-
Notifications
You must be signed in to change notification settings - Fork 0
/
run_xgb.py
175 lines (131 loc) · 6.87 KB
/
run_xgb.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
import pandas as pd
import numpy as np
import xgboost as xgb
from sklearn.metrics import roc_auc_score, roc_curve
from sklearn.model_selection import train_test_split, KFold, GridSearchCV
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
import os
from typing import Dict, Tuple, Any
from imblearn.over_sampling import SMOTE, RandomOverSampler, SVMSMOTE
from imblearn.under_sampling import RandomUnderSampler
warnings.filterwarnings("ignore")
def get_fairness_metrics(y_true: np.ndarray, y_pred: np.ndarray, groups: pd.Series, fixed_fpr: float = 0.05) -> Tuple[float, pd.DataFrame]:
from aequitas.group import Group
aequitas_df = pd.DataFrame({
"score": y_pred,
"label_value": y_true,
"group": groups
})
g = Group()
disparities_df = g.get_crosstabs(aequitas_df, score_thresholds={"score_val": [fixed_fpr]})[0]
predictive_equality = disparities_df["fpr"].min() / disparities_df["fpr"].max()
return predictive_equality, disparities_df
def evaluate(predictions: np.ndarray, ground_truth: np.ndarray, X: pd.DataFrame, fixed_fpr: float = 0.05) -> list[float]:
fprs, tprs, thresholds = roc_curve(ground_truth, predictions)
tpr = tprs[fprs < fixed_fpr][-1]
fpr = fprs[fprs < fixed_fpr][-1]
threshold = thresholds[fprs < fixed_fpr][-1]
sorted_ages = np.sort(X["customer_age"])
young_threshold = sorted_ages[int(0.95 * len(sorted_ages))]
groups = (X["customer_age"] > young_threshold).map({True: ">young_threshold", False: "<=young_threshold"})
predictive_equality, _ = get_fairness_metrics(ground_truth, predictions, groups, fixed_fpr)
return [round(tpr, 10), round(predictive_equality, 6)]
def train_and_evaluate_xgboost(X: pd.DataFrame, y: pd.Series, X_val: pd.DataFrame, y_val: pd.Series,
X_test: pd.DataFrame, y_test: pd.Series, best_params: Dict[str, Any]) -> Dict[str, Any]:
xgb_model = xgb.XGBClassifier(**best_params, use_label_encoder=False)
xgb_model.fit(X, y)
y_train_pred = xgb_model.predict_proba(X)[:, 1]
y_val_pred = xgb_model.predict_proba(X_val)[:, 1]
val_results = evaluate(y_val_pred, y_val, X=X_val)
y_test_pred = xgb_model.predict_proba(X_test)[:, 1]
test_results = evaluate(y_test_pred, y_test, X=X_test)
return {
'val_results': val_results,
'test_results': test_results
}
def read_data() -> Dict[str, pd.DataFrame]:
data = {
'X_train': pd.read_csv("data/X_train_oh_1.csv"),
'X_val': pd.read_csv("data/X_val_oh_1.csv"),
'X_test': pd.read_csv("data/X_test_oh_1.csv"),
'y_train': pd.read_csv("data/y_train_1.csv").iloc[:, 0],
'y_val': pd.read_csv("data/y_val_1.csv").iloc[:, 0],
'y_test': pd.read_csv("data/y_test_1.csv").iloc[:, 0],
}
return data
def apply_sampling_technique(X: pd.DataFrame, y: pd.Series, technique: str) -> Tuple[pd.DataFrame, pd.Series]:
if technique == 'SMOTE':
sampler = SMOTE(random_state=42)
elif technique == 'RandomOverSampler':
sampler = RandomOverSampler(random_state=42)
elif technique == 'RandomUnderSampler':
sampler = RandomUnderSampler(random_state=42)
elif technique == 'SVMSMOTE':
sampler = SVMSMOTE(random_state=42)
else:
return X, y
X_resampled, y_resampled = sampler.fit_resample(X, y)
return X_resampled, y_resampled
def main():
os.makedirs('model_performance', exist_ok=True)
data = read_data()
X, y = data['X_train'], data['y_train']
sampling_techniques = ['None'] # 'None', 'RandomUnderSampler', 'SVMSMOTE'
for technique in sampling_techniques:
print(f"\nApplying {technique} sampling technique:")
if technique != 'None':
X_sampled, y_sampled = apply_sampling_technique(X, y, technique)
else:
X_sampled, y_sampled = X, y
xgb_model = xgb.XGBClassifier()
