701 lines
23 KiB
Markdown
701 lines
23 KiB
Markdown
# 🔮 药店销售预测结果分析增强方案
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## 一、预测结果解释系统设计 📊
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### 1. 预测解释功能概述
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在现有的预测系统基础上，增加预测结果解释功能，不仅返回预测值，还提供对预测结果的分析解释，帮助用户理解"为什么会得到这样的预测结果"和"哪些因素影响了这个预测"。
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### 2. 核心功能模块
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#### A. 特征重要性分析
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```python
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def analyze_feature_importance(model, X_test, feature_names):
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"""
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分析各个特征对预测结果的重要性
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Args:
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model: 训练好的模型
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X_test: 测试数据特征
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feature_names: 特征名称列表
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Returns:
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feature_importance_dict: 特征重要性字典
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"""
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# 根据不同模型类型实现特征重要性计算
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if hasattr(model, 'feature_importances_'): # 如GBDT等树模型
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importances = model.feature_importances_
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else: # 对于深度学习模型，使用排列重要性或SHAP值
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importances = calculate_permutation_importance(model, X_test)
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# 将特征名称与重要性值匹配
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feature_importance_dict = dict(zip(feature_names, importances))
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return feature_importance_dict
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```
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#### B. 预测因素分解
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```python
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def decompose_prediction_factors(prediction, feature_values, feature_importance):
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"""
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将预测结果分解为各个因素的贡献
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Args:
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prediction: 预测结果
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feature_values: 输入特征值
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feature_importance: 特征重要性
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Returns:
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factor_contributions: 各因素对预测的贡献
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"""
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# 计算每个特征的贡献
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factor_contributions = {}
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for feature, value in feature_values.items():
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if feature in feature_importance:
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contribution = value * feature_importance[feature]
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factor_contributions[feature] = {
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'value': value,
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'importance': feature_importance[feature],
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'contribution': contribution
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}
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# 按贡献大小排序
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sorted_factors = sorted(factor_contributions.items(),
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key=lambda x: abs(x[1]['contribution']),
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reverse=True)
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return sorted_factors
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```
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#### C. 历史模式匹配
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```python
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def find_similar_historical_patterns(current_prediction, historical_data, window_size=7):
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"""
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查找历史数据中与当前预测模式相似的时间段
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Args:
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current_prediction: 当前预测结果序列
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historical_data: 历史数据
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window_size: 比较窗口大小
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Returns:
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similar_patterns: 相似的历史模式列表
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"""
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similar_patterns = []
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# 计算当前预测的特征(如趋势、周期性等)
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current_features = extract_pattern_features(current_prediction)
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# 在历史数据中滑动窗口寻找相似模式
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for i in range(len(historical_data) - window_size):
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window = historical_data[i:i+window_size]
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window_features = extract_pattern_features(window)
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# 计算相似度
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similarity = calculate_similarity(current_features, window_features)
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if similarity > SIMILARITY_THRESHOLD:
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similar_patterns.append({
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'period': (i, i+window_size),
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'data': window,
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'similarity': similarity
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})
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# 按相似度排序
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similar_patterns.sort(key=lambda x: x['similarity'], reverse=True)
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return similar_patterns[:5] # 返回前5个最相似的模式
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```
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#### D. 异常值检测与解释
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```python
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def detect_and_explain_anomalies(predictions, historical_stats):
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"""
