발표

Author

김보람

Published

December 8, 2023

imports

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt 
import networkx as nx
import sklearn
import xgboost as xgb

# sklearn
from sklearn import model_selection # split함수이용
from sklearn import ensemble # RF,GBM
from sklearn import metrics
from sklearn.metrics import precision_score, recall_score, f1_score
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.naive_bayes import GaussianNB

# gnn
import torch
import torch.nn.functional as F
import torch_geometric
from torch_geometric.nn import GCNConv

def down_sample_textbook(df):
    df_majority = df[df.is_fraud==0].copy()
    df_minority = df[df.is_fraud==1].copy()
    df_maj_dowsampled = sklearn.utils.resample(df_majority, n_samples=len(df_minority), replace=False, random_state=42)
    df_downsampled = pd.concat([df_minority, df_maj_dowsampled])
    return df_downsampled

def compute_time_difference(group):
    n = len(group)
    result = []
    for i in range(n):
        for j in range(n):
            time_difference = abs(group.iloc[i].trans_date_trans_time.value - group.iloc[j].trans_date_trans_time.value)
            result.append([group.iloc[i].name, group.iloc[j].name, time_difference])
    return result

def mask(df):
    df_tr,df_test = sklearn.model_selection.train_test_split(df, random_state=42)
    N = len(df)
    train_mask = [i in df_tr.index for i in range(N)]
    test_mask = [i in df_test.index for i in range(N)]
    train_mask = np.array(train_mask)
    test_mask = np.array(test_mask)
    return train_mask, test_mask

def edge_index_selected(edge_index):
    theta = edge_index[:,2].mean()
    edge_index[:,2] = (np.exp(-edge_index[:,2]/theta) != 1)*(np.exp(-edge_index[:,2]/theta))
    edge_index = edge_index.tolist()
    mean_ = np.array(edge_index)[:,2].mean()
    selected_edges = [(int(row[0]), int(row[1])) for row in edge_index if row[2] > mean_]
    edge_index_selected = torch.tensor(selected_edges, dtype=torch.long).t()
    return edge_index_selected


fraudTrain = pd.read_csv("~/Desktop/fraudTrain.csv").iloc[:,1:]
fraudTrain = fraudTrain.assign(trans_date_trans_time= list(map(lambda x: pd.to_datetime(x), fraudTrain.trans_date_trans_time)))
fraudTrain
trans_date_trans_time cc_num merchant category amt first last gender street city ... lat long city_pop job dob trans_num unix_time merch_lat merch_long is_fraud
0 2019-01-01 00:00:00 2.703190e+15 fraud_Rippin, Kub and Mann misc_net 4.97 Jennifer Banks F 561 Perry Cove Moravian Falls ... 36.0788 -81.1781 3495 Psychologist, counselling 1988-03-09 0b242abb623afc578575680df30655b9 1325376018 36.011293 -82.048315 0
1 2019-01-01 00:00:00 6.304230e+11 fraud_Heller, Gutmann and Zieme grocery_pos 107.23 Stephanie Gill F 43039 Riley Greens Suite 393 Orient ... 48.8878 -118.2105 149 Special educational needs teacher 1978-06-21 1f76529f8574734946361c461b024d99 1325376044 49.159047 -118.186462 0
2 2019-01-01 00:00:00 3.885950e+13 fraud_Lind-Buckridge entertainment 220.11 Edward Sanchez M 594 White Dale Suite 530 Malad City ... 42.1808 -112.2620 4154 Nature conservation officer 1962-01-19 a1a22d70485983eac12b5b88dad1cf95 1325376051 43.150704 -112.154481 0
3 2019-01-01 00:01:00 3.534090e+15 fraud_Kutch, Hermiston and Farrell gas_transport 45.00 Jeremy White M 9443 Cynthia Court Apt. 038 Boulder ... 46.2306 -112.1138 1939 Patent attorney 1967-01-12 6b849c168bdad6f867558c3793159a81 1325376076 47.034331 -112.561071 0
4 2019-01-01 00:03:00 3.755340e+14 fraud_Keeling-Crist misc_pos 41.96 Tyler Garcia M 408 Bradley Rest Doe Hill ... 38.4207 -79.4629 99 Dance movement psychotherapist 1986-03-28 a41d7549acf90789359a9aa5346dcb46 1325376186 38.674999 -78.632459 0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1048570 2020-03-10 16:07:00 6.011980e+15 fraud_Fadel Inc health_fitness 77.00 Haley Wagner F 05561 Farrell Crescent Annapolis ... 39.0305 -76.5515 92106 Accountant, chartered certified 1943-05-28 45ecd198c65e81e597db22e8d2ef7361 1362931649 38.779464 -76.317042 0
1048571 2020-03-10 16:07:00 4.839040e+15 fraud_Cremin, Hamill and Reichel misc_pos 116.94 Meredith Campbell F 043 Hanson Turnpike Hedrick ... 41.1826 -92.3097 1583 Geochemist 1999-06-28 c00ce51c6ebb7657474a77b9e0b51f34 1362931670 41.400318 -92.726724 0
1048572 2020-03-10 16:08:00 5.718440e+11 fraud_O'Connell, Botsford and Hand home 21.27 Susan Mills F 005 Cody Estates Louisville ... 38.2507 -85.7476 736284 Engineering geologist 1952-04-02 17c9dc8b2a6449ca2473726346e58e6c 1362931711 37.293339 -84.798122 0
1048573 2020-03-10 16:08:00 4.646850e+18 fraud_Thompson-Gleason health_fitness 9.52 Julia Bell F 576 House Crossroad West Sayville ... 40.7320 -73.1000 4056 Film/video editor 1990-06-25 5ca650881b48a6a38754f841c23b77ab 1362931718 39.773077 -72.213209 0
1048574 2020-03-10 16:08:00 2.283740e+15 fraud_Buckridge PLC misc_pos 6.81 Shannon Williams F 9345 Spencer Junctions Suite 183 Alpharetta ... 34.0770 -84.3033 165556 Prison officer 1997-12-27 8d0a575fe635bbde12f1a2bffc126731 1362931730 33.601468 -83.891921 0

