普货报告
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@ -1,9 +1,9 @@
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# 读取配置
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from lib.dataread import *
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from config_shiyoujiao_lvyong import *
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from config_shiyoujiao_puhuo import *
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from lib.tools import SendMail, exception_logger
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from models.nerulforcastmodels import ex_Model, model_losss, shiyoujiao_lvyong_export_pdf
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from models.nerulforcastmodels import ex_Model, model_losss, shiyoujiao_puhuo_export_pdf
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import datetime
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import torch
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torch.set_float32_matmul_precision("high")
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@ -425,8 +425,8 @@ def predict_main():
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reportname = '石油焦大模型普货渠道.pdf' # 报告文件名
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# reportname = f'石油焦大模型普货日度预测--{end_time}.pdf' # 报告文件名
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# reportname = reportname.replace(':', '-') # 替换冒号
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shiyoujiao_lvyong_export_pdf(dataset=dataset, num_models=5 if is_fivemodels else 22, time=end_time,
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reportname=reportname, sqlitedb=sqlitedb),
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shiyoujiao_puhuo_export_pdf(dataset=dataset, num_models=5 if is_fivemodels else 22, time=end_time,
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reportname=reportname, sqlitedb=sqlitedb),
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logger.info('制作报告end')
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logger.info('模型训练完成')
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@ -2807,6 +2807,325 @@ def shiyoujiao_lvyong_export_pdf(num_indicators=475, num_models=21, num_dayindic
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print(f"请求超时: {e}")
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@exception_logger
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def shiyoujiao_puhuo_export_pdf(num_indicators=475, num_models=21, num_dayindicator=202, inputsize=5, dataset='dataset', time='2024-07-30', reportname='report.pdf', sqlitedb='jbsh_yuanyou.db'):
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global y
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# 创建内容对应的空列表
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content = list()
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# 获取特征的近一月值
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import pandas as pd
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feature_data_df = pd.read_csv(os.path.join(
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config.dataset, '指标数据添加时间特征.csv'), parse_dates=['ds']).tail(60)
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def draw_feature_trend(feature_data_df, features):
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# 画特征近60天的趋势图
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feature_df = feature_data_df[['ds', 'y']+features]
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# 遍历X每一列，和yy画散点图 ，
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for i, col in enumerate(features):
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# try:
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print(f'正在绘制第{i+1}个特征{col}与价格散点图...')
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if col not in ['ds', 'y']:
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fig, ax1 = plt.subplots(figsize=(10, 6))
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# 在第一个坐标轴上绘制数据
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sns.lineplot(data=feature_df, x='ds', y='y', ax=ax1, color='b')
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ax1.set_xlabel('日期')
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ax1.set_ylabel('y', color='b')
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ax1.tick_params('y', colors='b')
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# 在 ax1 上添加文本显示值，添加一定的偏移避免值与曲线重叠
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for j in range(1, len(feature_df), 2):
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value = feature_df['y'].iloc[j]
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date = feature_df['ds'].iloc[j]
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offset = 1.001
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ax1.text(date, value * offset, str(round(value, 2)),
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ha='center', va='bottom', color='b', fontsize=10)
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# 创建第二个坐标轴
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ax2 = ax1.twinx()
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# 在第二个坐标轴上绘制数据
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sns.lineplot(data=feature_df, x='ds', y=col, ax=ax2, color='r')
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ax2.set_ylabel(col, color='r')
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ax2.tick_params('y', colors='r')
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# 在 ax2 上添加文本显示值，添加一定的偏移避免值与曲线重叠
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for j in range(0, len(feature_df), 2):
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value = feature_df[col].iloc[j]
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date = feature_df['ds'].iloc[j]
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offset = 1.0003
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ax2.text(date, value * offset, str(round(value, 2)),
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ha='center', va='bottom', color='r', fontsize=10)
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# 添加标题
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plt.title(col)
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# 设置横坐标为日期格式并自动调整
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locator = mdates.AutoDateLocator()
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formatter = mdates.AutoDateFormatter(locator)
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ax1.xaxis.set_major_locator(locator)
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ax1.xaxis.set_major_formatter(formatter)
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# 文件名特殊字符处理
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col = col.replace('*', '-')
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col = col.replace(':', '-')
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col = col.replace(r'/', '-')
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col = col.replace(r'>', '-')
