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Data Balancing.py
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Data Balancing.py
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#importing libraries
import pandas as pd
import numpy as np
import seaborn as sn
import matplotlib.pyplot as plt
import imblearn as imbl
import scipy
import sklearn
import joblib
from imblearn.over_sampling import BorderlineSMOTE, SMOTE
from imblearn.under_sampling import NeighbourhoodCleaningRule, EditedNearestNeighbours
from imblearn.combine import SMOTEENN, SMOTETomek
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import TimeSeriesSplit, cross_val_score
from sklearn import metrics
from sklearn.metrics import fbeta_score
from sklearn.metrics import recall_score, roc_auc_score, brier_score_loss, confusion_matrix
from sklearn.feature_selection import RFE, RFECV
from sklearn.pipeline import Pipeline
from numpy import mean, std
from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import mutual_info_classif
from collections import Counter
#Importing data
filename = r"creditcard.csv"
df = pd.read_csv(filename)
for col in ['Class']:
df[col] = df[col].astype('category')
#splitting into features and class as per SFS
X = df.loc[:, ['Time','V1','V5','V6','V7','V8','V9','V10','V11','V12','V13','V14',
'V18','V19','V23','V26','Amount']]
y = df.loc[:, 'Class']
# Create Decision Tree classifer object
clf = DecisionTreeClassifier(random_state=1)
#KFold Cross (with time series split) Validation approach
tss = TimeSeriesSplit(n_splits = 2)
tss.split(X)
# Initialize the accuracy of the models to blank list. The accuracy of each model will be appended to this list
F_measure_model = []
roc_auc_model = []
brier_score_model = []
'''
#############################################################################
#DECISION TREE WITHOUT FEATURE SELECTION AND NO BALANCING
# Iterate over each train-test split
for train_index, test_index in tss.split(X):
# Split train-test
X_train, X_test = X.iloc[train_index, :], X.iloc[test_index,:]
y_train, y_test = y.iloc[train_index], y.iloc[test_index]
# Train the model
model = clf.fit(X_train, y_train)
# Append metrics to the list
F_measure_model.append(fbeta_score(y_test, model.predict(X_test),beta=2))
roc_auc_model.append(roc_auc_score(y_test, model.predict(X_test)))
brier_score_model.append(brier_score_loss(y_test, model.predict(X_test)))
# Print the model metrics
metrics = pd.DataFrame(
{'Result': ["Average"],
'F_measure': np.average(F_measure_model),
'ROC_AUC': np.average(roc_auc_model),
'Brier_Score' : np.average(brier_score_model)
})
print("Model Metrics:")
print(metrics)
'''
'''
###############################################################################
#BORDERLINE SMOTE (OVERSAMPLING) + DECISION TREE
for i in (0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9):
print('sampling_strategy:', i)
for train_index, test_index in tss.split(X):
#split data
X_train, X_test = X.iloc[train_index, :], X.iloc[test_index,:]
y_train, y_test = y.iloc[train_index], y.iloc[test_index]
print('Original Data Shape %s' % Counter(y_train))
#Oversample data
sm = BorderlineSMOTE(sampling_strategy=i, random_state=1)
X_res, y_res = sm.fit_resample(X_train, y_train)
print('Resampled dataset shape %s' % Counter(y_res))
# Train the model
model = clf.fit(X_res, y_res)
# Append metrics to the list
F_measure_model.append(fbeta_score(y_test, model.predict(X_test),beta=2))
roc_auc_model.append(roc_auc_score(y_test, model.predict(X_test)))
brier_score_model.append(brier_score_loss(y_test, model.predict(X_test)))
# Print the model metrics
metrics = pd.DataFrame(
{'Result': ["Average"],
'F_measure': np.average(F_measure_model),
'ROC_AUC': np.average(roc_auc_model),
'Brier_Score' : np.average(brier_score_model)
})
print("Model Metrics:")
print(metrics)
'''
'''
###############################################################################
#NEIGHBOURHOOD CLEANING RULE (DISTANCE-BASED UNDERSAMPLING) + DECISION TREE
for i in (2,3,4,5):
print("n_neighbours:", i)
#initialize list
F_measure_model = []
roc_auc_model = []
brier_score_model = []
for train_index, test_index in tss.split(X):
#split data
X_train, X_test = X.iloc[train_index, :], X.iloc[test_index,:]
