forked from HanyangLiu/Hypoxemia-MLPred
-
Notifications
You must be signed in to change notification settings - Fork 0
/
train_realtime_catboost.py
189 lines (154 loc) · 8.28 KB
/
train_realtime_catboost.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
176
177
178
179
180
181
182
183
184
185
import os
import pandas as pd
import numpy as np
from file_config.config import config
from utils.utility_preprocess import PatientFilter, LabelAssignment, DataImputation
from utils.utility_analysis import plot_roc, plot_prc, metric_eval, line_search_best_metric, au_prc
from sklearn.model_selection import train_test_split, GridSearchCV, StratifiedKFold, cross_validate
from sklearn import preprocessing, metrics
from sklearn.utils import shuffle
from imblearn.metrics import sensitivity_specificity_support
from xgboost import XGBClassifier
from train_catboost import CatBoostClassifier
import matplotlib.pyplot as plt
import argparse
import pickle
import sys
import shap
def prepare_data(df_static, df_dynamic, dynamic_feature, args):
# label assignment (according to imputed SpO2)
print('Assigning labels...')
imputer = DataImputation()
df_static = imputer.impute_static_dataframe(df_static)
df_dynamic = imputer.impute_dynamic_dataframe(df_dynamic)
path_sta_label = 'data/result/static_label_' + str(args.hypoxemia_window) + '.pkl'
path_dyn_label = 'data/result/dynamic_label_' + str(args.hypoxemia_window) + '.pkl'
label_assign = LabelAssignment(hypoxemia_thresh=args.hypoxemia_thresh,
hypoxemia_window=args.hypoxemia_window,
prediction_window=args.prediction_window)
print('Assigning labels...')
static_label, dynamic_label = label_assign.assign_label(df_static, df_dynamic)
static_label.to_pickle(path_sta_label)
dynamic_label.to_pickle(path_dyn_label)
# static_label = pd.read_pickle(path_sta_label)
# dynamic_label = pd.read_pickle(path_dyn_label)
positive_pids = label_assign.get_positive_pids(static_label)
print('Done.')
# get subgroup pids
subgroup_pids = PatientFilter(df_static=df_static,
mode=args.filter_mode,
include_icd=['J96.', 'J98.', '519.', '518.', '277.0', 'E84', 'Q31.5', '770.7',
'P27.1', '490', '491', '492', '493', '494', '495', '496', 'P27.8',
'P27.9', 'J44', 'V46.1', 'Z99.1'], # High-risk group
exclude_icd9=['745', '746', '747'],
exclude_icd10=['Q20', 'Q21', 'Q22', 'Q23', 'Q24', 'Q25', 'Q26']).filter_by_icd()
# exclude patients from OR14 and CCL "bedname"
exclude_list = df_static[df_static['BedName'].isin([14., 23., 24])]['pid'].values.tolist()
subgroup_pids = list(set(subgroup_pids) - set(exclude_list))
# split subgroup pids into training and test pid set
pid_train, pid_test, _, _ = train_test_split(static_label.loc[subgroup_pids]['pid'].values,
static_label.loc[subgroup_pids]['label'].values,
test_size=0.1,
random_state=args.random_state,
stratify=static_label.loc[subgroup_pids]['label'].values)
pid_train = sorted(list(pid_train))
pid_test = sorted(list(pid_test))
print('Positive Patient:', len(set(subgroup_pids) & set(positive_pids)), '/', len(subgroup_pids))
print('Before trimming:', len(positive_pids), '/', len(df_static))
print('Trimmed cases:', len(df_static) - len(subgroup_pids))
del df_static, df_dynamic
# select feature rows with pid in subgroup as data matrix
print('Training/testing split:', len(pid_train), '/', len(pid_test))
print('Split into training and test set...')