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检测预测结果中的异常值并提供解释
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Args:
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predictions: 预测结果序列
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historical_stats: 历史数据统计信息
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Returns:
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anomalies: 异常值及其解释
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"""
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anomalies = []
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# 计算预测值的统计特性
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mean = historical_stats['mean']
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std = historical_stats['std']
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# 检测异常值(例如超过2个标准差)
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for i, value in enumerate(predictions):
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z_score = (value - mean) / std
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if abs(z_score) > 2:
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# 异常值检测
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anomaly = {
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'day': i,
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'value': value,
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'z_score': z_score,
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'severity': 'high' if abs(z_score) > 3 else 'medium',
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'explanation': generate_anomaly_explanation(value, z_score, i)
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}
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anomalies.append(anomaly)
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return anomalies
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```
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## 二、预测结果解释API设计 🔌
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### 1. API接口扩展
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```python
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@app.route('/api/prediction/explain', methods=['POST'])
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def explain_prediction():
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"""
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提供预测结果的详细解释
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---
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tags:
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- 预测分析
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parameters:
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- name: body
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in: body
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required: true
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schema:
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type: object
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properties:
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product_id:
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type: string
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example: P001
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model_type:
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type: string
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enum: [mlstm, transformer, kan, optimized_kan]
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future_days:
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type: integer
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default: 7
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include_factors:
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type: boolean
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default: true
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include_similar_patterns:
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type: boolean
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default: true
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include_anomaly_detection:
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type: boolean
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default: true
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responses:
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200:
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description: 预测结果及其解释
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"""
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data = request.json
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product_id = data.get('product_id')
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model_type = data.get('model_type')
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future_days = data.get('future_days', 7)
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# 获取预测结果
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predictions, features = load_model_and_predict_with_features(
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product_id, model_type, future_days
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)
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# 准备响应数据
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response = {
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'status': 'success',
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'predictions': predictions.tolist(),
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'explanation': {}
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}
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# 根据请求参数添加不同类型的解释
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if data.get('include_factors', True):
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# 添加特征重要性分析
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model = load_model(product_id, model_type)
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feature_importance = analyze_feature_importance(model, features, FEATURE_NAMES)
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factor_contributions = decompose_prediction_factors(
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predictions, features, feature_importance
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)
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response['explanation']['factors'] = factor_contributions
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if data.get('include_similar_patterns', True):
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# 添加历史模式匹配
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historical_data = load_historical_data(product_id)
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similar_patterns = find_similar_historical_patterns(
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predictions, historical_data
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)
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response['explanation']['similar_patterns'] = similar_patterns
