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

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

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

(214520, 22)

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

(12012, 23)

amt_info = df50.groupby('cc_num').agg(['mean','std'])['amt'].reset_index()

/tmp/ipykernel_2870693/562656812.py:1: FutureWarning: ['trans_date_trans_time', 'merchant', 'category', 'first', 'last', 'gender', 'street', 'city', 'state', 'job', 'dob', 'trans_num'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.
  amt_info = df50.groupby('cc_num').agg(['mean','std'])['amt'].reset_index()

df50 = df50.merge(amt_info, on ='cc_num', how='left')

df50['amt_z_score'] = (df50['amt'] - df50['mean']) / df50['std']

df50.drop(['mean','std'], axis=1, inplace=True)

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.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_z_score'], 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])

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

/home/coco/anaconda3/envs/py38/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))

	accuracy_score	precision_score	recall_score	f1_score
분석1	0.494838	0.0	0.0	0.0