根据配置选择报告函数名称
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liurui
parent
704e49b29f
commit
8ebbeed7b6
@ -211,7 +211,7 @@ upload_data = {
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### 开关
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is_train = True # 是否训练
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is_debug = False # 是否调试
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is_eta = True # 是否使用eta接口
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is_eta = False # 是否使用eta接口
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is_timefurture = True # 是否使用时间特征
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is_fivemodels = False # 是否使用之前保存的最佳的5个模型
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is_edbcode = False # 特征使用edbcoding列表中的
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52
main.py
52
main.py
@ -106,11 +106,6 @@ def predict_main():
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sqlitedb.create_table('most_model',columns="ds datetime, most_common_model TEXT")
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sqlitedb.insert_data('most_model',(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'),most_common_model,),columns=('ds','most_common_model',))
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if is_corr:
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df = corr_feature(df=df)
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@ -119,25 +114,25 @@ def predict_main():
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row,col = df.shape
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now = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
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ex_Model(df,
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horizon=horizon,
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input_size=input_size,
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train_steps=train_steps,
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val_check_steps=val_check_steps,
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early_stop_patience_steps=early_stop_patience_steps,
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is_debug=is_debug,
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dataset=dataset,
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is_train=is_train,
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is_fivemodels=is_fivemodels,
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val_size=val_size,
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test_size=test_size,
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settings=settings,
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now=now,
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etadata = etadata,
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modelsindex = modelsindex,
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data = data,
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is_eta=is_eta,
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)
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# ex_Model(df,
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# horizon=horizon,
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# input_size=input_size,
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# train_steps=train_steps,
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# val_check_steps=val_check_steps,
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# early_stop_patience_steps=early_stop_patience_steps,
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# is_debug=is_debug,
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# dataset=dataset,
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# is_train=is_train,
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# is_fivemodels=is_fivemodels,
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# val_size=val_size,
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# test_size=test_size,
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# settings=settings,
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# now=now,
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# etadata = etadata,
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# modelsindex = modelsindex,
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# data = data,
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# is_eta=is_eta,
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# )
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logger.info('模型训练完成')
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@ -151,10 +146,13 @@ def predict_main():
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logger.info('制作报告ing')
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title = f'{settings}--{now}-预测报告' # 报告标题
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brent_export_pdf(dataset=dataset,num_models = 5 if is_fivemodels else 22,time=end_time,
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if 'Brent' in y:
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brent_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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# pp_export_pdf(dataset=dataset,num_models = 5 if is_fivemodels else 22,time=end_time,
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# reportname=reportname),
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else:
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pp_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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@ -950,6 +950,7 @@ def brent_export_pdf(num_indicators=475,num_models=21, num_dayindicator=202,inpu
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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(os.path.join(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('1. 确定波动率置信区间：统计近60个交易日的真实价格波动率，找出在 10% ，90% 的分位值作为波动率置信区间;'))
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content.append(Graphs.draw_text('2. 确定通道上界：在所有模型的预测结果中 <= 前一天真实价格 乘以 90%的置信波动分位数'))
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@ -1391,10 +1392,528 @@ def brent_export_pdf(num_indicators=475,num_models=21, num_dayindicator=202,inpu
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upload_report_data(token, upload_data)
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except TimeoutError as e:
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print(f"请求超时: {e}")
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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'):
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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(f'dataset/指标数据添加时间特征.csv', parse_dates=['ds']).tail(20)