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if data.get('include_anomaly_detection', True):
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# 添加异常值检测
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historical_stats = calculate_historical_stats(product_id)
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anomalies = detect_and_explain_anomalies(predictions, historical_stats)
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response['explanation']['anomalies'] = anomalies
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# 生成总体解释文本
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response['explanation']['summary'] = generate_explanation_summary(
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predictions, response['explanation']
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)
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return jsonify(response)
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```
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### 2. 前端展示组件
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#### A. 预测结果解释面板
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```javascript
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// Vue组件示例
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<template>
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<div class="prediction-explanation">
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<el-card class="explanation-card">
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<div slot="header">
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<span>预测结果解释</span>
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</div>
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<!-- 总体解释摘要 -->
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<div class="explanation-summary">
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<h4>预测分析摘要</h4>
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<p>{{ explanation.summary }}</p>
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</div>
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<!-- 关键影响因素 -->
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<div v-if="explanation.factors" class="key-factors">
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<h4>关键影响因素</h4>
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<el-table :data="explanation.factors.slice(0, 5)" style="width: 100%">
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<el-table-column prop="factor" label="因素"></el-table-column>
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<el-table-column prop="contribution" label="贡献度">
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<template slot-scope="scope">
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<el-progress
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:percentage="Math.abs(scope.row.contribution * 100)"
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:color="scope.row.contribution > 0 ? '#67C23A' : '#F56C6C'"
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></el-progress>
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</template>
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</el-table-column>
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<el-table-column prop="impact" label="影响">
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<template slot-scope="scope">
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<span :class="scope.row.contribution > 0 ? 'positive' : 'negative'">
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{{ scope.row.contribution > 0 ? '增加' : '减少' }}
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</span>
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</template>
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</el-table-column>
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</el-table>
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</div>
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<!-- 异常值提示 -->
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<div v-if="explanation.anomalies && explanation.anomalies.length > 0" class="anomalies">
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<h4>异常值提示</h4>
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<el-alert
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v-for="(anomaly, index) in explanation.anomalies"
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:key="index"
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:title="`第${anomaly.day + 1}天: ${anomaly.value} (${anomaly.severity}级异常)`"
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:type="anomaly.severity === 'high' ? 'error' : 'warning'"
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:description="anomaly.explanation"
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show-icon
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></el-alert>
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</div>
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<!-- 相似历史模式 -->
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<div v-if="explanation.similar_patterns && explanation.similar_patterns.length > 0" class="similar-patterns">
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<h4>相似历史模式</h4>
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<el-collapse>
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<el-collapse-item
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v-for="(pattern, index) in explanation.similar_patterns"
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:key="index"
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:title="`相似模式 #${index + 1} (相似度: ${(pattern.similarity * 100).toFixed(1)}%)`"
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>
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<div class="pattern-chart">
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<line-chart :chart-data="prepareChartData(pattern.data)"></line-chart>
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</div>
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<div class="pattern-period">
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<p>历史时段: {{ formatDateRange(pattern.period) }}</p>
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</div>
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</el-collapse-item>
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</el-collapse>
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</div>
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</el-card>
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</div>
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</template>
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```
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## 三、预测解释生成器实现 🧩
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### 1. 解释文本生成器
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```python
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def generate_explanation_summary(predictions, explanation_data):