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col = col.replace(r'<', '-')
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plt.savefig(os.path.join(config.dataset, f'{col}与价格散点图.png'))
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content.append(Graphs.draw_img(
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os.path.join(config.dataset, f'{col}与价格散点图.png')))
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plt.close()
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# except Exception as e:
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# print(f'绘制第{i+1}个特征{col}与价格散点图时出错：{e}')
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# 添加标题
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content.append(Graphs.draw_title('石油焦铝用渠道'))
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# 预测结果
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content.append(Graphs.draw_little_title('一、预测结果：'))
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# 添加历史走势及预测价格的走势图片
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content.append(Graphs.draw_img(
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os.path.join(config.dataset, '历史价格-预测值.png')))
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# 波动率画图逻辑
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content.append(Graphs.draw_text('图示说明：'))
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content.append(Graphs.draw_text(
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' 确定置信区间：设置残差置信阈值，以每周最佳模型为基准，选取在置信区间的预测值作为置信区间;'))
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# 取df中y列为空的行
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import pandas as pd
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df = pd.read_csv(os.path.join(
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config.dataset, 'predict.csv'), encoding='gbk')
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df_true = pd.read_csv(os.path.join(
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config.dataset, '指标数据添加时间特征.csv'), encoding='utf-8') # 获取预测日期对应的真实值
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df_true = df_true[['ds', 'y']]
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eval_df = pd.read_csv(os.path.join(
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config.dataset, 'model_evaluation.csv'), encoding='utf-8')
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# 按评估指标排序，取前五
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fivemodels_list = eval_df['模型(Model)'].values # 列表形式，后面当作列名索引使用
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# 取 fivemodels_list 和 ds 列
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df = df[['ds'] + fivemodels_list.tolist()]
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# 拼接预测日期对应的真实值
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df = pd.merge(df, df_true, on='ds', how='left')
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# 删除全部为nan的列
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df = df.dropna(how='all', axis=1)
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# 选择除 'ds' 列外的数值列，并进行类型转换和四舍五入
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num_cols = [col for col in df.columns if col !=
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'ds' and pd.api.types.is_numeric_dtype(df[col])]
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for col in num_cols:
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df[col] = df[col].astype(float).round(2)
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# 添加最大值、最小值、平均值三列
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df['平均值'] = df[num_cols].mean(axis=1).round(2)
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df['最大值'] = df[num_cols].max(axis=1)
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df['最小值'] = df[num_cols].min(axis=1)
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# df转置
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df = df.T
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# df重置索引
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df = df.reset_index()
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# 添加预测值表格
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data = df.values.tolist()
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col_width = 500/len(df.columns)
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content.append(Graphs.draw_table(col_width, *data))
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content.append(Graphs.draw_little_title('二、上一预测周期偏差率分析：'))
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df = pd.read_csv(os.path.join(
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config.dataset, 'testandpredict_groupby.csv'), encoding='utf-8')
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df4 = df.copy() # 计算偏差率使用
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# 去掉created_dt 列
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df4 = df4.drop(columns=['created_dt'])
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# 计算模型偏差率
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# 计算各列对于y列的差值百分比
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df3 = pd.DataFrame() # 存储偏差率
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# 删除有null的行
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df4 = df4.dropna()
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df3['ds'] = df4['ds']
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for col in fivemodels_list:
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df3[col] = round(abs(df4[col] - df4['y']) / df4['y'] * 100, 2)
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# 找出决定系数前五的偏差率
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df3 = df3[['ds']+fivemodels_list.tolist()][-inputsize:]
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# 找出上一预测区间的时间
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stime = df3['ds'].iloc[0]
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etime = df3['ds'].iloc[-1]
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# 添加偏差率表格
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fivemodels = '、'.join(eval_df['模型(Model)'].values[:5]) # 字符串形式，后面写入字符串使用
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content.append(Graphs.draw_text(
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f'预测使用了{num_models}个模型进行训练，使用评估结果MAE前五的模型分别是 {fivemodels} ，模型上一预测区间 {stime} -- {etime}的偏差率(%)分别是：'))
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# # 添加偏差率表格
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df3 = df3.T
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df3 = df3.reset_index()
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data = df3.values.tolist()
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col_width = 500/len(df3.columns)
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content.append(Graphs.draw_table(col_width, *data))
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content.append(Graphs.draw_little_title('上一周预测准确率：'))
