机器学习入门数据集--6.信用卡诈骗预测
2019-02-12 本文已影响4人
ac619467fef3
通过用户使用信用卡行为数据，建立信用卡盗刷风控模型。当用户有了新的行为，通过这个模型就可以判断是正常用户的行为，还是有人盗刷这张卡。
由于数据集是PCA降维后的数据，这样就隐藏了原始信息的敏感信息，但保留了原数据中的信息量。
深度神经网络可解释性差，数据维度是用PCA处理之后的，所以很容易出现过拟合。可以把权重值加到Loss函数中，惩罚权重，降低过拟合。
查看数据
kaggle官网 下载数据需要登录。
欧洲的信用卡持卡人在2013年9月2天时间里的284807笔交易数据，其中有492笔交易是欺诈交易，占比0.172%。数据采用PCA变换映射为V1，V2，...，V28 数值型属性，只有交易时间和金额这两个变量没有经过PCA变换。输出变量为二值变量，1为正常，0为欺诈交易。
RangeIndex: 284807 entries, 0 to 284806
Data columns (total 31 columns):
Time 284807 non-null float64
V1 284807 non-null float64
V2 284807 non-null float64
V3 284807 non-null float64
V4 284807 non-null float64
V5 284807 non-null float64
V6 284807 non-null float64
V7 284807 non-null float64
V8 284807 non-null float64
V9 284807 non-null float64
V10 284807 non-null float64
V11 284807 non-null float64
V12 284807 non-null float64
V13 284807 non-null float64
V14 284807 non-null float64
V15 284807 non-null float64
V16 284807 non-null float64
V17 284807 non-null float64
V18 284807 non-null float64
V19 284807 non-null float64
V20 284807 non-null float64
V21 284807 non-null float64
V22 284807 non-null float64
V23 284807 non-null float64
V24 284807 non-null float64
V25 284807 non-null float64
V26 284807 non-null float64
V27 284807 non-null float64
V28 284807 non-null float64
Amount 284807 non-null float64
Class 284807 non-null int64
dtypes: float64(30), int64(1)
memory usage: 67.4 MB
count mean std min 25% \
Time 284807.0 9.481386e+04 47488.145955 0.000000 54201.500000
V1 284807.0 3.919560e-15 1.958696 -56.407510 -0.920373
V2 284807.0 5.688174e-16 1.651309 -72.715728 -0.598550
V3 284807.0 -8.769071e-15 1.516255 -48.325589 -0.890365
V4 284807.0 2.782312e-15 1.415869 -5.683171 -0.848640
V5 284807.0 -1.552563e-15 1.380247 -113.743307 -0.691597
V6 284807.0 2.010663e-15 1.332271 -26.160506 -0.768296
V7 284807.0 -1.694249e-15 1.237094 -43.557242 -0.554076
V8 284807.0 -1.927028e-16 1.194353 -73.216718 -0.208630
V9 284807.0 -3.137024e-15 1.098632 -13.434066 -0.643098
V10 284807.0 1.768627e-15 1.088850 -24.588262 -0.535426
V11 284807.0 9.170318e-16 1.020713 -4.797473 -0.762494
V12 284807.0 -1.810658e-15 0.999201 -18.683715 -0.405571
V13 284807.0 1.693438e-15 0.995274 -5.791881 -0.648539
V14 284807.0 1.479045e-15 0.958596 -19.214325 -0.425574
V15 284807.0 3.482336e-15 0.915316 -4.498945 -0.582884
V16 284807.0 1.392007e-15 0.876253 -14.129855 -0.468037
V17 284807.0 -7.528491e-16 0.849337 -25.162799 -0.483748
V18 284807.0 4.328772e-16 0.838176 -9.498746 -0.498850
V19 284807.0 9.049732e-16 0.814041 -7.213527 -0.456299
V20 284807.0 5.085503e-16 0.770925 -54.497720 -0.211721
V21 284807.0 1.537294e-16 0.734524 -34.830382 -0.228395
V22 284807.0 7.959909e-16 0.725702 -10.933144 -0.542350
V23 284807.0 5.367590e-16 0.624460 -44.807735 -0.161846
V24 284807.0 4.458112e-15 0.605647 -2.836627 -0.354586
V25 284807.0 1.453003e-15 0.521278 -10.295397 -0.317145
V26 284807.0 1.699104e-15 0.482227 -2.604551 -0.326984
V27 284807.0 -3.660161e-16 0.403632 -22.565679 -0.070840
V28 284807.0 -1.206049e-16 0.330083 -15.430084 -0.052960
Amount 284807.0 8.834962e+01 250.120109 0.000000 5.600000
Class 284807.0 1.727486e-03 0.041527 0.000000 0.000000
50% 75% max
Time 84692.000000 139320.500000 172792.000000
V1 0.018109 1.315642 2.454930
V2 0.065486 0.803724 22.057729
V3 0.179846 1.027196 9.382558
V4 -0.019847 0.743341 16.875344
V5 -0.054336 0.611926 34.801666
V6 -0.274187 0.398565 73.301626
V7 0.040103 0.570436 120.589494
V8 0.022358 0.327346 20.007208
V9 -0.051429 0.597139 15.594995
V10 -0.092917 0.453923 23.745136
V11 -0.032757 0.739593 12.018913
V12 0.140033 0.618238 7.848392
V13 -0.013568 0.662505 7.126883
V14 0.050601 0.493150 10.526766
V15 0.048072 0.648821 8.877742
V16 0.066413 0.523296 17.315112
V17 -0.065676 0.399675 9.253526
V18 -0.003636 0.500807 5.041069
V19 0.003735 0.458949 5.591971
V20 -0.062481 0.133041 39.420904