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def draw_feature_trend(feature_data_df, features):
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# 画特征近一周的趋势图
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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)), 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)), 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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plt.savefig(os.path.join(dataset, f'{col}与价格散点图.png'))
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content.append(Graphs.draw_img(os.path.join(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(f'{y}{time}预测报告'))
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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(os.path.join(dataset,'历史价格-预测值.png')))
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# 根据真实值分组，去掉最高最小预测值画图逻辑
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content.append(Graphs.draw_text('图示说明：'))
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content.append(Graphs.draw_text('1. 将所有模型的预测结果进行分组，大于真实值的为一组，小于真实值的为一组，去掉最高的预测值，去掉最小的预测值'))
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content.append(Graphs.draw_text('2. 确定通道上界：在大于真实值的分组中，取最大的预测值'))
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content.append(Graphs.draw_text('3. 确定通道下界：在小于真实值的分组中，取第二小的预测值'))
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content.append(Graphs.draw_text('4. 预测结果没有真实值作为参考依据，通道上界取近20个交易日内预测在上界值的模型对应的预测值，通道下界同理；'))
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content.append(Graphs.draw_text('5. 预测结果选用近20个交易日内，最多接近真实值的模型的预测值对应的预测结果；'))
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content.append(Graphs.draw_text('6. 预测结果在通道外的，代表最接近真实值的预测结果不在置信波动范围内。'))
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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('1. 确定波动率置信区间：统计近60个交易日的真实价格波动率，找出在 10% ，90% 的分位值作为波动率置信区间;'))
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# content.append(Graphs.draw_text('2. 确定通道上界：在所有模型的预测结果中 <= 前一天真实价格 乘以 90%的置信波动分位数'))
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# content.append(Graphs.draw_text('3. 确定通道下界：在所有模型的预测结果中 >= 前一天真实价格 乘以 10%的置信波动分位数'))
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# content.append(Graphs.draw_text('4. 预测结果没有真实值作为参考依据，通道上界取近20个交易日内预测在上界值的模型对应的预测值，通道下界同理；'))
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# content.append(Graphs.draw_text('5. 预测结果选用近20个交易日内，最多接近真实值的模型的预测值对应的预测结果；'))
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# content.append(Graphs.draw_text('6. 预测结果在通道外的，代表最接近真实值的预测结果不在置信波动范围内。'))
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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(dataset,'predict.csv'),encoding='gbk')
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df_true = pd.read_csv(os.path.join(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(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!= '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(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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# 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(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('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(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(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(dataset,'指标数据添加时间特征.csv'),encoding='utf-8') # 计算相关系数用
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df_zhibiaofenlei = loadcsv(os.path.join(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('''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('''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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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(), 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(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(os.path.join(dataset,f'{name}类指标相关性直方分布图.png')))
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content.append(Graphs.draw_text(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(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(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(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
|
||||
negative_corr_features = correlation_matrix[correlation_matrix < 0].sort_values(ascending=True).index.tolist()
|
||||
|
||||
print(f'{name}下负相关的特征值有：',negative_corr_features)
|
||||
if len(negative_corr_features) > 5:
|
||||
negative_corr_features = negative_corr_features[:5]
|
||||
content.append(Graphs.draw_text(f'与预测目标y负相关前五的特征有：{negative_corr_features}'))
|
||||
draw_feature_trend(feature_data_df, negative_corr_features)
|
||||
elif len(negative_corr_features) == 0:
|
||||
pass
|
||||
else:
|
||||
content.append(Graphs.draw_text(f'{name}类指标中，与预测目标y负相关的特征有：{negative_corr_features}'))
|
||||
draw_feature_trend(feature_data_df, negative_corr_features)
|
||||
|
||||
|
||||
# 计算correlation_sum 第一行的相关性的绝对值的总和
|
||||
correlation_sum = correlation_matrix.abs().sum()
|
||||
logger.info(f'{name}类指标的相关性总和为：{correlation_sum}')
|
||||
# 分组的相关性总和拼接到grouped_corr
|
||||
goup_corr = pd.DataFrame({'指标分类': [name], '指标数量': [len(cols_subset)], '相关性总和': [correlation_sum]})
|
||||
grouped_corr = pd.concat([grouped_corr, goup_corr], axis=0, ignore_index=True)
|
||||
|
||||
# 绘制相关性总和的气泡图
|
||||
logger.info(f'开始绘制相关性总和的气泡图')
|
||||
plt.figure(figsize=(10, 10))
|
||||
sns.scatterplot(data=grouped_corr, x='相关性总和', y='指标数量', size='相关性总和', sizes=(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')
|
||||
plt.title('指标分类相关性总和的气泡图')
|
||||
plt.ylabel('数量')
|
||||
plt.savefig(os.path.join(dataset, '指标分类相关性总和的气泡图.png'), bbox_inches='tight')
|
||||
plt.close()
|
||||
content.append(Graphs.draw_img(os.path.join(dataset,'指标分类相关性总和的气泡图.png')))
|
||||
content.append(Graphs.draw_text('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。'))
|
||||
logger.info(f'绘制相关性总和的气泡图结束')
|
||||
|
||||
|
||||
|
||||
# # 计算特征相关性
|
||||
# data.rename(columns={y: 'y'}, inplace=True)
|
||||
# data['ds'] = pd.to_datetime(data['ds'])
|
||||
# data.drop(columns=['ds'], inplace=True)
|
||||
# # 创建一个空的 DataFrame 来保存相关系数
|
||||
# correlation_df = pd.DataFrame(columns=['Feature', 'Correlation'])
|
||||
# # 计算各特征与目标列的皮尔逊相关系数，并保存到新的 Data 中
|
||||
# for col in data.columns:
|
||||
# if col!= 'y':
|
||||
# pearson_correlation = np.corrcoef(data[col], data['y'])[0, 1]
|
||||
# spearman_correlation, _ = spearmanr(data[col], data['y'])
|
||||
# new_row = {'Feature': col, 'Pearson_Correlation': round(pearson_correlation,3), 'Spearman_Correlation': round(spearman_correlation,2)}
