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+import pandas as pd
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+import matplotlib.pyplot as plt
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+from pymongo import MongoClient
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+import pickle
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+import numpy as np
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+import plotly.express as px
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+from plotly.subplots import make_subplots
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+import plotly.graph_objects as go
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+from flask import Flask,request,jsonify
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+from waitress import serve
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+import time
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+import random
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+import argparse
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+import logging
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+import traceback
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+import os
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+
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+app = Flask('analysis_report——service')
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+def get_data_from_mongo(args):
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+ # 1.读数据
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+ mongodb_connection,mongodb_database,all_table,accuracy_table,model_table,model_name = "mongodb://root:sdhjfREWFWEF23e@192.168.1.43:30000/",args['mongodb_database'],args['train_table'],args['accuracy_table'],args['model_table'],args['model_name']
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+ client = MongoClient(mongodb_connection)
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+ # 选择数据库（如果数据库不存在，MongoDB 会自动创建）
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+ db = client[mongodb_database]
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+ # 将游标转换为列表，并创建 pandas DataFrame
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+ df_all = pd.DataFrame(db[all_table].find({}, {'_id': 0}))
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+
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+ df_accuracy = pd.DataFrame(db[accuracy_table].find({}, {'_id': 0}))
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+
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+ model_data = db[model_table].find_one({"model_name": model_name})
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+ if model_data is not None:
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+ model_binary = model_data['model'] # 确保这个字段是存储模型的二进制数据
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+ # 反序列化模型
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+ model = pickle.loads(model_binary)
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+ client.close()
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+ return df_all,df_accuracy,model
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+
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+
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+def draw_info(df_all,df_accuracy,model,features,args):
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+ #1.数据描述 数据描述：
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+ col_time = args['col_time']
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+ label = args['label']
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+ df_accuracy_beginTime = df_accuracy[col_time].min()
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+ df_accuracy_endTime = df_accuracy[col_time].max()
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+ df_train = df_all[df_all[col_time]<df_accuracy_beginTime][features+[col_time,label]]
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+ df_train_beginTime = df_train[col_time].min()
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+ df_train_endTime = df_train[col_time].max()
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+ text_content = f"训练数据时间范围：{df_train_beginTime} 至 {df_train_endTime}，共{df_train.shape[0]}条记录，测试集数据时间范围：{df_accuracy_beginTime} 至 {df_accuracy_endTime}。<br>lightgbm模型参数：{model.params}"
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+ return text_content
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+
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+
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+
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+def draw_global_scatter(df,args):
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+ # --- 1. 实际功率和辐照度的散点图 ---
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+ col_x = args['scatter_col_x']
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+ col_y = args['label']
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+ scatter_fig = px.scatter(
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+ df,
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+ x=col_x,
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+ y=col_y,
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+ title=f"{col_x}和{col_y}的散点图",
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+ labels={"辐照度": "辐照度 (W/m²)", "实际功率": "实际功率 (kW)"}
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+ )
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+ return scatter_fig
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+
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+
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+
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+def draw_corr(df,features,args):
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+
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+ # --- 2. 相关性热力图 ---
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+ # 计算相关性矩阵
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+ label = args['label']
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+ features_coor = features+[label]
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+ corr_matrix = df[features_coor].corr()
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+ # 使用 Plotly Express 绘制热力图
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+ heatmap_fig = px.imshow(corr_matrix,
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+ text_auto=True, # 显示数值
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+ color_continuous_scale='RdBu', # 配色方案
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+ title="Correlation Heatmap")
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+ heatmap_fig.update_coloraxes(showscale=False)
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+
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+ return heatmap_fig
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+
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+def draw_feature_importance(model,features):
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+ # --- 3. 特征重要性排名 ---
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+ # 获取特征重要性
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+ importance = model.feature_importance() # 'split' 或 'gain'，根据需求选择
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+
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+ # 转换为 DataFrame 方便绘图
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+ feature_importance_df = pd.DataFrame({
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+ 'Feature': features,
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+ 'Importance': importance
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+ })
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+ feature_importance_df = feature_importance_df.sort_values(by='Importance', ascending=False)
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+
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+ # 使用 Plotly Express 绘制条形图
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+ importance_fig = px.bar(feature_importance_df, x='Feature', y='Importance',
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+ title="特征重要性排名",
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+ labels={'Feature': '特征', 'Importance': '重要性'},
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+ color='Importance',
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+ color_continuous_scale='Viridis')
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+ # 更新每个 trace，确保没有图例
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+
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+ importance_fig.update_layout(title="模型特征重要性排名",
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+ showlegend=False # 移除图例
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+ )
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+ importance_fig.update_coloraxes(showscale=False)
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+ return importance_fig
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+
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+
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+def draw_data_info_table(content):
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+ # --- 4. 创建数据说明的表格 ---
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+ # 转换为表格格式：1行1列，且填充文字说明
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+ # 转换为表格格式
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+ # 创建一个空的图
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+ table_fig = go.Figure()
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+
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+ # 第一部分: 显示文字说明
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+ table_fig.add_trace(go.Table(
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+ header=dict(
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+ values=["说明"], # 表格只有一列：说明
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+ fill_color="paleturquoise",
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+ align="center"
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+ ),
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+ cells=dict(
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+ values=[[content]] , # 第一行填入文本说明
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+ fill_color="lavender",
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+ align="center"
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+ )
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+ ))
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+
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+
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+ return table_fig
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+
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+
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+
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+def draw_accuracy_table(df,content):
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+
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+ # --- 4. 每日的准确率表格 ---
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+ # 转换为表格格式
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+ table_fig = go.Figure(
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+ data=[
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+ go.Table(
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+ header=dict(
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+ values=list(df.columns),
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+ fill_color="paleturquoise",
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+ align="center"
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+ ),
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+ cells=dict(
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+ values=[df[col] for col in df.columns],
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+ fill_color="lavender",
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+ align="center"
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+ )
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+ )
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+ ]
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+ )
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+ table_fig.update_layout(title="准确率表", showlegend=False)
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+ return table_fig
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+
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+
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+@app.route('/analysis_report', methods=['POST'])
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+def analysis_report():
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+ start_time = time.time()
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+ result = {}
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+ success = 0
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+ path = ""
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+ print("Program starts execution!")