to_keep = (dynamic_label['if_to_drop'] == 0).values
is_in_train = dynamic_label[['pid']].isin(pid_train)['pid'].values
is_in_test = dynamic_label[['pid']].isin(pid_test)['pid'].values
selected_idx_train = list(np.where(to_keep & is_in_train)[0])
selected_idx_test = list(np.where(to_keep & is_in_test)[0])
# adjust features used
dynamic_feature = dynamic_feature.drop(columns=['AnesthesiaDuration', 'EBL', 'Urine_Output'])
# column_names = list(dynamic_feature.columns)
# drop_list = []
# for name in column_names:
# if 'FiO2' in name or 'coreTemp' in name:
# drop_list.append(name)
# dynamic_feature.drop(columns=drop_list)
# split into training and test set
X_train = dynamic_feature.iloc[selected_idx_train, 2:]
X_test = dynamic_feature.iloc[selected_idx_test, 2:]
y_train = dynamic_label.loc[selected_idx_train, 'label']
y_test = dynamic_label.loc[selected_idx_test, 'label']
# shuffle X and y
X_train, y_train = shuffle(X_train, y_train,
random_state=0
)
# positive number
num_pos = np.sum(y_train) + np.sum(y_test)
num_all = len(selected_idx_train) + len(selected_idx_test)
pos_rate = num_pos/num_all
print('Positive samples:', num_pos, '/', num_all)
print('Ratio:', '%0.2f' % (num_pos/num_all*100), '%')
return X_train, X_test, y_train, y_test, pos_rate
def train_gbtree(X_train, y_train, pos_rate, args):
X_train, y_train = shuffle(X_train, y_train,
random_state=0
)
result_table = pd.DataFrame(columns=['random_state', 'model', 'fpr', 'tpr', 'roc', 'prec', 'rec', 'prc', 'pos_rate'])
for rs in range(1):
classifiers = [
CatBoostClassifier(verbose=0,
# scale_pos_weight=(1 - pos_rate) / pos_rate,
learning_rate=args.lr,
depth=args.depth,
l2_leaf_reg=args.l2,
random_state=rs
)
]
for cls in classifiers:
print('Round', rs)
print('Training:', cls.__class__.__name__)
model = cls.fit(X_train, y_train)
y_prob = model.predict_proba(X_test)[::, 1]
# Evaluation
fpr, tpr, _ = metrics.roc_curve(y_test, y_prob)
prec, rec, _ = metrics.precision_recall_curve(y_test, y_prob)
print('--------------------------------------------')
print('Evaluation of test set:', cls.__class__.__name__)
print("AU-ROC:", "%0.4f" % metrics.auc(fpr, tpr),
"AU-PRC:", "%0.4f" % metrics.auc(rec, prec))
print('--------------------------------------------')
result_table = result_table.append({
'random_state': rs,
'model': cls.__class__.__name__,
'fpr': fpr,
'tpr': tpr,
'roc': metrics.auc(fpr, tpr),
'prec': prec,
'rec': rec,
'prc': metrics.auc(rec, prec),
'y_test': y_test,
'y_prob': y_prob,
'pos_rate': pos_rate
}, ignore_index=True)
save_name = 'data/result/model_comparison/realtime_gbtree_random.pkl'
# save results
result_table.to_pickle(save_name)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='hypoxemia prediction')
parser.add_argument('--hypoxemia_thresh', type=int, default=90)
parser.add_argument('--hypoxemia_window', type=int, default=10)
parser.add_argument('--prediction_window', type=int, default=5)
parser.add_argument('--filter_mode', type=str, default='exclude')
parser.add_argument('--feature_file', type=str, default='dynamic-ewm-notxt-nonimp.csv')
parser.add_argument('--random_state', type=int, default=1)
parser.add_argument('--n_jobs', type=int, default=-1)
parser.add_argument('--lr', type=float, default=0.02)
parser.add_argument('--depth', type=int, default=6)
parser.add_argument('--l2', type=int, default=3)
args = parser.parse_args()
print(args)
X_train, X_test, y_train, y_test, pos_rate = prepare_data(df_static=pd.read_csv(config.get('processed', 'df_static_file')),
df_dynamic=pd.read_csv(config.get('processed', 'df_dynamic_file')),
dynamic_feature=pd.read_csv('data/features/' + args.feature_file),
args=args)
train_gbtree(X_train, y_train, pos_rate, args)