|
||||
# correlation_df = correlation_df._append(new_row, ignore_index=True)
|
||||
|
||||
# correlation_df.drop('Correlation', axis=1, inplace=True)
|
||||
# correlation_df.dropna(inplace=True)
|
||||
# correlation_df.to_csv(os.path.join(dataset,'指标相关性分析.csv'), index=False)
|
||||
|
||||
# data = correlation_df['Pearson_Correlation'].values.tolist()
|
||||
# # 生成 -1 到 1 的 20 个区间
|
||||
# bins = np.linspace(-1, 1, 21)
|
||||
# # 计算每个区间的统计数（这里是区间内数据的数量）
|
||||
# hist_values = [np.sum((data >= bins[i]) & (data < bins[i + 1])) for i in range(len(bins) - 1)]
|
||||
|
||||
# #设置画布大小
|
||||
# plt.figure(figsize=(10, 6))
|
||||
# # 绘制直方图
|
||||
# plt.bar(bins[:-1], hist_values, width=(bins[1] - bins[0]))
|
||||
|
||||
# # 添加标题和坐标轴标签
|
||||
# plt.title('皮尔逊相关系数分布图')
|
||||
# plt.xlabel('区间')
|
||||
# plt.ylabel('统计数')
|
||||
# plt.savefig(os.path.join(dataset, '皮尔逊相关性系数.png'))
|
||||
# plt.close()
|
||||
|
||||
|
||||
# #设置画布大小
|
||||
# plt.figure(figsize=(10, 6))
|
||||
# data = correlation_df['Spearman_Correlation'].values.tolist()
|
||||
# # 计算每个区间的统计数（这里是区间内数据的数量）
|
||||
# hist_values = [np.sum((data >= bins[i]) & (data < bins[i + 1])) for i in range(len(bins) - 1)]
|
||||
|
||||
# # 绘制直方图
|
||||
# plt.bar(bins[:-1], hist_values, width=(bins[1] - bins[0]))
|
||||
|
||||
# # 添加标题和坐标轴标签
|
||||
# plt.title('斯皮尔曼相关系数分布图')
|
||||
# plt.xlabel('区间')
|
||||
# plt.ylabel('统计数')
|
||||
# plt.savefig(os.path.join(dataset, '斯皮尔曼相关性系数.png'))
|
||||
# plt.close()
|
||||
# content.append(Graphs.draw_text(f'指标相关性分析--皮尔逊相关系数：'))
|
||||
# # 皮尔逊正相关 不相关 负相关 的表格
|
||||
# content.append(Graphs.draw_img(os.path.join(dataset,'皮尔逊相关性系数.png')))
|
||||
# content.append(Graphs.draw_text('''皮尔逊相关系数说明：'''))
|
||||
# content.append(Graphs.draw_text('''衡量两个特征之间的线性相关性。'''))
|
||||
# content.append(Graphs.draw_text('''
|
||||
# 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。'''))
|
||||
# content.append(Graphs.draw_text('''当前特征中正相关前十的有：'''))
|
||||
# top10_columns = correlation_df.sort_values(by='Pearson_Correlation',ascending=False).head(10)['Feature'].to_list()
|
||||
# top10 = ','.join(top10_columns)
|
||||
# content.append(Graphs.draw_text(f'''{top10}'''))
|
||||
|
||||