y_train, y_test = y.iloc[train_index], y.iloc[test_index]
print('Original Data Shape %s' % Counter(y_train))
#Undersample data
ncr = NeighbourhoodCleaningRule(sampling_strategy='majority', n_neighbors=i)
X_res, y_res = ncr.fit_sample(X_train, y_train)
print('Resampled dataset shape %s' % Counter(y_res))
# Train the model
model = clf.fit(X_res, y_res)
# Append metrics to the list
F_measure_model.append(fbeta_score(y_test, model.predict(X_test),beta=2))
roc_auc_model.append(roc_auc_score(y_test, model.predict(X_test)))
brier_score_model.append(brier_score_loss(y_test, model.predict(X_test)))
# Print the model metrics
metrics = pd.DataFrame(
{'Result': ["Average"],
'F_measure': np.average(F_measure_model),
'ROC_AUC': np.average(roc_auc_model),
'Brier_Score' : np.average(brier_score_model)
})
print("Model Metrics:")
print(metrics)
'''
'''
###############################################################################
#UNDER/OVERSAMPLING COMBO - SMOTE-ENN + DECISION TREE
for i in (0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9):
print('sampling_strategy:', i)
#initialize list
F_measure_model = []
roc_auc_model = []
brier_score_model = []
for train_index, test_index in tss.split(X):
#split data
X_train, X_test = X.iloc[train_index, :], X.iloc[test_index,:]
y_train, y_test = y.iloc[train_index], y.iloc[test_index]
print('Original Data Shape %s' % Counter(y_train))
#Run sampling model
smote_enn = SMOTEENN(random_state=1, sampling_strategy=i, enn=EditedNearestNeighbours(sampling_strategy='majority'), smote=SMOTE(sampling_strategy='minority'))
X_res, y_res = smote_enn.fit_resample(X_train, y_train)
print('Resampled dataset shape %s' % Counter(y_res))
# Train the model
model = clf.fit(X_res, y_res)
# Append metrics to the list
F_measure_model.append(fbeta_score(y_test, model.predict(X_test),beta=2))
roc_auc_model.append(roc_auc_score(y_test, model.predict(X_test)))
brier_score_model.append(brier_score_loss(y_test, model.predict(X_test)))
# Print the model metrics
metrics = pd.DataFrame(
{'Result': ["Average"],
'F_measure': np.average(F_measure_model),
'ROC_AUC': np.average(roc_auc_model),
'Brier_Score' : np.average(brier_score_model)
})
print("Model Metrics:")
print(metrics)
'''
'''
###############################################################################
#UNDER/OVERSAMPLING COMBO - SMOTE-TOMEK + DECISION TREE
for i in ("0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9"):
print('sampling_strategy:', i)
#initialize list
F_measure_model = []
roc_auc_model = []
brier_score_model = []
for train_index, test_index in tss.split(X):
#split data
X_train, X_test = X.iloc[train_index, :], X.iloc[test_index,:]
y_train, y_test = y.iloc[train_index], y.iloc[test_index]
print('Original Data Shape %s' % Counter(y_train))
#Run sampling model
smote_tomek = SMOTETomek(random_state=1, sampling_strategy=i)
X_res, y_res = smote_tomek.fit_resample(X_train, y_train)
print('Resampled dataset shape %s' % Counter(y_res))
# Train the model
model = clf.fit(X_res, y_res)
# Append metrics to the list
F_measure_model.append(fbeta_score(y_test, model.predict(X_test),beta=2))
roc_auc_model.append(roc_auc_score(y_test, model.predict(X_test)))
brier_score_model.append(brier_score_loss(y_test, model.predict(X_test)))
# Print the model metrics
metrics = pd.DataFrame(
{'Result': ["Average"],
'F_measure': np.average(F_measure_model),
'ROC_AUC': np.average(roc_auc_model),
'Brier_Score' : np.average(brier_score_model)
})
print("Model Metrics:")
print(metrics)
'''
F_measure_model = []
roc_auc_model = []
brier_score_model = []
for train_index, test_index in tss.split(X):
#split data
X_train, X_test = X.iloc[train_index, :], X.iloc[test_index,:]
y_train, y_test = y.iloc[train_index], y.iloc[test_index]
print('Original Data Shape %s' % Counter(y_train))
#Run sampling model
smote_enn = SMOTEENN(random_state=1, sampling_strategy='auto')
X_res, y_res = smote_enn.fit_resample(X_train, y_train)
print('Resampled dataset shape %s' % Counter(y_res))
# Train the model
model = clf.fit(X_res, y_res)
# Append metrics to the list
print(fbeta_score(y_test, model.predict(X_test),beta=2))
print(roc_auc_score(y_test, model.predict(X_test)))
print(brier_score_loss(y_test, model.predict(X_test)))