param_grid = {
'n_estimators': [300, 400],
'learning_rate': [0.1],
'max_depth': [3, 4],
#'subsample': [0.9, 1],
#'colsample_bytree': [0.8, 1.0],
#'reg_alpha': [0.5, 0.7],
'scale_pos_weight': [103], # use balanced weight
# The scale_pos_weight value is used to scale the gradient for the positive class.
# Use 100 can simulate the effect that we use class_weight = 'balanced' in sklearn logistics.
}
kf = KFold(n_splits=4, shuffle=True, random_state=42)
grid_search = GridSearchCV(estimator=xgb_model, param_grid=param_grid, scoring='roc_auc', cv=kf, n_jobs=-1)
grid_search.fit(X_sampled, y_sampled)
best_params = grid_search.best_params_
print("Best parameters found: ", best_params)
evaluation_results = train_and_evaluate_xgboost(
X_sampled, y_sampled, data['X_val'], data['y_val'],
data['X_test'], data['y_test'], best_params
)
print("Evaluation Results: ", evaluation_results)
if __name__ == "__main__":
main()
'''
Model results:
!!!Best Model without re-sampling!!!
Best parameters found: {'colsample_bytree': 0.6, 'learning_rate': 0.1, 'max_depth': 3, 'n_estimators': 400, 'reg_alpha': 0.7, 'reg_lambda': 1, 'subsample': 0.9}
Evaluation Results: {'val_results': [0.5209580838, 0.410855], 'test_results': [0.5731462926, 0.279965]}
!!!Re-weight!!!
Applying None sampling technique:
Best parameters found: {'learning_rate': 0.1, 'max_depth': 3, 'n_estimators': 300, 'scale_pos_weight': 100}
Evaluation Results: {'val_results': [0.5309381238, 0.90646], 'test_results': [0.5551102204, 0.865386]}
Applying RandomOverSampler sampling technique:
Best parameters found: {'colsample_bytree': 1.0, 'learning_rate': 0.1, 'max_depth': 4, 'n_estimators': 400, 'reg_alpha': 0.7, 'subsample': 0.9}
Evaluation Results: {'val_results': [0.49500998, 0.854023], 'test_results': [0.5130260521, 0.733977]}
!!!Best Model with re-sampling!!!
Applying RandomUnderSampler sampling technique:
Best parameters found: {'colsample_bytree': 0.8, 'learning_rate': 0.1, 'max_depth': 3, 'n_estimators': 300, 'reg_alpha': 0.7, 'subsample': 0.9}
Evaluation Results: {'val_results': [0.4930139721, 0.918649], 'test_results': [0.5170340681, 0.898675]}
Applying SMOTE sampling technique:
Best parameters found: {'colsample_bytree': 0.8, 'learning_rate': 0.1, 'max_depth': 4, 'n_estimators': 400, 'reg_alpha': 0.7, 'subsample': 0.9}
Evaluation Results: {'val_results': [0.4810379242, 0.513641], 'test_results': [0.5190380762, 0.371253]}
Applying SVMSMOTE sampling technique:
Best parameters found: {'colsample_bytree': 0.8, 'learning_rate': 0.1, 'max_depth': 4, 'n_estimators': 400, 'reg_alpha': 0.5, 'subsample': 0.9}
Evaluation Results: {'val_results': [0.5149700599, 0.447515], 'test_results': [0.5531062124, 0.345252]}
'''
'''
Findings
'''