move：修改人格文件结构
This commit is contained in:
SengokuCola
349
src/plugins/personality_s/combined_test.py
Normal file
349
src/plugins/personality_s/combined_test.py
Normal file
@@ -0,0 +1,349 @@
|
||||
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"
|
||||
|
||||
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()
|
||||
Reference in New Issue
Block a user