本文共 23944 字，大约阅读时间需要 79 分钟。

1、 脱敏后的数据文件

2、程序解读

2.1 读取文件

#!/usr/bin/env python3# -*- coding: utf-8 -*-import pandas as pdimport numpy as np import matplotlib.pyplot as pltfrom sklearn.utils import shuffleimport seaborn as snsimport matplotlib.gridspec as gridspec#读取csv文件data = pd.read_csv('../dataset/creditcard.csv')# .iloc：根据标签的所在位置，从0开始计数，选取列x_train = np.array(data.iloc[:,0:29])y_train = np.array(data.iloc[:,30])

2.2 查看前5行记录

# df.head(n)：查看DataFrame对象的前n行print(data.head())

Time        V1        V2        V3        V4        V5        V6        V7  \0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941            V8        V9       V10       V11       V12       V13       V14  \0  0.098698  0.363787  0.090794 -0.551600 -0.617801 -0.991390 -0.311169   1  0.085102 -0.255425 -0.166974  1.612727  1.065235  0.489095 -0.143772   2  0.247676 -1.514654  0.207643  0.624501  0.066084  0.717293 -0.165946   3  0.377436 -1.387024 -0.054952 -0.226487  0.178228  0.507757 -0.287924   4 -0.270533  0.817739  0.753074 -0.822843  0.538196  1.345852 -1.119670           V15       V16       V17       V18       V19       V20       V21  \0  1.468177 -0.470401  0.207971  0.025791  0.403993  0.251412 -0.018307   1  0.635558  0.463917 -0.114805 -0.183361 -0.145783 -0.069083 -0.225775   2  2.345865 -2.890083  1.109969 -0.121359 -2.261857  0.524980  0.247998   3 -0.631418 -1.059647 -0.684093  1.965775 -1.232622 -0.208038 -0.108300   4  0.175121 -0.451449 -0.237033 -0.038195  0.803487  0.408542 -0.009431           V22       V23       V24       V25       V26       V27       V28  \0  0.277838 -0.110474  0.066928  0.128539 -0.189115  0.133558 -0.021053   1 -0.638672  0.101288 -0.339846  0.167170  0.125895 -0.008983  0.014724   2  0.771679  0.909412 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752   3  0.005274 -0.190321 -1.175575  0.647376 -0.221929  0.062723  0.061458   4  0.798278 -0.137458  0.141267 -0.206010  0.502292  0.219422  0.215153      Amount  Class  0  149.62      0  1    2.69      0  2  378.66      0  3  123.50      0  4   69.99      0

