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Copy pathOperational_Risk_XGBoost.py
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Operational_Risk_XGBoost.py
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import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import datetime as dt
import seaborn as sns
from sklearn.model_selection import train_test_split, GridSearchCV
from xgboost import XGBClassifier
from sklearn.metrics import (classification_report, confusion_matrix, f1_score)
def load_raw_data(file_path):
fraud_data = pd.read_csv(file_path)
del fraud_data['Unnamed: 0']
fraud_data['time'] = pd.to_datetime(fraud_data['trans_date_trans_time'])
del fraud_data['trans_date_trans_time']
fraud_data['days'] = fraud_data['time'].dt.day_name()
fraud_data['hour'] = fraud_data['time'].dt.hour
return fraud_data
def haversine_distance(lat1, lon1, lat2, lon2):
R = 6371 # Earth radius in km
# Converting from degrees to radians
lat1, lon1, lat2, lon2 = map(np.radians, [lat1, lon1, lat2, lon2])
dlat = lat2 - lat1
dlon = lon2 - lon1
a = np.sin(dlat/2)**2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon/2)**2
c = 2 * np.arctan2(np.sqrt(a), np.sqrt(1-a))
distance = R * c
return distance
def data_preparation(fraud_data):
fraud_data['dob'] = pd.to_datetime(fraud_data['dob'])
final_date = dt.datetime(2023, 1, 1)
fraud_data['age'] = ((final_date - fraud_data['dob']) / np.timedelta64(1, 'Y')).astype(int)
fraud_data['distance'] = haversine_distance(fraud_data['lat'], fraud_data['long'], fraud_data['merch_lat'], fraud_data['merch_long'])
numerical_fraud = fraud_data.select_dtypes(include=[np.number])
del numerical_fraud['cc_num']
del numerical_fraud['zip']
del numerical_fraud['lat']
del numerical_fraud['long']
del numerical_fraud['unix_time']
del numerical_fraud['merch_lat']
del numerical_fraud['merch_long']
del numerical_fraud['hour']
del numerical_fraud['is_fraud']
non_numerical_fraud = fraud_data.select_dtypes(include=['object']).copy()
non_numerical_fraud['hour'] = fraud_data['hour']
del non_numerical_fraud['merchant']
del non_numerical_fraud['category']
del non_numerical_fraud['first']
del non_numerical_fraud['last']
del non_numerical_fraud['street']
del non_numerical_fraud['city']
del non_numerical_fraud['job']
del non_numerical_fraud['trans_num']
dummies_fraud = pd.get_dummies(non_numerical_fraud, drop_first=True)
dummies_fraud = dummies_fraud.astype(int)
hour_dummies = pd.get_dummies(non_numerical_fraud['hour'], prefix='hour', drop_first=True).astype(int)
dummies_fraud = pd.concat([dummies_fraud, hour_dummies], axis=1)
del dummies_fraud['hour'] # Remove the original 'hour' column if needed
fraud_df = pd.concat([numerical_fraud, dummies_fraud], axis=1)
fraud_df['is_fraud'] = fraud_data['is_fraud']
return numerical_fraud, non_numerical_fraud, dummies_fraud, fraud_df
def data_under_sampling(fraud_df):
non_fraud_class = fraud_df[fraud_df['is_fraud'] == 0]
fraud_class = fraud_df[fraud_df['is_fraud'] == 1]
print('The number of observations in non_fraud_class is {}'.format(non_fraud_class['is_fraud'].value_counts()))
print('The number of observation in fraud_class is {}'.format(fraud_class['is_fraud'].value_counts()))
non_fraud_count, fraud_count = fraud_df['is_fraud'].value_counts()
non_fraud_under = non_fraud_class.sample(fraud_count)
under_sampled = pd.concat([non_fraud_under, fraud_class], axis=0)
return non_fraud_class, fraud_class, non_fraud_under, under_sampled
def data_split(fraud_data):
X = fraud_data.drop('is_fraud', axis=1)
y = fraud_data['is_fraud']
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
return X_train, X_test, y_train, y_test
def xgboost_model_train_and_evaluate(X_train, X_test, y_train, y_test):
param_boost = {'learning_rate': [0.01, 0.1],
'max_depth': [3, 5, 7],
'subsample': [0.5, 0.7],
'colsample_bytree': [0.5, 0.7],
'n_estimators': [10, 20, 30]}
boost_grid = GridSearchCV(XGBClassifier(),
param_grid=param_boost, n_jobs=-1)
boost_grid.fit(X_train, y_train)
prediction_boost = boost_grid.predict(X_test)
conf_mat_boost = confusion_matrix(y_true=y_test, y_pred=prediction_boost)
print('Confusion matrix:\n', conf_mat_boost)
print('--' * 25)
print('Classification report:\n', classification_report(y_test, prediction_boost))
return boost_grid
if __name__ == '__main__':
file_path = 'D:/PyCharm Community Edition 2023.1.2/Python_Project/Finance/ml4frm/fraudTrain.csv'
fraud_data_ = load_raw_data(file_path)
numerical_fraud_, non_numerical_fraud_, dummies_fraud_, fraud_df_ = data_preparation(fraud_data_)
non_fraud_class_, fraud_class_, non_fraud_under_, under_sampled_ = data_under_sampling(fraud_df_)
X_train_, X_test_, y_train_, y_test_ = data_split(under_sampled_)
model = xgboost_model_train_and_evaluate(X_train_, X_test_, y_train_, y_test_)