1048575 rows × 22 columns

데이터정리

_df1 = fraudTrain[fraudTrain["is_fraud"] == 0].sample(frac=0.20, random_state=42)
_df2 = fraudTrain[fraudTrain["is_fraud"] == 1]
df02 = pd.concat([_df1,_df2])
df02.shape
(214520, 22)
df50 = down_sample_textbook(df02)
df50 = df50.reset_index()
df50.shape
(12012, 23)

tr/test

mask(df50)
(array([False,  True,  True, ...,  True, False,  True]),
 array([ True, False, False, ..., False,  True, False]))
train_mask, test_mask = mask(df50)

edge_index 설정

edge_index = np.load('edge_index_list_plus50.npy').astype(np.float64)
edge_index.shape
(200706, 3)
edge_index_selected = edge_index_selected(edge_index)

data설정(x, edge_index, y)

x = torch.tensor(df50['amt'], dtype=torch.float).reshape(-1,1)
y = torch.tensor(df50['is_fraud'],dtype=torch.int64)
data = torch_geometric.data.Data(x=x, edge_index = edge_index_selected, y=y, train_mask = train_mask, test_mask = test_mask)
data
Data(x=[12012, 1], edge_index=[2, 93730], y=[12012], train_mask=[12012], test_mask=[12012])

정리

구분 Train Test 모형 설명변수 비고
분석1 df50_tr df50_test Proposed amt
분석2 df50_tr df50_test 로지스틱 회귀 amt
분석3 df50_tr df50_test XGBoost amt
분석4 df50_tr df50_test LightGBM amt 
lst = [_results1, _results2,_results3,_results4]
pd.concat(lst)
accuracy_score precision_score recall_score f1_score
분석1 0.902098 0.862478 0.959130 0.908240
분석2 0.849484 0.933279 0.756098 0.835397
분석3 0.880120 0.886957 0.874094 0.880478
분석4 0.885115 0.893817 0.876730 0.885191

분석 1(GCN)

torch.manual_seed(202250926)

class GCN1(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = GCNConv(1, 32)
        self.conv2 = GCNConv(32,2)

    def forward(self, data):
        x, edge_index = data.x, data.edge_index

        x = self.conv1(x, edge_index)
        x = F.relu(x)
        x = F.dropout(x, training=self.training)
        x = self.conv2(x, edge_index)

        return F.log_softmax(x, dim=1)

