的調優建議和叢集環境容量規劃等內容，請持續關注本套部落格。QQ郵箱地址：1120746959@qq.com，如有任何學術交流，可隨時聯絡。
秦凱新 於深圳 201812081811