用python实现反推kano模型的数据，并且用py分析kano模型

import tkinter as tk from tkinter import messagebox import pandas as pd import random # 定义Kano模型的评分标准 kano_scores = { '魅力型 (A)': {'positive': [5], 'negative': [1, 2, 3, 4]}, '期望型 (O)': {'positive': [4, 5], 'negative': [1, 2, 3]}, '必备型 (M)': {'positive': [3, 4, 5], 'negative': [1, 2]}, '无差异型 (I)': {'positive': [2, 3, 4, 5], 'negative': [1, 2, 3, 4, 5]}, '反向型 (R)': {'positive': [1, 2], 'negative': [3, 4, 5]}, '可疑型 (Q)': {'positive': [1, 2, 3, 4, 5], 'negative': [1, 2, 3, 4, 5]} } def generate_kano_data(num_records, num_questions, classifications): data = {} for i in range(1, num_questions + 1): question_key_positive = f"问题 {i} 正向" question_key_negative = f"问题 {i} 反向" # 根据分类选择对应的评分 if i in classifications['魅力型 (A)']: positive_scores = kano_scores['魅力型 (A)']['positive'] negative_scores = kano_scores['魅力型 (A)']['negative'] elif i in classifications['期望型 (O)']: positive_scores = kano_scores['期望型 (O)']['positive'] negative_scores = kano_scores['期望型 (O)']['negative'] elif i in classifications['必备型 (M)']: positive_scores = kano_scores['必备型 (M)']['positive'] negative_scores = kano_scores['必备型 (M)']['negative'] elif i in classifications['无差异型 (I)']: positive_scores = kano_scores['无差异型 (I)']['positive'] negative_scores = kano_scores['无差异型 (I)']['negative'] elif i in classifications['反向型 (R)']: positive_scores = kano_scores['反向型 (R)']['positive'] negative_scores = kano_scores['反向型 (R)']['negative'] else: positive_scores = kano_scores['可疑型 (Q)']['positive'] negative_scores = kano_scores['可疑型 (Q)']['negative'] # 生成数据 data[question_key_positive] = [random.choice(positive_scores) for _ in range(num_records)] data[question_key_negative] = [random.choice(negative_scores) for _ in range(num_records)] return pd.DataFrame(data) def save_to_excel(df): try: filename = "kano_model_data.xlsx" df.to_excel(filename, index=False) messagebox.showinfo("成功", f"数据已保存到 {filename}") except Exception as e: messagebox.showerror("错误", f"导出数据时发生错误: {str(e)}") def on_generate_click(): try: num_records = int(entry_num_records.get()) num_questions = int(entry_num_questions.get()) except ValueError: messagebox.showerror("错误", "请输入有效的数字。") return # 初始化classifications字典，确保包含所有Kano分类的键 classifications = { '魅力型 (A)': [], '期望型 (O)': [], '必备型 (M)': [], '无差异型 (I)': [], '反向型 (R)': [], '可疑型 (Q)': [] } for label, entry in kano_entries.items(): values = entry.get().strip() if values: classifications[label] = [int(v.strip()) for v in values.split(',')] else: classifications[label] = [] # 调试信息：打印classifications字典 print("Classifications:", classifications) df = generate_kano_data(num_records, num_questions, classifications) global generated_df generated_df = df # 全局存储生成的数据 messagebox.showinfo("成功", "数据生成成功。") def on_export_click(): if 'generated_df' in globals(): save_to_excel(generated_df) else: messagebox.showwarning("警告", "没有数据可以导出。请先生成数据。") # 创建主窗口 root = tk.Tk() root.title("Kano模型数据生成器") # 输入框 - 生成多少条数据 label_num_records = tk.Label(root, text="生成数据条数:") label_num_records.pack() entry_num_records = tk.Entry(root) entry_num_records.pack() # 输入框 - 生成多少个题目 label_num_questions = tk.Label(root, text="生成题目数量:") label_num_questions.pack() entry_num_questions = tk.Entry(root) entry_num_questions.pack() # Kano分类输入框 kano_labels = ["魅力型 (A):", "期望型 (O):", "必备型 (M):", "无差异型 (I):", "反向型 (R):", "可疑型 (Q):"] kano_entries = {} for label in kano_labels: kano_label = tk.Label(root, text=label) kano_label.pack() kano_entry = tk.Entry(root) kano_entry.pack() kano_entries[label] = kano_entry # 生成按钮 generate_button = tk.Button(root, text="生成数据", command=on_generate_click) generate_button.pack() # 导出按钮 export_button = tk.Button(root, text="导出到Excel", command=on_export_click) export_button.pack() # 运行主循环 root.mainloop()