2.3 统计描述

# 生成描述性统计，总结数据集分布的中心趋势，分散和形状，不包括NaN值。print(data.describe())

Time            V1            V2            V3            V4  \count  284807.000000  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   mean    94813.859575  1.759072e-12 -8.251146e-13 -9.655448e-13  8.321385e-13   std     47488.145955  1.958696e+00  1.651309e+00  1.516255e+00  1.415869e+00   min         0.000000 -5.640751e+01 -7.271573e+01 -4.832559e+01 -5.683171e+00   25%     54201.500000 -9.203734e-01 -5.985499e-01 -8.903648e-01 -8.486401e-01   50%     84692.000000  1.810880e-02  6.548556e-02  1.798463e-01 -1.984653e-02   75%    139320.500000  1.315642e+00  8.037239e-01  1.027196e+00  7.433413e-01   max    172792.000000  2.454930e+00  2.205773e+01  9.382558e+00  1.687534e+01                    V5            V6            V7            V8            V9  \count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   mean   1.649983e-13  4.248434e-13 -3.054696e-13  8.777981e-14 -1.179757e-12   std    1.380247e+00  1.332271e+00  1.237094e+00  1.194353e+00  1.098632e+00   min   -1.137433e+02 -2.616051e+01 -4.355724e+01 -7.321672e+01 -1.343407e+01   25%   -6.915971e-01 -7.682956e-01 -5.540759e-01 -2.086297e-01 -6.430976e-01   50%   -5.433583e-02 -2.741871e-01  4.010308e-02  2.235804e-02 -5.142873e-02   75%    6.119264e-01  3.985649e-01  5.704361e-01  3.273459e-01  5.971390e-01   max    3.480167e+01  7.330163e+01  1.205895e+02  2.000721e+01  1.559499e+01                   V10           V11           V12           V13           V14  \count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   mean   7.092627e-13  1.874974e-12  1.053347e-12  7.127607e-13 -1.474787e-13   std    1.088850e+00  1.020713e+00  9.992014e-01  9.952742e-01  9.585956e-01   min   -2.458826e+01 -4.797473e+00 -1.868371e+01 -5.791881e+00 -1.921433e+01   25%   -5.354257e-01 -7.624942e-01 -4.055715e-01 -6.485393e-01 -4.255740e-01   50%   -9.291738e-02 -3.275735e-02  1.400326e-01 -1.356806e-02  5.060132e-02   75%    4.539234e-01  7.395934e-01  6.182380e-01  6.625050e-01  4.931498e-01   max    2.374514e+01  1.201891e+01  7.848392e+00  7.126883e+00  1.052677e+01                   V15           V16           V17           V18           V19  \count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   mean  -5.231430e-13 -2.282231e-13 -6.425412e-13  4.950748e-13  7.057401e-13   std    9.153160e-01  8.762529e-01  8.493371e-01  8.381762e-01  8.140405e-01   min   -4.498945e+00 -1.412985e+01 -2.516280e+01 -9.498746e+00 -7.213527e+00   25%   -5.828843e-01 -4.680368e-01 -4.837483e-01 -4.988498e-01 -4.562989e-01   50%    4.807155e-02  6.641332e-02 -6.567575e-02 -3.636312e-03  3.734823e-03   75%    6.488208e-01  5.232963e-01  3.996750e-01  5.008067e-01  4.589494e-01   max    8.877742e+00  1.731511e+01  9.253526e+00  5.041069e+00  5.591971e+00                   V20           V21           V22           V23           V24  \count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   mean   1.766109e-12 -3.405785e-13 -5.723165e-13 -9.725860e-13  1.464148e-12   std    7.709250e-01  7.345240e-01  7.257016e-01  6.244603e-01  6.056471e-01   min   -5.449772e+01 -3.483038e+01 -1.093314e+01 -4.480774e+01 -2.836627e+00   25%   -2.117214e-01 -2.283949e-01 -5.423504e-01 -1.618463e-01 -3.545861e-01   50%   -6.248109e-02 -2.945017e-02  6.781943e-03 -1.119293e-02  4.097606e-02   75%    1.330408e-01  1.863772e-01  5.285536e-01  1.476421e-01  4.395266e-01   max    3.942090e+01  2.720284e+01  1.050309e+01  2.252841e+01  4.584549e+00                   V25           V26           V27           V28         Amount  \count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  284807.000000   mean  -6.987110e-13 -5.617884e-13  3.332082e-12 -3.518875e-12      88.349619   std    5.212781e-01  4.822270e-01  4.036325e-01  3.300833e-01     250.120109   min   -1.029540e+01 -2.604551e+00 -2.256568e+01 -1.543008e+01       0.000000   25%   -3.171451e-01 -3.269839e-01 -7.083953e-02 -5.295979e-02       5.600000   50%    1.659350e-02 -5.213911e-02  1.342146e-03  1.124383e-02      22.000000   75%    3.507156e-01  2.409522e-01  9.104512e-02  7.827995e-02      77.165000   max    7.519589e+00  3.517346e+00  3.161220e+01  3.384781e+01   25691.160000                  Class  count  284807.000000  mean        0.001727  std         0.041527  min         0.000000  25%         0.000000  50%         0.000000  75%         0.000000  max         1.000000

2.4 空值统计

print(data.isnull().sum())

Time      0V1        0V2        0V3        0V4        0V5        0V6        0V7        0V8        0V9        0V10       0V11       0V12       0V13       0V14       0V15       0V16       0V17       0V18       0V19       0V20       0V21       0V22       0V23       0V24       0V25       0V26       0V27       0V28       0Amount    0Class     0dtype: int64

2.5 维度统计描述

# Time, 时间维度print("Fraud")# data.Class == 1表示选择Class列值等于1的记录print(data.Time[data.Class == 1].describe())print()print("Normal")print(data.Time[data.Class == 0].describe())print()

Fraudcount       492.000000mean      80746.806911std       47835.365138min         406.00000025%       41241.50000050%       75568.50000075%      128483.000000max      170348.000000Name: Time, dtype: float64Normalcount    284315.000000mean      94838.202258std       47484.015786min           0.00000025%       54230.00000050%       84711.00000075%      139333.000000max      172792.000000Name: Time, dtype: float64