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"""
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根据预测结果和解释数据生成人类可读的解释摘要
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Args:
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predictions: 预测结果序列
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explanation_data: 包含各种解释数据的字典
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Returns:
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summary: 人类可读的解释摘要
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"""
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summary = []
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# 分析预测趋势
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trend = analyze_trend(predictions)
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if trend == 'increasing':
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summary.append("预测显示未来销量呈上升趋势。")
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elif trend == 'decreasing':
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summary.append("预测显示未来销量呈下降趋势。")
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else:
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summary.append("预测显示未来销量相对稳定。")
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# 添加关键影响因素解释
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if 'factors' in explanation_data:
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top_factors = explanation_data['factors'][:3] # 取前三个最重要的因素
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factor_text = "主要影响因素包括: "
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for i, (factor, data) in enumerate(top_factors):
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direction = "增加" if data['contribution'] > 0 else "减少"
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if i > 0:
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factor_text += "、"
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factor_text += f"{factor}({direction})"
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summary.append(factor_text)
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# 添加异常值解释
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if 'anomalies' in explanation_data and explanation_data['anomalies']:
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anomaly_count = len(explanation_data['anomalies'])
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summary.append(f"预测中发现{anomaly_count}个异常值，可能需要特别关注。")
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# 添加历史模式参考
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if 'similar_patterns' in explanation_data and explanation_data['similar_patterns']:
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top_pattern = explanation_data['similar_patterns'][0]
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similarity = top_pattern['similarity'] * 100
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summary.append(f"当前预测模式与历史数据中的某些时段有{similarity:.1f}%的相似度，可参考历史表现。")
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# 生成建议
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recommendations = generate_recommendations(predictions, explanation_data)
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if recommendations:
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summary.append("建议: " + recommendations)
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return " ".join(summary)
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```
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### 2. 特定场景解释生成
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```python
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def generate_anomaly_explanation(value, z_score, day_index):
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"""
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为异常值生成解释
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Args:
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value: 异常预测值
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z_score: Z分数(标准差倍数)
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day_index: 异常值所在的天数索引
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Returns:
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explanation: 异常值解释文本
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"""
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weekday = (datetime.now() + timedelta(days=day_index)).strftime("%A")
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if z_score > 0:
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if weekday in ['Saturday', 'Sunday']:
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return f"该值异常偏高，可能与周末效应有关。周末通常客流量增加，销量上升。"
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else:
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return f"该值异常偏高，可能受到促销活动、季节性因素或特殊事件影响。"
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else:
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if weekday in ['Saturday', 'Sunday']:
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return f"该值在周末异常偏低，与典型周末销售模式不符，可能有特殊原因导致客流减少。"
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else:
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return f"该值异常偏低，可能受到库存问题、竞争对手活动或外部因素影响。"
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```
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### 3. 建议生成器
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```python
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def generate_recommendations(predictions, explanation_data):
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"""
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基于预测结果和解释数据生成行动建议
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Args:
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predictions: 预测结果序列
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explanation_data: 解释数据字典
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Returns:
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recommendations: 建议文本
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"""
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recommendations = []
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# 分析预测趋势
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trend = analyze_trend(predictions)
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# 基于趋势的库存建议
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if trend == 'increasing':
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recommendations.append("考虑增加采购量，确保库存充足")
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elif trend == 'decreasing':
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recommendations.append("考虑减少采购，避免库存积压")
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# 基于异常值的建议
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if 'anomalies' in explanation_data and explanation_data['anomalies']:
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high_anomalies = [a for a in explanation_data['anomalies'] if a['severity'] == 'high']