X = (data.x[data.train_mask]).numpy()
XX = (data.x[data.test_mask]).numpy()
y = (data.y[data.train_mask]).numpy()
yy = (data.y[data.test_mask]).numpy()

model = GCN1()
optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=5e-4)
model.train()
for epoch in range(400):
    optimizer.zero_grad()
    out = model(data)
    loss = F.nll_loss(out[data.train_mask], data.y[data.train_mask])
    loss.backward()
    optimizer.step()
model.eval()

pred = model(data).argmax(dim=1)
yyhat = pred[data.test_mask]

pred

metrics = [sklearn.metrics.accuracy_score,
           sklearn.metrics.precision_score,
           sklearn.metrics.recall_score,
           sklearn.metrics.f1_score]

_results1= pd.DataFrame({m.__name__:[m(yy,yyhat).round(6)] for m in metrics},index=['분석1'])
_results1
accuracy_score precision_score recall_score f1_score
분석1 0.902098 0.862478 0.95913 0.90824 
b1,W1 = list(model.conv1.parameters())
b1,W1
(Parameter containing:
 tensor([ 3.3281e-01, -5.2539e-01,  1.1277e+00,  2.8239e-01, -1.2373e-01,
          9.6607e-01, -1.7652e-02,  8.3960e-01,  3.3252e-01, -4.6080e-01,
          8.6407e-01,  1.3294e+00,  3.2962e-01, -1.3215e-06,  2.1968e+00,
         -1.8843e-01,  7.6852e-10, -5.0779e-01, -7.7613e-09, -7.8956e-04,
         -4.6320e-01, -6.8497e-02,  3.4533e-02, -4.4006e-01, -3.8074e-01,
         -4.5996e-01,  4.4989e-13, -3.9142e-08,  4.3476e-01,  1.5457e+00,
         -3.1396e-01,  4.3208e-01], requires_grad=True),
 Parameter containing:
 tensor([[ 1.6974e-01],
         [ 1.4139e-01],
         [-6.2153e-03],
         [ 1.3422e-01],
         [-8.4340e-02],
         [-5.0439e-03],
         [-5.0210e-02],
         [-4.3200e-03],
         [ 2.5801e-01],
         [ 2.1217e-01],
         [-4.4886e-03],
         [-7.1453e-03],
         [ 1.8556e-01],
         [-3.8936e-02],
         [-1.2772e-02],
         [-3.0888e-03],
         [-1.9216e-05],
         [ 4.9480e-02],
         [-6.8162e-02],
         [-8.9236e-02],
         [ 1.3753e-01],
         [-6.8890e-02],
         [-6.3743e-02],
         [ 2.1896e-01],
         [ 2.9288e-03],
         [ 4.8694e-03],
         [-4.5865e-02],
         [-5.2090e-02],
         [ 1.2880e-01],
         [-8.3668e-03],
         [ 9.4231e-03],
         [ 1.3015e-01]], requires_grad=True))
b2,W2 = list(model.conv2.parameters())
b2,W2
(Parameter containing:
 tensor([ 0.8103, -0.8103], requires_grad=True),
 Parameter containing:
 tensor([[-3.1862e-02,  1.2264e-01,  2.5667e-01, -2.9070e-01,  1.9933e-02,
           2.8287e-01, -8.1168e-03,  1.7061e-01,  3.1362e-02, -1.3049e-01,
           2.4063e-02,  1.5218e-01,  1.7274e-01,  5.8768e-06,  2.9730e-01,
           3.7174e-02, -1.1418e-09, -2.0023e-01, -1.3184e-02,  5.8934e-02,
           2.0550e-01, -5.7726e-02,  2.8572e-01, -1.4259e-01, -2.8006e-01,
          -6.7342e-02, -3.5160e-02, -7.0944e-02, -5.3993e-02,  1.6846e-01,
           5.3713e-02, -1.7743e-01],
         [-2.9673e-02,  1.2650e-01, -1.8649e-01, -2.8863e-01,  2.7340e-01,
          -2.5555e-02, -2.4640e-02, -9.0935e-02,  3.2923e-02, -1.2820e-01,
          -2.4358e-01, -2.7795e-01,  1.7514e-01, -4.4449e-06, -3.2877e-01,
           5.9096e-02,  1.0013e-09, -1.9043e-01,  1.0161e-02, -4.9584e-02,
           2.0870e-01, -6.1236e-03,  2.2979e-01, -1.4060e-01,  1.1504e-01,
           2.0197e-01,  4.5961e-02, -1.0499e-01, -5.0285e-02, -3.6542e-01,
           1.1680e-01, -1.7159e-01]], requires_grad=True))

