代码交付.rar

import tkinter as tk from sklearn import preprocessing import random from sklearn.model_selection import train_test_split from sklearn.preprocessing import MinMaxScaler from sklearn import preprocessing from datetime import datetime import time import math from matplotlib import pyplot from sklearn.model_selection import train_test_split from scipy import stats, integrate import matplotlib.pyplot as plt from matplotlib.font_manager import FontProperties import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.model_selection import KFold from sklearn.metrics import classification_report from sklearn.model_selection import learning_curve from sklearn.model_selection import validation_curve from sklearn.model_selection import cross_val_score from sklearn.preprocessing import StandardScaler from sklearn.decomposition import PCA from sklearn.metrics import confusion_matrix from sklearn.model_selection import train_test_split from sklearn import metrics from sklearn.svm import SVR from sklearn.neighbors import KNeighborsRegressor from sklearn.metrics import mean_squared_error, mean_absolute_error # 评价指标 from sklearn.linear_model import LogisticRegression from sklearn.ensemble import RandomForestRegressor from sklearn.ensemble import AdaBoostRegressor,AdaBoostClassifier from xgboost import XGBRegressor from sklearn.ensemble import GradientBoostingRegressor from lightgbm import LGBMRegressor from date_process import data_read_csv train_x,train_y=data_read_csv() x_train, x_test, y_train, y_test = train_test_split(np.array(train_x), np.array(train_y), test_size=0.4,shuffle=True,random_state=1) twox_train, twox_test, twoy_train, twoy_test = train_test_split(np.array(x_test), np.array(y_test), test_size=0.5,shuffle=True,random_state=1) print('x_train.shape',x_train.shape) print('x_test.shape',x_test.shape) # 集成学习模型 # svm算法 print(x_train[1]) svm = AdaBoostClassifier() svm.fit(twox_train, twoy_train) svm_pred = svm.predict(twox_test) def add_numbers(): # try: # 从输入框获取值，并将其转换为浮点数 numbers = [float(entry_list[i].get()) for i in range(11)] print(numbers) numbers=np.array(numbers).reshape(1,-1) # 执行加法操作 result = svm.predict(numbers) # 在结果标签中显示结果 result_label.config(text=f"预测结果：{result}") # except ValueError: # # 处理非法输入，例如文本而不是数字 # result_label.config(text="请输入有效的特征") # 创建主窗口 root = tk.Tk() root.title("疾病预测系统") # 创建输入框和标签 entry_list = [] for i in range(11): label = tk.Label(root, text=f"请输入特征{i + 1}:") label.grid(row=i, column=0, padx=10, pady=10) entry = tk.Entry(root) entry.grid(row=i, column=1, padx=10, pady=10) entry_list.append(entry) # 创建按钮，并绑定点击事件 calculate_button = tk.Button(root, text="预测是否有疾病", command=add_numbers) calculate_button.grid(row=12, column=0, columnspan=2, pady=10) # 创建用于显示结果的标签 result_label = tk.Label(root, text="预测结果：") result_label.grid(row=13, column=0, columnspan=2, pady=10) # 启动主循环 root.mainloop() # [ 40. 0. 1. 106. 240. 0. 0. 80. 1. 0. 0.]