python代码主要围绕脂肪胰相关数据展开了一系列数据处理、分析、建模和评估工作

import numpy as np
import pandas as pd
import torch
from torch.utils.data import Dataset, DataLoader, TensorDataset
import re
import matplotlib.pyplot as plt
import seaborn as sns
import scipy
from datetime import datetime
from sklearn.impute import SimpleImputer
from sklearn.svm import SVC
from sklearn.feature_selection import RFE, RFECV
from sklearn.linear_model import LogisticRegression
from sklearn import preprocessing
from sklearn.svm import SVC, LinearSVC
from sklearn.feature_selection import SelectFromModel
from sklearn.metrics import confusion_matrix
from sklearn.metrics import f1_score
from sklearn.metrics import roc_curve,auc
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from xgboost import XGBClassifier
from sklearn.model_selection import GridSearchCV
from PIL import Image
import warnings
warnings.filterwarnings("ignore")
# 显示所有列
pd.set_option('display.max_columns', None)
# 显示所有行
pd.set_option('display.max_rows', None)
plt.rcParams['font.sans-serif'] = ['KaiTi']
plt.rcParams['axes.unicode_minus'] = False
dataConcat1 = pd.read_csv('test.csv')

dataConcat1["P"] = dataConcat1["P"].astype(float).apply(lambda x: x*333 if (x < 50) or (x!='nan') or (x is not None) else x)
dataConcat1["Q"] = dataConcat1["Q"].astype(float).apply(lambda x: x*333 if (x < 50) or (x!='nan') or (x is not None) else x)
dataConcat1.fillna