분석2(로지스틱 회귀)

torch.manual_seed(202250926)
X = np.array(df50_tr.loc[:,['amt']])
XX = np.array(df50_test.loc[:,['amt']])
y = np.array(df50_tr.is_fraud)
yy = np.array(df50_test.is_fraud)

lrnr = sklearn.linear_model.LogisticRegression()

lrnr.fit(X,y)

#thresh = y.mean()
#yyhat = (lrnr.predict_proba(XX)> thresh)[:,-1]
yyhat = lrnr.predict(XX) 

yyhat

metrics = [sklearn.metrics.accuracy_score,
           sklearn.metrics.precision_score,
           sklearn.metrics.recall_score,
           sklearn.metrics.f1_score]

_results2= pd.DataFrame({m.__name__:[m(yy,yyhat).round(6)] for m in metrics},index=['분석2'])
_results2
accuracy_score precision_score recall_score f1_score
분석2 0.849484 0.933279 0.756098 0.835397

분석3(XGBoost)

import xgboost as xgb


torch.manual_seed(202250926)
X = np.array(df50_tr.loc[:, ['amt']])
XX = np.array(df50_test.loc[:, ['amt']])
y = np.array(df50_tr.is_fraud)
yy = np.array(df50_test.is_fraud)

lrnr = xgb.XGBClassifier()
 
lrnr.fit(X,y)
yyhat = lrnr.predict(XX)


metrics = [sklearn.metrics.accuracy_score,
           sklearn.metrics.precision_score,
           sklearn.metrics.recall_score,
           sklearn.metrics.f1_score]

_results3= pd.DataFrame({m.__name__:[m(yy,yyhat).round(6)] for m in metrics},index=['분석3'])
_results3
accuracy_score precision_score recall_score f1_score
분석3 0.88012 0.886957 0.874094 0.880478

분석4(Light GBM)

import lightgbm as lgb

torch.manual_seed(202250926)
X = np.array(df50_tr.loc[:, ['amt']])
XX = np.array(df50_test.loc[:, ['amt']])
y = np.array(df50_tr.is_fraud)
yy = np.array(df50_test.is_fraud)


lrnr = lgb.LGBMClassifier()

lrnr.fit(X, y)
yyhat = lrnr.predict(XX)

metrics = [sklearn.metrics.accuracy_score,
           sklearn.metrics.precision_score,
           sklearn.metrics.recall_score,
           sklearn.metrics.f1_score]

_results4 = pd.DataFrame({m.__name__: [m(yy, yyhat).round(6)] for m in metrics}, index=['분석4'])
_results4
accuracy_score precision_score recall_score f1_score
분석4 0.885115 0.893817 0.87673 0.885191

분석5(One class SVM)

from sklearn.svm import OneClassSVM

torch.manual_seed(202250926)

X = np.array(df50_tr.loc[:, ['amt']])
XX = np.array(df50_test.loc[:, ['amt']])
y = np.array(df50_tr.is_fraud)
yy = np.array(df50_test.is_fraud)

ocsvm = OneClassSVM(gamma='auto')  
ocsvm.fit(X[y == 0])  


yyhat_ocsvm = ocsvm.predict(XX)

yyhat_ocsvm_binary = np.where(yyhat_ocsvm ==  1, 0)


metrics_ocsvm = [
    sklearn.metrics.accuracy_score,
    sklearn.metrics.precision_score,
    sklearn.metrics.recall_score,
    sklearn.metrics.f1_score
]

_results5 = pd.DataFrame({m.__name__: [m(yy, yyhat_ocsvm_binary).round(6)] for m in metrics_ocsvm}, index=['분석5'])
_results5