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if high_anomalies:
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recommendations.append("关注高度异常的预测日期，制定应对预案")
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# 基于特征重要性的建议
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if 'factors' in explanation_data:
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weather_factors = [f for f, _ in explanation_data['factors'] if 'temperature' in f or 'weather' in f]
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if weather_factors:
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recommendations.append("密切关注天气变化对销量的影响")
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promotion_factors = [f for f, _ in explanation_data['factors'] if 'promotion' in f]
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if promotion_factors:
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recommendations.append("促销活动对销量影响显著，可考虑优化促销策略")
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return "，".join(recommendations) + "。" if recommendations else ""
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```
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## 四、实施与集成方案 🔧
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### 1. 系统集成步骤
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1. **后端API实现**:
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- 在`pharmacy_predictor.py`中添加预测解释相关函数
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- 在`api.py`中添加新的API端点
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2. **数据存储扩展**:
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- 添加特征重要性存储
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- 保存历史预测与实际销量对比数据
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3. **前端集成**:
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- 在`UI/src/views/PredictionView.vue`中添加解释面板组件
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- 添加可视化图表展示影响因素
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### 2. 代码实现示例
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#### A. 后端实现
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```python
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# 在pharmacy_predictor.py中添加
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def explain_model_prediction(product_id, model_type, predictions, features):
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"""
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为模型预测结果提供解释
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"""
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# 加载模型
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model_path = get_model_path(product_id, model_type)
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model = torch.load(model_path)
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# 获取特征重要性
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feature_names = ['sales', 'price', 'weekday', 'month', 'is_holiday',
|
||
'is_weekend', 'is_promotion', 'temperature']
|
||
feature_importance = analyze_feature_importance(model, features, feature_names)
|
||
|
||
# 获取历史数据
|
||
historical_data = load_historical_sales_data(product_id)
|
||
|
||
# 生成解释
|
||
explanation = {
|
||
'feature_importance': feature_importance,
|
||
'similar_patterns': find_similar_historical_patterns(predictions, historical_data),
|
||
'anomalies': detect_and_explain_anomalies(predictions, calculate_historical_stats(historical_data))
|
||
}
|
||
|
||
# 生成总结
|
||
explanation['summary'] = generate_explanation_summary(predictions, explanation)
|
||
|
||
return explanation
|
||
```
|
||
|
||
#### B. API端点实现
|
||
```python
|
||
# 在api.py中添加
|
||
|
||
@app.route('/api/prediction/explain', methods=['POST'])
|
||
def explain_prediction():
|
||
"""
|
||
提供预测结果的详细解释
|
||
"""
|
||
try:
|
||
data = request.json
|
||
product_id = data.get('product_id')
|
||
model_type = data.get('model_type')
|
||
future_days = data.get('future_days', 7)
|
||
|
||
# 验证参数
|
||
if not product_id or not model_type:
|
||
return jsonify({"status": "error", "error": "缺少必要参数"}), 400
|
||
|
||
# 获取预测结果和特征
|
||
predictions, features = load_model_and_predict_with_features(
|
||
product_id, model_type, future_days
|
||
)
|
||
|
||
# 生成解释
|
||
explanation = explain_model_prediction(
|
||
product_id, model_type, predictions, features
|
||
)
|
||
|
||
return jsonify({
|
||
"status": "success",
|
||
"product_id": product_id,
|
||
"model_type": model_type,
|
||
"predictions": predictions.tolist(),
|
||
"explanation": explanation
|
||
})
|
||
|
||
except Exception as e:
|
||
return jsonify({"status": "error", "error": str(e)}), 500
|
||
```
|
||
|
||
### 3. UI实现示例
|
||
```vue
|
||
<!-- 在PredictionView.vue中添加 -->
|
||
<template>
|
||
<div>
|
||
<!-- 现有预测结果展示 -->
|
||
<div class="prediction-results">
|
||
<!-- ... 现有代码 ... -->
|
||
</div>
|
||
|
||
<!-- 新增预测解释面板 -->
|
||
<el-collapse v-if="predictionExplanation">
|
||
<el-collapse-item title="预测结果解释" name="explanation">
|
||
<div class="explanation-summary">
|
||
<el-alert
|
||
:title="predictionExplanation.summary"
|
||
type="info"
|
||
:closable="false"
|
||
show-icon
|
||
></el-alert>
|
||
</div>
|
||
|
||
<!-- 关键影响因素 -->
|
||
<div class="key-factors-section">
|
||
<h4>关键影响因素</h4>
|
||
<el-table
|
||
:data="keyFactors"
|
||
style="width: 100%"
|
||
>
|
||
<el-table-column prop="factor" label="因素名称"></el-table-column>
|
||
<el-table-column prop="importance" label="重要性">
|
||
<template slot-scope="scope">
|
||
<el-progress :percentage="scope.row.importance * 100"></el-progress>
|
||
</template>
|
||
</el-table-column>
|
||
<el-table-column prop="impact" label="影响方向">
|
||
<template slot-scope="scope">
|
||
<span :class="scope.row.impact > 0 ? 'positive-impact' : 'negative-impact'">
|
||
{{ scope.row.impact > 0 ? '正面影响' : '负面影响' }}
|
||
</span>
|
||
</template>
|
||
</el-table-column>
|
||
</el-table>
|
||
</div>
|
||
|
||
<!-- 异常值检测 -->
|
||
<div v-if="predictionExplanation.anomalies && predictionExplanation.anomalies.length > 0" class="anomalies-section">
|
||
<h4>异常值检测</h4>
|
||
<el-card v-for="(anomaly, index) in predictionExplanation.anomalies" :key="index" class="anomaly-card">
|
||
<div slot="header">
|
||
<span>第{{ anomaly.day + 1 }}天异常: {{ anomaly.value }}</span>
|
||
</div>
|
||
<p>{{ anomaly.explanation }}</p>
|
||
</el-card>
|
||
</div>
|
||
|
||
<!-- 相似历史模式 -->
|
||
<div v-if="predictionExplanation.similar_patterns && predictionExplanation.similar_patterns.length > 0" class="similar-patterns-section">
|
||
<h4>相似历史模式</h4>
|
||
<el-tabs>
|
||
<el-tab-pane
|
||
v-for="(pattern, index) in predictionExplanation.similar_patterns.slice(0, 3)"
|
||
:key="index"
|
||
:label="`模式 ${index + 1} (相似度: ${(pattern.similarity * 100).toFixed(1)}%)`"
|
||
>
|
||
<div class="pattern-chart">
|
||
<line-chart :chart-data="getPatternChartData(pattern)"></line-chart>
|
||
</div>
|
||
</el-tab-pane>
|
||
</el-tabs>
|
||
</div>
|
||
</el-collapse-item>
|
||
</el-collapse>
|
||
</div>
|
||
</template>
|
||
|
||
<script>
|
||
export default {
|
||
// ... 现有代码 ...