V21 -0.029450 0.186377 27.202839
V22 0.006782 0.528554 10.503090
V23 -0.011193 0.147642 22.528412
V24 0.040976 0.439527 4.584549
V25 0.016594 0.350716 7.519589
V26 -0.052139 0.240952 3.517346
V27 0.001342 0.091045 31.612198
V28 0.011244 0.078280 33.847808
Amount 22.000000 77.165000 25691.160000
Class 0.000000 0.000000 1.000000
数据主要是由PCA产生，不需要过多预处理。
建模——深度学习模型
数据集有一个特点，正标签很少，因此在训练的时候应该均和正负标签。
import numpy as np
import pandas as pd
np.set_printoptions(suppress=True)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
data_df = pd.read_csv("/Users/wangsen/ai/13/homework/creditcard.csv")
# data_df.info()
# print(data_df.describe().T)
#print(data_df.Time.head(100))
data_df = data_df.drop("Time",axis=1)
neg_df = data_df[data_df.Class==0]
pos_df = data_df[data_df.Class==1]
#print(neg_df.head())
#print(pos_df.head())
neg_data = neg_df.drop('Class',axis=1).values
pos_data = pos_df.drop('Class',axis=1).values
print("neg_data shape:",neg_data.shape)
print("pos_data shape:",pos_data.shape)
import tensorflow as tf
X = tf.placeholder(dtype=tf.float32,shape=[None,29])
label = tf.placeholder(dtype=tf.float32,shape=[None,2])
net = tf.layers.dense(X,16,tf.nn.relu)
net = tf.layers.dense(net,256,tf.nn.relu)
net = tf.layers.dense(net,256,tf.nn.relu)
net = tf.layers.dense(net,256,tf.nn.relu)
net = tf.layers.dense(net,256,tf.nn.relu)
y = tf.layers.dense(net,2,None)
#y = tf.nn.softmax(y)
loss = tf.losses.softmax_cross_entropy(label,y)
#loss = tf.reduce_mean(tf.square(label-y))
#loss = tf.reduce_sum(-label*tf.log(y))
accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(y, 1), tf.argmax(label, 1)), tf.float32))
train_step = tf.train.AdamOptimizer(0.0001).minimize(loss)
# train_step = tf.train.GradientDescentOptimizer(0.01).minimize(loss)
neg_high = neg_data.shape[0]
pos_high = pos_data.shape[0]
input_y = np.zeros([64, 2])
input_y[:32, 0] = 1
input_y[32:, 1] = 1
test_x = np.concatenate([neg_data[10000:10000+450],pos_data[0:450]])
test_y = np.zeros([900,2])
test_y[:450,0] = 1
test_y[450:,1] = 1
sess = tf.Session()
sess.run(tf.global_variables_initializer())
l = []
s = []
for itr in range(10000):
neg_ind = np.random.randint(0,neg_high,32)
pos_ind = np.random.randint(0,pos_high,32)
input_x = np.concatenate([neg_data[neg_ind],pos_data[pos_ind]])
_,loss_var = sess.run((train_step,loss),feed_dict={X:input_x,label:input_y})
if itr%100==0:
accuracy_var = sess.run(accuracy,feed_dict={X:test_x,label:test_y})
print("iter: accurency: loss:%f",(itr,accuracy_var,loss_var))
s.append(accuracy_var)
l.append(loss_var)
import matplotlib.pyplot as plt
plt.plot(l,color="red")
plt.plot(s,color="green")
plt.show()
'''
neg_data shape: (284315, 29)
pos_data shape: (492, 29)
'''
iter: accurency: loss:%f (9900, 0.98888886, 0.06290674)
损失函数和准确率
防止过拟合，添加L2惩罚
loss_w = [tf.nn.l2_loss(var) for var in tf.trainable_variables() if "kernel" in var.name]
print("variables:",tf.trainable_variables())
weights_norm = tf.reduce_sum(loss_w)
loss = tf.losses.softmax_cross_entropy(label,y)+0.001*weights_norm
variables: [<tf.Variable 'dense/kernel:0' shape=(29, 16) dtype=float32_ref>, <tf.Variable 'dense/bias:0' shape=(16,) dtype=float32_ref>, <tf.Variable 'dense_1/kernel:0' shape=(16, 256) dtype=float32_ref>, <tf.Variable 'dense_1/bias:0' shape=(256,) dtype=float32_ref>, <tf.Variable 'dense_2/kernel:0' shape=(256, 256) dtype=float32_ref>, <tf.Variable 'dense_2/bias:0' shape=(256,) dtype=float32_ref>, <tf.Variable 'dense_3/kernel:0' shape=(256, 256) dtype=float32_ref>, <tf.Variable 'dense_3/bias:0' shape=(256,) dtype=float32_ref>, <tf.Variable 'dense_4/kernel:0' shape=(256, 256) dtype=float32_ref>, <tf.Variable 'dense_4/bias:0' shape=(256,) dtype=float32_ref>, <tf.Variable 'dense_5/kernel:0' shape=(256, 2) dtype=float32_ref>, <tf.Variable 'dense_5/bias:0' shape=(2,) dtype=float32_ref>]
iter:4900 accurency:0.972222 loss:0.291221 weight:197.521942
添加L2惩罚项