2.6

f, (ax1, ax2) = plt.subplots(2, 1, sharex=True, figsize=(12,4))bins = 50ax1.hist(data.Time[data.Class == 1], bins = bins)ax1.set_title('Fraud')ax2.hist(data.Time[data.Class == 0], bins = bins)ax2.set_title('Normal')plt.xlabel('Time (in Seconds)')plt.ylabel('Number of Transactions')plt.show()

图

# Amount 金额print("Fraud")print(data.Amount[data.Class == 1].describe())print()print("Normal")print(data.Amount[data.Class == 0].describe())

Fraudcount     492.000000mean      122.211321std       256.683288min         0.00000025%         1.00000050%         9.25000075%       105.890000max      2125.870000Name: Amount, dtype: float64Normalcount    284315.000000mean         88.291022std         250.105092min           0.00000025%           5.65000050%          22.00000075%          77.050000max       25691.160000Name: Amount, dtype: float64

f, (ax1, ax2) = plt.subplots(2, 1, sharex=True, figsize=(12,4))bins = 30ax1.hist(data.Amount[data.Class == 1], bins = bins)ax1.set_title('Fraud')ax2.hist(data.Amount[data.Class == 0], bins = bins)ax2.set_title('Normal')plt.xlabel('Amount ($)')plt.ylabel('Number of Transactions')plt.yscale('log')plt.show()

data['Amount_max_fraud'] = 1data.loc[data.Amount <= 2125.87, 'Amount_max_fraud'] = 0f, (ax1, ax2) = plt.subplots(2, 1, sharex=True, figsize=(12,6))ax1.scatter(data.Time[data.Class == 1], data.Amount[data.Class == 1])ax1.set_title('Fraud')ax2.scatter(data.Time[data.Class == 0], data.Amount[data.Class == 0])ax2.set_title('Normal')plt.xlabel('Time (in Seconds)')plt.ylabel('Amount')plt.show()

# analysis the anonymized features.#Select only the anonymized features.v_features = data.iloc[:,1:29].columnsplt.figure(figsize=(12,28*4))gs = gridspec.GridSpec(28, 1)for i, cn in enumerate(data[v_features]):    ax = plt.subplot(gs[i])    sns.distplot(data[cn][data.Class == 1], bins=50)    # 看两者的形状差异    sns.distplot(data[cn][data.Class == 0], bins=50)    ax.set_xlabel('')    ax.set_title('histogram of feature: ' + str(cn))plt.show()

#Drop all of the features that have very similar distributions between the two types of transactions.data = data.drop(['V28','V27','V26','V25','V24','V23','V22','V20','V15','V13','V8'], axis =1)#Based on the plots above, these features are created to identify values where fraudulent transaction are more common.data['V1_'] = data.V1.map(lambda x: 1 if x < -3 else 0)data['V2_'] = data.V2.map(lambda x: 1 if x > 2.5 else 0)data['V3_'] = data.V3.map(lambda x: 1 if x < -4 else 0)data['V4_'] = data.V4.map(lambda x: 1 if x > 2.5 else 0)data['V5_'] = data.V5.map(lambda x: 1 if x < -4.5 else 0)data['V6_'] = data.V6.map(lambda x: 1 if x < -2.5 else 0)data['V7_'] = data.V7.map(lambda x: 1 if x < -3 else 0)data['V9_'] = data.V9.map(lambda x: 1 if x < -2 else 0)data['V10_'] = data.V10.map(lambda x: 1 if x < -2.5 else 0)data['V11_'] = data.V11.map(lambda x: 1 if x > 2 else 0)data['V12_'] = data.V12.map(lambda x: 1 if x < -2 else 0)data['V14_'] = data.V14.map(lambda x: 1 if x < -2.5 else 0)data['V16_'] = data.V16.map(lambda x: 1 if x < -2 else 0)data['V17_'] = data.V17.map(lambda x: 1 if x < -2 else 0)data['V18_'] = data.V18.map(lambda x: 1 if x < -2 else 0)data['V19_'] = data.V19.map(lambda x: 1 if x > 1.5 else 0)data['V21_'] = data.V21.map(lambda x: 1 if x > 0.6 else 0)print('每个单一属性的欺诈记录与整车记录的差异统计：')print(data.describe())print(data.sum())

