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, roc_curve, auc
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

file_url = 'https://media.githubusercontent.com/media/musthave-ML10/data_source/main/fraud.csv'
fraudTrain = pd.read_csv(file_url)

# 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 00:00:18	2703186189652095	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 00:00:44	630423337322	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 00:00:51	38859492057661	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 00:01:16	3534093764340240	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 00:03:06	375534208663984	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
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1852389	2020-12-31 23:59:07	30560609640617	fraud_Reilly and Sons	health_fitness	43.77	Michael	Olson	M	558 Michael Estates	Luray	...	40.4931	-91.8912	519	Town planner	1966-02-13	9b1f753c79894c9f4b71f04581835ada	1388534347	39.946837	-91.333331	0
1852390	2020-12-31 23:59:09	3556613125071656	fraud_Hoppe-Parisian	kids_pets	111.84	Jose	Vasquez	M	572 Davis Mountains	Lake Jackson	...	29.0393	-95.4401	28739	Futures trader	1999-12-27	2090647dac2c89a1d86c514c427f5b91	1388534349	29.661049	-96.186633	0
1852391	2020-12-31 23:59:15	6011724471098086	fraud_Rau-Robel	kids_pets	86.88	Ann	Lawson	F	144 Evans Islands Apt. 683	Burbank	...	46.1966	-118.9017	3684	Musician	1981-11-29	6c5b7c8add471975aa0fec023b2e8408	1388534355	46.658340	-119.715054	0
1852392	2020-12-31 23:59:24	4079773899158	fraud_Breitenberg LLC	travel	7.99	Eric	Preston	M	7020 Doyle Stream Apt. 951	Mesa	...	44.6255	-116.4493	129	Cartographer	1965-12-15	14392d723bb7737606b2700ac791b7aa	1388534364	44.470525	-117.080888	0
1852393	2020-12-31 23:59:34	4170689372027579	fraud_Dare-Marvin	entertainment	38.13	Samuel	Frey	M	830 Myers Plaza Apt. 384	Edmond	...	35.6665	-97.4798	116001	Media buyer	1993-05-10	1765bb45b3aa3224b4cdcb6e7a96cee3	1388534374	36.210097	-97.036372	0

1852394 rows × 22 columns

street/tsate/zip: 고객 거주지 정보

lat/long: rhror wnthdp eogks dnleh alc rudeh

city_pop: 고객의 zipcode에 속하는 인구 수

job: 직업

dob: 생년월일

trans_num: 거래번호

unix_time: 거래 시간(유닉스 타임 스탬프 형식)

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

(378200, 22)

(df02['is_fraud'] == 1).sum() / 378200

0.025518244315177154

df50 = down_sample_textbook(df02)
df50 = df50.reset_index()
df50.shape

(19302, 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)

df50_tr,df50_test = sklearn.model_selection.train_test_split(df50, random_state=42)

edge_index 설정

# groups = df50.groupby('cc_num')
# edge_index_list_plus = [compute_time_difference(group) for _, group in groups]
# edge_index_list_plus_flat = [item for sublist in edge_index_list_plus for item in sublist]
# edge_index_list_plus_nparr = np.array(edge_index_list_plus_flat)
# np.save('edge_index_list_plus50.npy', edge_index_list_plus_nparr)

edge_index = np.load('edge_index_list_plus50_2.npy').astype(np.float64)
edge_index.shape

(429162, 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=[19302, 1], edge_index=[2, 195056], y=[19302], train_mask=[19302], test_mask=[19302])

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) # argmax말고
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	0.860133	0.810655	0.937943	0.869666

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	0.857853	0.951078	0.75302	0.840539

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	0.888313	0.896508	0.876718	0.886502

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	0.887484	0.894312	0.877551	0.885852