|
||
|
||
data() {
|
||
return {
|
||
// ... 现有数据 ...
|
||
predictionExplanation: null,
|
||
keyFactors: []
|
||
};
|
||
},
|
||
|
||
methods: {
|
||
// ... 现有方法 ...
|
||
|
||
async getPrediction() {
|
||
// 获取预测结果
|
||
const response = await this.fetchPrediction();
|
||
|
||
// 获取预测解释
|
||
this.fetchPredictionExplanation();
|
||
},
|
||
|
||
async fetchPredictionExplanation() {
|
||
try {
|
||
const response = await fetch('/api/prediction/explain', {
|
||
method: 'POST',
|
||
headers: {
|
||
'Content-Type': 'application/json'
|
||
},
|
||
body: JSON.stringify({
|
||
product_id: this.form.product_id,
|
||
model_type: this.form.model_type,
|
||
future_days: this.form.future_days
|
||
})
|
||
});
|
||
|
||
const data = await response.json();
|
||
|
||
if (data.status === 'success') {
|
||
this.predictionExplanation = data.explanation;
|
||
this.keyFactors = this.formatKeyFactors(data.explanation.feature_importance);
|
||
}
|
||
} catch (error) {
|
||
console.error('获取预测解释失败:', error);
|
||
}
|
||
},
|
||
|
||
formatKeyFactors(featureImportance) {
|
||
// 将特征重要性数据转换为表格数据
|
||
return Object.entries(featureImportance)
|
||
.map(([factor, value]) => ({
|
||
factor,
|
||
importance: Math.abs(value),
|
||
impact: value
|
||
}))
|
||
.sort((a, b) => b.importance - a.importance)
|
||
.slice(0, 5); // 只显示前5个最重要的因素
|
||
},
|
||
|
||
getPatternChartData(pattern) {
|
||
// 准备图表数据
|
||
return {
|
||
labels: Array.from({length: pattern.data.length}, (_, i) => `Day ${i+1}`),
|
||
datasets: [{
|
||
label: '历史销量',
|
||
data: pattern.data,
|
||
borderColor: '#409EFF',
|
||
fill: false
|
||
}]
|
||
};
|
||
}
|
||
}
|
||
};
|
||
</script>
|
||
```
|
||
|
||
## 五、应用场景与优势 🌟
|
||
|
||
### 1. 应用场景
|
||
- **库存管理决策支持**：解释预测结果背后的原因，帮助制定更精准的库存计划
|
||
- **销售策略优化**：理解影响销量的关键因素，优化促销和定价策略
|
||
- **异常情况预警**：提前识别可能的销售异常，制定应对措施
|
||
- **多模型比较分析**：解释不同模型预测结果的差异原因，选择最适合的模型
|
||
|
||
### 2. 系统优势
|
||
- **提高可解释性**：将"黑盒"模型转变为可理解的决策支持工具
|
||
- **增强用户信任**：通过解释预测背后的逻辑，增强用户对预测结果的信任
|
||
- **辅助决策制定**：提供数据支持的建议，帮助用户做出更明智的决策
|
||
- **持续学习优化**：通过解释系统，识别模型的不足和改进方向
|
||
|
||
### 3. 未来扩展方向
|
||
- **交互式解释**：允许用户通过调整输入参数，实时观察对预测结果的影响
|
||
- **场景模拟**：基于不同假设条件(如天气变化、促销力度)模拟预测结果
|
||
- **自动化建议系统**：根据预测解释自动生成库存管理和销售策略建议
|
||
- **多维度可视化**：提供更丰富的可视化工具，展示预测结果与各因素的关系
|
||
</rewritten_file> |