secret 完善了两种测评方法

Author: SengokuCola

src/plugins/personality/big5_test.py

@@ -6,6 +6,7 @@
 import os
 import sys
 from pathlib import Path
+import random
 
 current_dir = Path(__file__).resolve().parent
 project_root = current_dir.parent.parent.parent
@@ -37,14 +38,24 @@ def run_test(self):
         print("6 = 完全符合")
         print("\n请认真阅读每个描述，选择最符合您实际情况的选项。\n")
 
+        # 创建题目序号到题目的映射
+        questions_map = {q['id']: q for q in self.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 = {}
-        for question in self.questions:
+        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{question['id']}. {question['content']}")
+                    print(f"\n[{i}/{total_questions}] {question['content']}")
                     score = int(input("您的评分（1-6）: "))
                     if 1 <= score <= 6:
-                        answers[question['id']] = score
+                        answers[question_id] = score
                         break
                     else:
                         print("请输入1-6之间的数字！")

src/plugins/personality/combined_test.py

@@ -0,0 +1,357 @@
+from typing import Dict, List
+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
+from src.plugins.personality.renqingziji import PersonalityEvaluator_direct
+from src.plugins.personality.questionnaire import FACTOR_DESCRIPTIONS, PERSONALITY_QUESTIONS
+
+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请认真阅读每个描述，选择最符合您实际情况的选项。")
+        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。")
+        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(f"\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()