每个单一属性的欺诈记录与整车记录的差异统计：                Time            V1            V2            V3            V4  \count  284807.000000  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   mean    94813.859575  1.759072e-12 -8.251146e-13 -9.655448e-13  8.321385e-13   std     47488.145955  1.958696e+00  1.651309e+00  1.516255e+00  1.415869e+00   min         0.000000 -5.640751e+01 -7.271573e+01 -4.832559e+01 -5.683171e+00   25%     54201.500000 -9.203734e-01 -5.985499e-01 -8.903648e-01 -8.486401e-01   50%     84692.000000  1.810880e-02  6.548556e-02  1.798463e-01 -1.984653e-02   75%    139320.500000  1.315642e+00  8.037239e-01  1.027196e+00  7.433413e-01   max    172792.000000  2.454930e+00  2.205773e+01  9.382558e+00  1.687534e+01                    V5            V6            V7            V9           V10  \count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   mean   1.649983e-13  4.248434e-13 -3.054696e-13 -1.179757e-12  7.092627e-13   std    1.380247e+00  1.332271e+00  1.237094e+00  1.098632e+00  1.088850e+00   min   -1.137433e+02 -2.616051e+01 -4.355724e+01 -1.343407e+01 -2.458826e+01   25%   -6.915971e-01 -7.682956e-01 -5.540759e-01 -6.430976e-01 -5.354257e-01   50%   -5.433583e-02 -2.741871e-01  4.010308e-02 -5.142873e-02 -9.291738e-02   75%    6.119264e-01  3.985649e-01  5.704361e-01  5.971390e-01  4.539234e-01   max    3.480167e+01  7.330163e+01  1.205895e+02  1.559499e+01  2.374514e+01                   V11           V12           V14           V16           V17  \count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   mean   1.874974e-12  1.053347e-12 -1.474787e-13 -2.282231e-13 -6.425412e-13   std    1.020713e+00  9.992014e-01  9.585956e-01  8.762529e-01  8.493371e-01   min   -4.797473e+00 -1.868371e+01 -1.921433e+01 -1.412985e+01 -2.516280e+01   25%   -7.624942e-01 -4.055715e-01 -4.255740e-01 -4.680368e-01 -4.837483e-01   50%   -3.275735e-02  1.400326e-01  5.060132e-02  6.641332e-02 -6.567575e-02   75%    7.395934e-01  6.182380e-01  4.931498e-01  5.232963e-01  3.996750e-01   max    1.201891e+01  7.848392e+00  1.052677e+01  1.731511e+01  9.253526e+00                   V18           V19           V21         Amount          Class  \count  2.848070e+05  2.848070e+05  2.848070e+05  284807.000000  284807.000000   mean   4.950748e-13  7.057401e-13 -3.405785e-13      88.349619       0.001727   std    8.381762e-01  8.140405e-01  7.345240e-01     250.120109       0.041527   min   -9.498746e+00 -7.213527e+00 -3.483038e+01       0.000000       0.000000   25%   -4.988498e-01 -4.562989e-01 -2.283949e-01       5.600000       0.000000   50%   -3.636312e-03  3.734823e-03 -2.945017e-02      22.000000       0.000000   75%    5.008067e-01  4.589494e-01  1.863772e-01      77.165000       0.000000   max    5.041069e+00  5.591971e+00  2.720284e+01   25691.160000       1.000000          Amount_max_fraud            V1_            V2_            V3_  \count     284807.000000  284807.000000  284807.000000  284807.000000   mean           0.002117       0.047042       0.024771       0.009838   std            0.045965       0.211730       0.155427       0.098699   min            0.000000       0.000000       0.000000       0.000000   25%            0.000000       0.000000       0.000000       0.000000   50%            0.000000       0.000000       0.000000       0.000000   75%            0.000000       0.000000       0.000000       0.000000   max            1.000000       1.000000       1.000000       1.000000                    V4_            V5_            V6_            V7_  \count  284807.000000  284807.000000  284807.000000  284807.000000   mean        0.052794       0.004579       0.006274       0.010059   std         0.223622       0.067510       0.078963       0.099791   min         0.000000       0.000000       0.000000       0.000000   25%         0.000000       0.000000       0.000000       0.000000   50%         0.000000       0.000000       0.000000       0.000000   75%         0.000000       0.000000       0.000000       0.000000   max         1.000000       1.000000       1.000000       1.000000                    V9_           V10_           V11_           V12_  \count  284807.000000  284807.000000  284807.000000  284807.000000   mean        0.031530       0.005049       0.018244       0.048408   std         0.174746       0.070877       0.133833       0.214628   min         0.000000       0.000000       0.000000       0.000000   25%         0.000000       0.000000       0.000000       0.000000   50%         0.000000       0.000000       0.000000       0.000000   75%         0.000000       0.000000       0.000000       0.000000   max         1.000000       1.000000       1.000000       1.000000                   V14_           V16_           V17_           V18_  \count  284807.000000  284807.000000  284807.000000  284807.000000   mean        0.013697       0.021165       0.002173       0.013943   std         0.116230       0.143935       0.046569       0.117254   min         0.000000       0.000000       0.000000       0.000000   25%         0.000000       0.000000       0.000000       0.000000   50%         0.000000       0.000000       0.000000       0.000000   75%         0.000000       0.000000       0.000000       0.000000   max         1.000000       1.000000       1.000000       1.000000                   V19_           V21_  count  284807.000000  284807.000000  mean        0.032952       0.041958  std         0.178512       0.200494  min         0.000000       0.000000  25%         0.000000       0.000000  50%         0.000000       0.000000  75%         0.000000       0.000000  max         1.000000       1.000000  Time                2.700365e+10V1                  5.009022e-07V2                 -2.350312e-07V3                 -2.744665e-07V4                  2.368500e-07V5                  4.533991e-08V6                  1.209676e-07V7                 -8.687127e-08V9                 -3.359903e-07V10                 2.020664e-07V11                 5.340173e-07V12                 3.000407e-07V14                -4.247506e-08V16                -6.495627e-08V17                -1.830887e-07V18                 1.412354e-07V19                 2.010940e-07V21                -9.702072e-08Amount              2.516259e+07Class               4.920000e+02Amount_max_fraud    6.030000e+02V1_                 1.339800e+04V2_                 7.055000e+03V3_                 2.802000e+03V4_                 1.503600e+04V5_                 1.304000e+03V6_                 1.787000e+03V7_                 2.865000e+03V9_                 8.980000e+03V10_                1.438000e+03V11_                5.196000e+03V12_                1.378700e+04V14_                3.901000e+03V16_                6.028000e+03V17_                6.190000e+02V18_                3.971000e+03V19_                9.385000e+03V21_                1.195000e+04dtype: float64