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df4 = sqlitedb.select_data('accuracy_rote', order_by='结束日期 desc', limit=1)
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df4 = df4.T
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df4 = df4.reset_index()
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df4 = df4.T
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data = df4.values.tolist()
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col_width = 500/len(df4.columns)
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content.append(Graphs.draw_table(col_width, *data))
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content.append(Graphs.draw_little_title('三、预测过程解析：'))
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# 特征、模型、参数配置
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content.append(Graphs.draw_little_title('模型选择：'))
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content.append(Graphs.draw_text(
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f'本次预测使用了一个专门收集时间序列的NeuralForecast库中的{num_models}个模型：'))
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content.append(Graphs.draw_text(f'使用40天的数据预测未来{inputsize}天的数据。'))
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content.append(Graphs.draw_little_title('指标情况：'))
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with open(os.path.join(config.dataset, '特征频度统计.txt'), encoding='utf-8') as f:
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for line in f.readlines():
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content.append(Graphs.draw_text(line))
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data = pd.read_csv(os.path.join(config.dataset, '指标数据添加时间特征.csv'),
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encoding='utf-8') # 计算相关系数用
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df_zhibiaofenlei = loadcsv(os.path.join(
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config.dataset, '特征处理后的指标名称及分类.csv')) # 气泡图用
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df_zhibiaoshuju = data.copy() # 气泡图用
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# 绘制特征相关气泡图
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grouped = df_zhibiaofenlei.groupby('指标分类')
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grouped_corr = pd.DataFrame(columns=['指标分类', '指标数量', '相关性总和'])
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content.append(Graphs.draw_little_title('按指标分类分别与预测目标进行皮尔逊相关系数分析：'))
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content.append(Graphs.draw_text('''皮尔逊相关系数说明：'''))
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content.append(Graphs.draw_text('''衡量两个特征之间的线性相关性。'''))
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content.append(Graphs.draw_text('''
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相关系数为1：表示两个变量之间存在完全正向的线性关系，即当一个变量增加时，另一个变量也相应增加，且变化是完全一致的。'''))
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content.append(Graphs.draw_text(
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'''相关系数为-1：表示两个变量之间存在完全负向的线性关系，即当一个变量增加时，另一个变量会相应减少，且变化是完全相反的'''))
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content.append(Graphs.draw_text(
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'''相关系数接近0：表示两个变量之间不存在线性关系，即它们的变化不会随着对方的变化而变化。'''))
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for name, group in grouped:
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cols = group['指标名称'].tolist()
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config.logger.info(f'开始绘制{name}类指标的相关性直方图')
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cols_subset = cols
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feature_names = ['y'] + cols_subset
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correlation_matrix = df_zhibiaoshuju[feature_names].corr()['y']
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# 绘制特征相关性直方分布图
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plt.figure(figsize=(10, 8))
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sns.histplot(correlation_matrix.values.flatten(),
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bins=20, kde=True, color='skyblue')
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plt.title(f'{name}类指标(共{len(cols_subset)}个)相关性直方分布图')
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plt.xlabel('相关系数')
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plt.ylabel('频数')
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plt.savefig(os.path.join(
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config.dataset, f'{name}类指标相关性直方分布图.png'), bbox_inches='tight')
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plt.close()
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content.append(Graphs.draw_img(
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os.path.join(config.dataset, f'{name}类指标相关性直方分布图.png')))
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content.append(Graphs.draw_text(
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f'{name}类指标(共{len(cols_subset)}个)的相关性直方分布图如上所示。'))
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# 相关性大于0的特征
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positive_corr_features = correlation_matrix[correlation_matrix > 0].sort_values(
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ascending=False).index.tolist()[1:]
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print(f'{name}下正相关的特征值有：', positive_corr_features)
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if len(positive_corr_features) > 5:
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positive_corr_features = positive_corr_features[0:5]
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content.append(Graphs.draw_text(
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f'{name}类指标中，与预测目标y正相关前五的特征有：{positive_corr_features}'))
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draw_feature_trend(feature_data_df, positive_corr_features)
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elif len(positive_corr_features) == 0:
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pass
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else:
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positive_corr_features = positive_corr_features
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content.append(Graphs.draw_text(
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f'其中，与预测目标y正相关的特征有：{positive_corr_features}'))
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draw_feature_trend(feature_data_df, positive_corr_features)
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# 相关性小于0的特征
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negative_corr_features = correlation_matrix[correlation_matrix < 0].sort_values(
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ascending=True).index.tolist()
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print(f'{name}下负相关的特征值有：', negative_corr_features)