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+ try:
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+ args = request.values.to_dict()
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+ print('args',args)
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+ logger.info(args)
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+ #获取数据
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+ df_all, df_accuracy, model = get_data_from_mongo(args)
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+ features = model.feature_name()
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+ text_content = draw_info(df_all,df_accuracy,model,features,args)
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+ text_fig,scatter_fig,heatmap_fig,importance_fig,table_fig=draw_data_info_table(text_content),draw_global_scatter(df_all,args),draw_corr(df_all,features,args),draw_feature_importance(model,features),\
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+ draw_accuracy_table(df_accuracy,text_content)
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+ # --- 合并图表并保存到一个 HTML 文件 ---
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+ # 创建子图布局
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+ combined_fig = make_subplots(
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+ rows=5, cols=1,
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+ subplot_titles=["数据-模型概览","辐照度和实际功率的散点图", "相关性","特征重要性排名", "准确率表"],
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+ row_heights=[0.3, 0.6, 0.6, 0.6, 0.4],
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+ specs=[[{"type": "table"}], [{"type": "xy"}], [{"type": "heatmap"}], [{"type": "xy"}],[{"type": "table"}]] # 指定每个子图类型
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+ )
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+ # 添加文本信息到子图（第一行）
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+ # 添加文字说明
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+ for trace in text_fig.data:
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+ combined_fig.add_trace(trace, row=1, col=1)
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+
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+ # 添加散点图
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+ for trace in scatter_fig.data:
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+ combined_fig.add_trace(trace, row=2, col=1)
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+
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+ # 添加相关性热力图
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+ for trace in heatmap_fig.data:
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+ combined_fig.add_trace(trace, row=3, col=1)
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+
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+ # 添加特征重要性排名图
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+ for trace in importance_fig.data:
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+ combined_fig.add_trace(trace, row=4, col=1)
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+
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+ # 添加表格
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+ for trace in table_fig.data:
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+ combined_fig.add_trace(trace, row=5, col=1)
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+
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+ # 更新布局
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+ combined_fig.update_layout(
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+ height=1500,
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+ title_text="分析结果汇总", # 添加换行符以适应文本内容
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+ title_x=0.5, # 中心对齐标题
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+ showlegend=False,
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+ )
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+ combined_fig.update_coloraxes(showscale=False)
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+ filename = f"{int(time.time() * 1000)}_{random.randint(1000, 9999)}.html"
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+ # 保存为 HTML
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+ directory = '/usr/share/nginx/html'
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+ if not os.path.exists(directory):
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+ os.makedirs(directory)
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+ file_path = os.path.join(directory, filename)
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+ # combined_fig.write_html(f"D://usr//{filename}")
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+ combined_fig.write_html(file_path)
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+ path = f"交互式 HTML 文件已生成！路径/data/html/{filename}"
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+ success = 1
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+ except Exception as e:
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+ my_exception = traceback.format_exc()
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+ my_exception.replace("\n","\t")
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+ result['msg'] = my_exception
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+ end_time = time.time()
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+ result['success'] = success
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+ result['args'] = args
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+ result['start_time'] = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(start_time))
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+ result['end_time'] = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(end_time))
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+ result['file_path'] = path
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+ print("Program execution ends!")
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+ return result
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+
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+
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+if __name__=="__main__":
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+ print("Program starts execution!")
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+ logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
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+ logger = logging.getLogger("analysis_report log")
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+ from waitress import serve
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+ serve(app, host="0.0.0.0", port=10092)
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+ print("server start!")
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