#Create a new feature for normal (non-fraudulent) transactions.data.loc[data.Class == 0, 'Normal'] = 1data.loc[data.Class == 1, 'Normal'] = 0#Rename 'Class' to 'Fraud'.data = data.rename(columns={'Class': 'Fraud'})#492 fraudulent transactions, 284,315 normal transactions.#0.172% of transactions were fraud. print('欺诈记录的占比：')print(data.Normal.value_counts())print()print(data.Fraud.value_counts())pd.set_option("display.max_columns",101)print(data.head())

欺诈记录的占比：1.0    2843150.0       492Name: Normal, dtype: int640    2843151       492Name: Fraud, dtype: int64   Time        V1        V2        V3        V4        V5        V6        V7  \0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941            V9       V10       V11       V12       V14       V16       V17  \0  0.363787  0.090794 -0.551600 -0.617801 -0.311169 -0.470401  0.207971   1 -0.255425 -0.166974  1.612727  1.065235 -0.143772  0.463917 -0.114805   2 -1.514654  0.207643  0.624501  0.066084 -0.165946 -2.890083  1.109969   3 -1.387024 -0.054952 -0.226487  0.178228 -0.287924 -1.059647 -0.684093   4  0.817739  0.753074 -0.822843  0.538196 -1.119670 -0.451449 -0.237033           V18       V19       V21  Amount  Fraud  Amount_max_fraud  V1_  V2_  \0  0.025791  0.403993 -0.018307  149.62      0                 0    0    0   1 -0.183361 -0.145783 -0.225775    2.69      0                 0    0    0   2 -0.121359 -2.261857  0.247998  378.66      0                 0    0    0   3  1.965775 -1.232622 -0.108300  123.50      0                 0    0    0   4 -0.038195  0.803487 -0.009431   69.99      0                 0    0    0      V3_  V4_  V5_  V6_  V7_  V9_  V10_  V11_  V12_  V14_  V16_  V17_  V18_  \0    0    0    0    0    0    0     0     0     0     0     0     0     0   1    0    0    0    0    0    0     0     0     0     0     0     0     0   2    0    0    0    0    0    0     0     0     0     0     1     0     0   3    0    0    0    0    0    0     0     0     0     0     0     0     0   4    0    0    0    0    0    0     0     0     0     0     0     0     0      V19_  V21_  Normal  0     0     0     1.0  1     0     0     1.0  2     0     0     1.0  3     0     0     1.0  4     0     0     1.0

