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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
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data = pd.read_csv('creditcard.csv')
data.head()
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count_classes = pd.value_counts(data['Class'],sort=True).sort_index()
#print count_classes
count_classes.plot(kind='bar')
plt.title('Fraud class histogram')
plt.xlabel('Class')
plt.ylabel('Frequency')
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from sklearn.preprocessing import StandardScaler
data['normAmount'] = StandardScaler().fit_transform(data['Amount'].values.reshape(-1,1))
#data['normAmount'].head()
data = data.drop(['Time','Amount'],1)
data.head()
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In [55]:
X = data.ix[:,data.columns != 'Class']
y = data.ix[:,data.columns == 'Class']
#x,y
# number of data points in the minority class
number_records_fraud = len(y[y.Class == 1])
#number_records_fraud
fraud_indices = np.array(y[y.Class == 1].index)
#fraud_indices
#picking the indices of the normal classes
normal_indices = data[data.Class == 0].index
#normal_indices
#out of the indices we picked,randomly select x number (number_fraud_indices)
random_normal_indices = np.random.choice(normal_indices,number_records_fraud,replace = False)
#random_normal_indices
#len(random_normal_indices)
#random_normal_indices = np.array(random_normal_indices)
#random_normal_indices
#appending the 2 indices
under_sample_indices = np.concatenate([fraud_indices,random_normal_indices])
#under_sample_indices[0:5]
#under sample dataset
under_sample_data = data.ix[under_sample_indices,:]
#under_sample_data.tail()
X_undersample = under_sample_data.ix[:,under_sample_data.columns != 'Class']
y_undersample = under_sample_data.ix[:,under_sample_data.columns == 'Class']
#y_undersample
#y_0 = len(under_sample_data[under_sample_data.Class == 0])
#y_total = len(under_sample_data)
#float(y_0) / y_total
#showing ratio
print 'percentage of normal transactions: %.1f' %(len(under_sample_data[under_sample_data.Class == 0])/float(len(under_sample_data)))
print 'percentage of fraud transactions:',len(under_sample_data[under_sample_data.Class ==1])/float(len(under_sample_data))
print 'total number of transactions in resapmled data:' , len(y_undersample)
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from sklearn.cross_validation import train_test_split
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#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)
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#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
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def printing_kfold_scores(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'])
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 ''
recall_accs = []
for iteration,indices in enumerate(fold,start=1):
#call the logistic regression model with a certain C-param
lr = LogisticRegression(C=c_param,penalty='l1')
#ues 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.ix[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
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best_c = printing_kfold_scores(X_train_undersample,y_train_undersample)
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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 = 'red' if cm[i,j]>thresh else 'black')
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('predicted label')
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import itertools
In [82]:
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)
#computer confusion matrix
cnf_matrix = confusion_matrix(y_test_undersample,y_pred_undersample)
np.set_printoptions(precision=2)
print 'recall metric in the testing dataset:',float(cnf_matrix[1,1])/(cnf_matrix[1,0]+cnf_matrix[1,1])
#print cnf_matrix.shape[0]
#print cnf_matrix.shape[1]
print cnf_matrix.max()/2
class_names=[0,1]
plt.figure()
plot_confusion_matrix(cnf_matrix,classes=class_names)
plt.show()
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lr = LogisticRegression(C=best_c,penalty='l1')
lr.fit(X_train_undersample,y_train_undersample.values.ravel())
y_pred = lr.predict(X_test.values)
#computer confusion matrix
cnf_matrix = confusion_matrix(y_test,y_pred)
np.set_printoptions(precision=2)
print 'recall metric in the testing dataset:',float(cnf_matrix[1,1])/(cnf_matrix[1,0]+cnf_matrix[1,1])
#print cnf_matrix.shape[0]
#print cnf_matrix.shape[1]
#print cnf_matrix.max()/2
class_names=[0,1]
plt.figure()
plot_confusion_matrix(cnf_matrix,classes=class_names)
plt.show()
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best_c = printing_kfold_scores(X_train,y_train)
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lr = LogisticRegression(C=best_c,penalty='l1')
lr.fit(X_train,y_train.values.ravel())
y_pred = lr.predict(X_test.values)
#computer confusion matrix
cnf_matrix = confusion_matrix(y_test,y_pred)
np.set_printoptions(precision=2)
print 'recall metric in the testing dataset:',float(cnf_matrix[1,1])/(cnf_matrix[1,0]+cnf_matrix[1,1])
#print cnf_matrix.shape[0]
#print cnf_matrix.shape[1]
#print cnf_matrix.max()/2
class_names=[0,1]
plt.figure()
plot_confusion_matrix(cnf_matrix,classes=class_names)
plt.show()
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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
cnf_matrix = confusion_matrix(y_test_undersample,y_test_predictions_high_recall)
np.set_printoptions(precision=2)
print("Recall metric in the testing dataset: ", float(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)
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