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# conda create -n IMH-XGBoost conda-forge::huggingface_hub | |
# pip install -r requirements.txt -i https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple | |
import os | |
# 获取模型 | |
if not os.path.exists('xgb.baseline.model.json'): | |
from huggingface_hub import login, snapshot_download | |
login(token=os.environ.get("HF_TOKEN")) | |
snapshot_download(repo_id='Limour-blog/IMH-XGBoost', local_dir=r'.', allow_patterns='xgb.baseline.model.json') | |
import xgboost as xgb | |
import numpy as np | |
clf = xgb.XGBClassifier(enable_categorical=True) | |
clf.load_model(r"xgb.baseline.model.json") | |
def limit(_value, _min, _max): | |
return min(max(_value, _min), _max) | |
def args2Array( | |
BSA=1.824, | |
CTNT=4.715, # _0 | |
CK_MB=200.5, # _0 | |
CRP=18.01, # _1 | |
PD_DIMER=1.047, | |
NT_PROBNP=883.6, # _3 | |
ARRHYTHMIA=0, | |
APOE=36.76, | |
MHR=0.8378 | |
): | |
BSA = limit(BSA, 1.401, 2.231) | |
BSA = (BSA - 1.824) / 0.1654 | |
CTNT = limit(CTNT, -9.566, 19.58) | |
CTNT = (CTNT - 4.715) / 3.877 | |
CK_MB = limit(CK_MB, -213, 571) | |
CK_MB = (CK_MB - 200.5) / 154.3 | |
CRP = limit(CRP, -25.04, 55.86) | |
CRP = (CRP - 18.01) / 17.53 | |
PD_DIMER = limit(PD_DIMER, -1.131, 2.959) | |
PD_DIMER = (PD_DIMER - 1.047) / 0.8045 | |
NT_PROBNP = limit(NT_PROBNP, -610.1, 2106) | |
NT_PROBNP = (NT_PROBNP - 883.6) / 625.8 | |
APOE = limit(APOE, 3.625, 68.62) | |
APOE = (APOE - 36.76) / 13.85 | |
MHR = limit(MHR, -0.06439, 1.683) | |
MHR = (MHR - 0.8378) / 0.3103 | |
return np.array([[BSA, CTNT, CK_MB, | |
CRP, PD_DIMER, NT_PROBNP, | |
ARRHYTHMIA, APOE, MHR]]) | |
def predict(_array): | |
return float(clf.predict_proba(_array)[0,1]) | |
# 测试模型预测阳性正确 | |
assert predict(args2Array( | |
BSA=1.99, | |
CTNT=10, # _0 | |
CK_MB=374, # _0 | |
CRP=14.4, # _1 | |
PD_DIMER=0.88, | |
NT_PROBNP=463.7, # _3 | |
ARRHYTHMIA=0, | |
APOE=37, | |
MHR=0.8378 | |
)) >= 0.72 | |
# 测试模型预测阴性正确 | |
assert predict(args2Array( | |
BSA=1.51, | |
CTNT=1.53, # _0 | |
CK_MB=95, # _0 | |
CRP=4.9, # _1 | |
PD_DIMER=1.4, | |
NT_PROBNP=519.2, # _3 | |
ARRHYTHMIA=0, | |
APOE=36.76, | |
MHR=0.5581 | |
)) < 0.72 | |
import gradio as gr | |
# ========== 完整版的模型 ========== | |
with gr.Blocks() as complete_model: | |
with gr.Row(): | |
g_BSA = gr.Number(label="BSA", scale=1, value=1.824, | |
info="患者的体表面积, 缺失请保持默认值", | |
interactive=True) | |
g_ARRHYTHMIA = gr.Checkbox(label="ARRHYTHMIA", scale=1, value=False, | |
info="患者是否发生恶性心律失常或传导阻滞, 缺失请保持默认值", | |
interactive=True) | |
g_PD_DIMER = gr.Number(label="PD_DIMER", scale=1, value=1.047, | |
info="PCI术后D-二聚体峰值, 缺失请保持默认值", | |
interactive=True) | |
with gr.Row(): | |
g_CTNT = gr.Number(label="CTNT", scale=1, value=4.715, | |
info="PCI术后即刻的CTNT值, 缺失请保持默认值", | |
interactive=True) | |
g_CK_MB = gr.Number(label="CK_MB", scale=1, value=200.5, | |
info="PCI术后即刻的CK_MB值, 缺失请保持默认值", | |
interactive=True) | |
g_NT_PROBNP = gr.Number(label="NT_PROBNP", scale=1, value=883.6, | |
info="PCI术后36小时的NT_PROBNP值, 缺失请保持默认值", | |
interactive=True) | |
with gr.Row(): | |
g_CRP = gr.Number(label="CRP", scale=1, value=18.01, | |
info="PCI术后24小时的CRP值, 缺失请保持默认值", | |
interactive=True) | |
g_APOE = gr.Number(label="APOE", scale=1, value=36.76, | |
info="患者血脂APOE值, 缺失请保持默认值", | |
interactive=True) | |
g_MHR = gr.Number(label="MHR", scale=1, value=0.8378, | |
info="单核细胞与高密度脂蛋白胆固醇比值, 缺失请保持默认值", | |
interactive=True) | |
with gr.Row(): | |
g_output1 = gr.Number(label="XGB.predict_proba", scale=1, interactive=False, info="cutoff值为0.72") | |
g_output2 = gr.Textbox(label="结论", scale=1, interactive=False, info="预测患者IMH为阳性或阴性") | |
g_calc = gr.Button("计算", variant="primary", size='lg') | |
def btn_calc( | |
BSA, CTNT, CK_MB, | |
CRP, PD_DIMER, NT_PROBNP, | |
ARRHYTHMIA, APOE, MHR | |
): | |
res1 = predict(args2Array( | |
BSA=BSA, | |
CTNT=CTNT, # _0 | |
CK_MB=CK_MB, # _0 | |
CRP=CRP, # _1 | |
PD_DIMER=PD_DIMER, | |
NT_PROBNP=NT_PROBNP, # _3 | |
ARRHYTHMIA = (1 if ARRHYTHMIA else 0), | |
APOE=APOE, | |
MHR=MHR | |
)) | |
if res1 >= 0.72: | |
res2 = '阳性' | |
else: | |
res2 = '阴性' | |
return round(res1, 4), res2 | |
g_calc.click( | |
fn = btn_calc, | |
inputs=[g_BSA, g_CTNT, g_CK_MB, | |
g_CRP, g_PD_DIMER, g_NT_PROBNP, | |
g_ARRHYTHMIA, g_APOE, g_MHR], | |
outputs=[g_output1, g_output2] | |
) | |
# ========== 开始运行 ========== | |
demo = gr.TabbedInterface([complete_model], | |
["complete_model"]) | |
gr.close_all() | |
demo.queue(api_open=False, max_size=1).launch( | |
server_name = "0.0.0.0", | |
share=False, show_error=True, show_api=False) | |