from typing import Dict import json import os from pathlib import Path import sys from datetime import datetime import random from scipy import stats # 添加scipy导入用于t检验 current_dir = Path(__file__).resolve().parent project_root = current_dir.parent.parent.parent env_path = project_root / ".env.prod" root_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../..")) sys.path.append(root_path) from src.plugins.personality.big5_test import BigFiveTest # noqa: E402 from src.plugins.personality.renqingziji import PersonalityEvaluator_direct # noqa: E402 from src.plugins.personality.questionnaire import FACTOR_DESCRIPTIONS, PERSONALITY_QUESTIONS # noqa: E402 class CombinedPersonalityTest: def __init__(self): self.big5_test = BigFiveTest() self.scenario_test = PersonalityEvaluator_direct() self.dimensions = ["开放性", "严谨性", "外向性", "宜人性", "神经质"] def run_combined_test(self): """运行组合测试""" print("\n=== 人格特征综合评估系统 ===") print("\n本测试将通过两种方式评估人格特征：") print("1. 传统问卷测评（约40题）") print("2. 情景反应测评（15个场景）") print("\n两种测评完成后，将对比分析结果的异同。") input("\n准备好开始第一部分（问卷测评）了吗？按回车继续...") # 运行问卷测试 print("\n=== 第一部分：问卷测评 ===") print("本部分采用六级评分，请根据每个描述与您的符合程度进行打分：") print("1 = 完全不符合") print("2 = 比较不符合") print("3 = 有点不符合") print("4 = 有点符合") print("5 = 比较符合") print("6 = 完全符合") print("\n重要提示：您可以选择以下两种方式之一来回答问题：") print("1. 根据您自身的真实情况来回答") print("2. 根据您想要扮演的角色特征来回答") print("\n无论选择哪种方式，请保持一致并认真回答每个问题。") input("\n按回车开始答题...") questionnaire_results = self.run_questionnaire() # 转换问卷结果格式以便比较 questionnaire_scores = {factor: data["得分"] for factor, data in questionnaire_results.items()} # 运行情景测试 print("\n=== 第二部分：情景反应测评 ===") print("接下来，您将面对一系列具体场景，请描述您在每个场景中可能的反应。") print("每个场景都会评估不同的人格维度，共15个场景。") print("您可以选择提供自己的真实反应，也可以选择扮演一个您创作的角色来回答。") input("\n准备好开始了吗？按回车继续...") scenario_results = self.run_scenario_test() # 比较和展示结果 self.compare_and_display_results(questionnaire_scores, scenario_results) # 保存结果 self.save_results(questionnaire_scores, scenario_results) def run_questionnaire(self): """运行问卷测试部分""" # 创建题目序号到题目的映射 questions_map = {q["id"]: q for q in PERSONALITY_QUESTIONS} # 获取所有题目ID并随机打乱顺序 question_ids = list(questions_map.keys()) random.shuffle(question_ids) answers = {} total_questions = len(question_ids) for i, question_id in enumerate(question_ids, 1): question = questions_map[question_id] while True: try: print(f"\n问题 [{i}/{total_questions}]") print(f"{question['content']}") score = int(input("您的评分（1-6）: ")) if 1 <= score <= 6: answers[question_id] = score break else: print("请输入1-6之间的数字！") except ValueError: print("请输入有效的数字！") # 每10题显示一次进度 if i % 10 == 0: print(f"\n已完成 {i}/{total_questions} 题 ({int(i / total_questions * 100)}%)") return self.calculate_questionnaire_scores(answers) def calculate_questionnaire_scores(self, answers): """计算问卷测试的维度得分""" results = {} factor_questions = {"外向性": [], "神经质": [], "严谨性": [], "开放性": [], "宜人性": []} # 将题目按因子分类 for q in PERSONALITY_QUESTIONS: factor_questions[q["factor"]].append(q) # 计算每个维度的得分 for factor, questions in factor_questions.items(): total_score = 0 for q in questions: score = answers[q["id"]] # 处理反向计分题目 if q["reverse_scoring"]: score = 7 - score # 6分量表反向计分为7减原始分 total_score += score # 计算平均分 avg_score = round(total_score / len(questions), 2) results[factor] = {"得分": avg_score, "题目数": len(questions), "总分": total_score} return results def run_scenario_test(self): """运行情景测试部分""" final_scores = {"开放性": 0, "严谨性": 0, "外向性": 0, "宜人性": 0, "神经质": 0} dimension_counts = {trait: 0 for trait in final_scores.keys()} # 随机打乱场景顺序 scenarios = self.scenario_test.scenarios.copy() random.shuffle(scenarios) for i, scenario_data in enumerate(scenarios, 1): print(f"\n场景 [{i}/{len(scenarios)}] - {scenario_data['场景编号']}") print("-" * 50) print(scenario_data["场景"]) print("\n请描述您在这种情况下会如何反应：") response = input().strip() if not response: print("反应描述不能为空！") continue print("\n正在评估您的描述...") scores = self.scenario_test.evaluate_response(scenario_data["场景"], response, scenario_data["评估维度"]) # 更新分数 for dimension, score in scores.items(): final_scores[dimension] += score dimension_counts[dimension] += 1 # print("\n当前场景评估结果：") # print("-" * 30) # for dimension, score in scores.items(): # print(f"{dimension}: {score}/6") # 每5个场景显示一次总进度 if i % 5 == 0: print(f"\n已完成 {i}/{len(scenarios)} 个场景 ({int(i / len(scenarios) * 100)}%)") if i < len(scenarios): input("\n按回车继续下一个场景...") # 计算平均分 for dimension in final_scores: if dimension_counts[dimension] > 0: final_scores[dimension] = round(final_scores[dimension] / dimension_counts[dimension], 2) return final_scores def compare_and_display_results(self, questionnaire_scores: Dict, scenario_scores: Dict): """比较和展示两种测试的结果""" print("\n=== 测评结果对比分析 ===") print("\n" + "=" * 60) print(f"{'维度':<8} {'问卷得分':>10} {'情景得分':>10} {'差异':>10} {'差异程度':>10}") print("-" * 60) # 收集每个维度的得分用于统计分析 questionnaire_values = [] scenario_values = [] diffs = [] for dimension in self.dimensions: q_score = questionnaire_scores[dimension] s_score = scenario_scores[dimension] diff = round(abs(q_score - s_score), 2) questionnaire_values.append(q_score) scenario_values.append(s_score) diffs.append(diff) # 计算差异程度 diff_level = "低" if diff < 0.5 else "中" if diff < 1.0 else "高" print(f"{dimension:<8} {q_score:>10.2f} {s_score:>10.2f} {diff:>10.2f} {diff_level:>10}") print("=" * 60) # 计算整体统计指标 mean_diff = sum(diffs) / len(diffs) std_diff = (sum((x - mean_diff) ** 2 for x in diffs) / (len(diffs) - 1)) ** 0.5 # 计算效应量 (Cohen's d) pooled_std = ( ( sum((x - sum(questionnaire_values) / len(questionnaire_values)) ** 2 for x in questionnaire_values) + sum((x - sum(scenario_values) / len(scenario_values)) ** 2 for x in scenario_values) ) / (2 * len(self.dimensions) - 2) ) ** 0.5 if pooled_std != 0: cohens_d = abs(mean_diff / pooled_std) # 解释效应量 if cohens_d < 0.2: effect_size = "微小" elif cohens_d < 0.5: effect_size = "小" elif cohens_d < 0.8: effect_size = "中等" else: effect_size = "大" # 对所有维度进行整体t检验 t_stat, p_value = stats.ttest_rel(questionnaire_values, scenario_values) print("\n整体统计分析:") print(f"平均差异: {mean_diff:.3f}") print(f"差异标准差: {std_diff:.3f}") print(f"效应量(Cohen's d): {cohens_d:.3f}") print(f"效应量大小: {effect_size}") print(f"t统计量: {t_stat:.3f}") print(f"p值: {p_value:.3f}") if p_value < 0.05: print("结论: 两种测评方法的结果存在显著差异 (p < 0.05)") else: print("结论: 两种测评方法的结果无显著差异 (p >= 0.05)") print("\n维度说明：") for dimension in self.dimensions: print(f"\n{dimension}:") desc = FACTOR_DESCRIPTIONS[dimension] print(f"定义：{desc['description']}") print(f"特征词：{', '.join(desc['trait_words'])}") # 分析显著差异 significant_diffs = [] for dimension in self.dimensions: diff = abs(questionnaire_scores[dimension] - scenario_scores[dimension]) if diff >= 1.0: # 差异大于等于1分视为显著 significant_diffs.append( { "dimension": dimension, "diff": diff, "questionnaire": questionnaire_scores[dimension], "scenario": scenario_scores[dimension], } ) if significant_diffs: print("\n\n显著差异分析：") print("-" * 40) for diff in significant_diffs: print(f"\n{diff['dimension']}维度的测评结果存在显著差异：") print(f"问卷得分：{diff['questionnaire']:.2f}") print(f"情景得分：{diff['scenario']:.2f}") print(f"差异值：{diff['diff']:.2f}") # 分析可能的原因 if diff["questionnaire"] > diff["scenario"]: print("可能原因：在问卷中的自我评价较高，但在具体情景中的表现较为保守。") else: print("可能原因：在具体情景中表现出更多该维度特征，而在问卷自评时较为保守。") def save_results(self, questionnaire_scores: Dict, scenario_scores: Dict): """保存测试结果""" results = { "测试时间": datetime.now().strftime("%Y-%m-%d %H:%M:%S"), "问卷测评结果": questionnaire_scores, "情景测评结果": scenario_scores, "维度说明": FACTOR_DESCRIPTIONS, } # 确保目录存在 os.makedirs("results", exist_ok=True) # 生成带时间戳的文件名 filename = f"results/personality_combined_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" # 保存到文件 with open(filename, "w", encoding="utf-8") as f: json.dump(results, f, ensure_ascii=False, indent=2) print(f"\n完整的测评结果已保存到：{filename}") def load_existing_results(): """检查并加载已有的测试结果""" results_dir = "results" if not os.path.exists(results_dir): return None # 获取所有personality_combined开头的文件 result_files = [f for f in os.listdir(results_dir) if f.startswith("personality_combined_") and f.endswith(".json")] if not result_files: return None # 按文件修改时间排序，获取最新的结果文件 latest_file = max(result_files, key=lambda f: os.path.getmtime(os.path.join(results_dir, f))) print(f"\n发现已有的测试结果：{latest_file}") try: with open(os.path.join(results_dir, latest_file), "r", encoding="utf-8") as f: results = json.load(f) return results except Exception as e: print(f"读取结果文件时出错：{str(e)}") return None def main(): test = CombinedPersonalityTest() # 检查是否存在已有结果 existing_results = load_existing_results() if existing_results: print("\n=== 使用已有测试结果进行分析 ===") print(f"测试时间：{existing_results['测试时间']}") questionnaire_scores = existing_results["问卷测评结果"] scenario_scores = existing_results["情景测评结果"] # 直接进行结果对比分析 test.compare_and_display_results(questionnaire_scores, scenario_scores) else: print("\n未找到已有的测试结果，开始新的测试...") test.run_combined_test() if __name__ == "__main__": main()