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if len(negative_corr_features) > 5:
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negative_corr_features = negative_corr_features[:5]
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content.append(Graphs.draw_text(
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f'与预测目标y负相关前五的特征有：{negative_corr_features}'))
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draw_feature_trend(feature_data_df, negative_corr_features)
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elif len(negative_corr_features) == 0:
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pass
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else:
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content.append(Graphs.draw_text(
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f'{name}类指标中，与预测目标y负相关的特征有：{negative_corr_features}'))
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draw_feature_trend(feature_data_df, negative_corr_features)
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# 计算correlation_sum 第一行的相关性的绝对值的总和
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correlation_sum = correlation_matrix.abs().sum()
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config.logger.info(f'{name}类指标的相关性总和为：{correlation_sum}')
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# 分组的相关性总和拼接到grouped_corr
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goup_corr = pd.DataFrame(
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{'指标分类': [name], '指标数量': [len(cols_subset)], '相关性总和': [correlation_sum]})
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grouped_corr = pd.concat(
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[grouped_corr, goup_corr], axis=0, ignore_index=True)
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# 绘制相关性总和的气泡图
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config.logger.info(f'开始绘制相关性总和的气泡图')
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plt.figure(figsize=(10, 10))
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sns.scatterplot(data=grouped_corr, x='相关性总和', y='指标数量', size='相关性总和', sizes=(
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grouped_corr['相关性总和'].min()*5, grouped_corr['相关性总和'].max()*5), hue='指标分类', palette='viridis')
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plt.title('指标分类相关性总和的气泡图')
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plt.ylabel('数量')
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plt.savefig(os.path.join(config.dataset, '指标分类相关性总和的气泡图.png'),
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bbox_inches='tight')
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plt.close()
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content.append(Graphs.draw_img(os.path.join(
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config.dataset, '指标分类相关性总和的气泡图.png')))
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content.append(Graphs.draw_text(
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'气泡图中，横轴为指标分类，纵轴为指标分类下的特征数量，气泡的面积越大表示该分类中特征的相关系数和越大。'))
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config.logger.info(f'绘制相关性总和的气泡图结束')
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content.append(Graphs.draw_little_title('模型选择：'))
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content.append(Graphs.draw_text(
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f'预测使用了{num_models}个模型进行训练拟合，通过评估指标MAE从小到大排列，前5个模型的简介如下：'))
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# 读取模型简介
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with open(os.path.join(config.dataset, 'model_introduction.txt'), 'r', encoding='utf-8') as f:
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for line in f:
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line_split = line.strip().split('--')
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if line_split[0] in fivemodels_list:
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for introduction in line_split:
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content.append(Graphs.draw_text(introduction))
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content.append(Graphs.draw_little_title('模型评估：'))
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df = pd.read_csv(os.path.join(
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config.dataset, 'model_evaluation.csv'), encoding='utf-8')
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# 判断 df 的数值列转为float
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for col in eval_df.columns:
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if col not in ['模型(Model)']:
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eval_df[col] = eval_df[col].astype(float)
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eval_df[col] = eval_df[col].round(3)
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# 筛选 fivemodels_list.tolist() 的行
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eval_df = eval_df[eval_df['模型(Model)'].isin(fivemodels_list)]
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# df转置
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eval_df = eval_df.T
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# df重置索引
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eval_df = eval_df.reset_index()
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eval_df = eval_df.T
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# # 添加表格
|
||||
data = eval_df.values.tolist()
|
||||
col_width = 500/len(eval_df.columns)
|
||||
content.append(Graphs.draw_table(col_width, *data))
|
||||
content.append(Graphs.draw_text('评估指标释义：'))
|
||||
content.append(Graphs.draw_text(
|
||||
'1. 均方根误差(RMSE)：均方根误差是衡量预测值与实际值之间误差的一种方法，取值越小，误差越小，预测效果越好。'))
|
||||
content.append(Graphs.draw_text(
|
||||
'2. 平均绝对误差(MAE)：平均绝对误差是衡量预测值与实际值之间误差的一种方法，取值越小，误差越小，预测效果越好。'))
|
||||
content.append(Graphs.draw_text(
|
||||
'3. 平均平方误差(MSE)：平均平方误差是衡量预测值与实际值之间误差的一种方法，取值越小，误差越小，预测效果越好。'))
|
||||
content.append(Graphs.draw_text('模型拟合：'))
|
||||
# 添加图片
|
||||
content.append(Graphs.draw_img(
|
||||
os.path.join(config.dataset, '预测值与真实值对比图.png')))
|
||||
# 生成pdf文件
|
||||
doc = SimpleDocTemplate(os.path.join(
|
||||
config.dataset, reportname), pagesize=letter)
|
||||
doc.build(content)
|
||||
# pdf 上传到数字化信息平台
|
||||
try:
|
||||
if config.is_update_report:
|
||||
with open(os.path.join(config.dataset, reportname), 'rb') as f:
|
||||
base64_data = base64.b64encode(f.read()).decode('utf-8')
|
||||
upload_data["data"]["fileBase64"] = base64_data
|
||||
upload_data["data"]["fileName"] = reportname
|
||||
token = get_head_auth_report()
|
||||
upload_report_data(token, upload_data)
|
||||
except TimeoutError as e:
|
||||
print(f"请求超时: {e}")
|
||||
|
||||
|
||||
@exception_logger
|
||||
def pp_export_pdf(num_indicators=475, num_models=21, num_dayindicator=202, inputsize=5, dataset='dataset', time='2024-07-30', reportname='report.pdf', sqlitedb='jbsh_yuanyou.db'):
|
||||
# 创建内容对应的空列表
|
||||
|
||||
Loading…
Reference in New Issue
Block a user