#Create dataframes of only Fraud and Normal transactions.Fraud = data[data.Fraud == 1]Normal = data[data.Normal == 1]# Set X_train equal to 80% of the fraudulent transactions.X_train = Fraud.sample(frac=0.8)count_Frauds = len(X_train)# Add 80% of the normal transactions to X_train.X_train = pd.concat([X_train, Normal.sample(frac = 0.8)], axis = 0)# X_test contains all the transaction not in X_train.X_test = data.loc[~data.index.isin(X_train.index)]#Shuffle the dataframes so that the training is done in a random order.X_train = shuffle(X_train)X_test = shuffle(X_test)#Add our target features to y_train and y_test.y_train = X_train.Fraudy_train = pd.concat([y_train, X_train.Normal], axis=1)y_test = X_test.Fraudy_test = pd.concat([y_test, X_test.Normal], axis=1)#Drop target features from X_train and X_test.X_train = X_train.drop(['Fraud','Normal'], axis = 1)X_test = X_test.drop(['Fraud','Normal'], axis = 1)#Check to ensure all of the training/testing dataframes are of the correct lengthprint()print('切割[学习、校验]处理后的记录数量：')print(len(X_train))print(len(y_train))print(len(X_test))print(len(y_test))

切割[学习、校验]处理后的记录数量：2278462278465696156961

'''Due to the imbalance in the data, ratio will act as an equal weighting system for our model. By dividing the number of transactions by those that are fraudulent, ratio will equal the value that when multipliedby the number of fraudulent transactions will equal the number of normal transaction. Simply put: # of fraud * ratio = # of normal'''ratio = len(X_train)/count_Frauds print()print('数据的占比:', ratio)y_train.Fraud *= ratioy_test.Fraud  *= ratioprint('训练数据的数量:\n', y_train.Fraud)print('测试数据的数量:\n', y_train.Fraud)

数据的占比: 578.2893401015228训练数据的数量: 22023     0.0185560    0.0112703    0.0165996    0.0245243    0.0238885    0.039966     0.0112043    0.0171013    0.0255567    0.0283619    0.0203942    0.082908     0.0245906    0.0225464    0.013679     0.0107609    0.0140858    0.0156028    0.0158914    0.072341     0.0208184    0.0111027    0.0217998    0.0229747    0.0281186    0.0259994    0.0112170    0.0204651    0.0184758    0.0118430    0.015155     0.028982     0.0193685    0.0209645    0.0201038    0.0226108    0.0219122    0.0266437    0.045419     0.099879     0.0167812    0.0117954    0.020935     0.0238062    0.013355     0.071356     0.054123     0.095958     0.0280240    0.0271372    0.0259493    0.0149400    0.0231110    0.030784     0.0186483    0.074528     0.0187912    0.017719     0.042839     0.0Name: Fraud, Length: 227846, dtype: float64测试数据的数量: 22023     0.0185560    0.0112703    0.0165996    0.0245243    0.0238885    0.039966     0.0112043    0.0171013    0.0255567    0.0283619    0.0203942    0.082908     0.0245906    0.0225464    0.013679     0.0107609    0.0140858    0.0156028    0.0158914    0.072341     0.0208184    0.0111027    0.0217998    0.0229747    0.0281186    0.0259994    0.0112170    0.0204651    0.0184758    0.0118430    0.015155     0.028982     0.0193685    0.0209645    0.0201038    0.0226108    0.0219122    0.0266437    0.045419     0.099879     0.0167812    0.0117954    0.020935     0.0238062    0.013355     0.071356     0.054123     0.095958     0.0280240    0.0271372    0.0259493    0.0149400    0.0231110    0.030784     0.0186483    0.074528     0.0187912    0.017719     0.042839     0.0

转载地址：http://pgvab.baihongyu.com/