# feature_df = feature_data_df[['ds','y']+top10_columns]
|
||||
# # 遍历X每一列，和yy画散点图 ，
|
||||
# for i, col in enumerate(feature_df.columns):
|
||||
# print(f'正在绘制第{i+1}个特征{col}与价格散点图...')
|
||||
# if col not in ['ds', 'y']:
|
||||
# fig, ax1 = plt.subplots(figsize=(10, 6))
|
||||
# # 在第一个坐标轴上绘制数据
|
||||
# ax1.plot(feature_df['ds'], feature_df['y'], 'b-')
|
||||
# ax1.set_xlabel('日期')
|
||||
# ax1.set_ylabel('y', color='b')
|
||||
# ax1.tick_params('y', colors='b')
|
||||
# # 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
|
||||
# for j in range(1,len(feature_df),2):
|
||||
# value = feature_df['y'].iloc[j]
|
||||
# date = feature_df['ds'].iloc[j]
|
||||
# offset = 1.001
|
||||
# ax1.text(date, value * offset, str(round(value, 2)), ha='center', va='bottom', color='b', fontsize=10)
|
||||
# # 创建第二个坐标轴
|
||||
# ax2 = ax1.twinx()
|
||||
# # 在第二个坐标轴上绘制数据
|
||||
# line2 = ax2.plot(feature_df['ds'], feature_df[col], 'r-')
|
||||
# ax2.set_ylabel(col, color='r')
|
||||
# ax2.tick_params('y', colors='r')
|
||||
# # 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
|
||||
# for j in range(0,len(feature_df),2):
|
||||
# value = feature_df[col].iloc[j]
|
||||
# date = feature_df['ds'].iloc[j]
|
||||
# offset = 1.001
|
||||
# ax2.text(date, value * offset, str(round(value, 2)), ha='center', va='bottom', color='r', fontsize=10)
|
||||
# # 添加标题
|
||||
# plt.title(col)
|
||||
# # 设置横坐标为日期格式并自动调整
|
||||
# locator = mdates.AutoDateLocator()
|
||||
# formatter = mdates.AutoDateFormatter(locator)
|
||||
# ax1.xaxis.set_major_locator(locator)
|
||||
# ax1.xaxis.set_major_formatter(formatter)
|
||||
# # 文件名特殊字符处理
|
||||
# col = col.replace('*', '-')
|
||||
# col = col.replace(':', '-')
|
||||
# plt.savefig(os.path.join(dataset, f'{col}与价格散点图.png'))
|
||||
# content.append(Graphs.draw_img(os.path.join(dataset, f'{col}与价格散点图.png')))
|
||||
# plt.close()
|
||||
|
||||
|
||||
# content.append(Graphs.draw_text(f'指标相关性分析--斯皮尔曼相关系数：'))
|
||||
# # 皮尔逊正相关 不相关 负相关 的表格
|
||||
# content.append(Graphs.draw_img(os.path.join(dataset,'斯皮尔曼相关性系数.png')))
|
||||
# content.append(Graphs.draw_text('斯皮尔曼相关系数（Spearmans rank correlation coefficient）是一种用于衡量两个变量之间的单调关系（不一定是线性关系）的统计指标。'))
|
||||
# content.append(Graphs.draw_text('它的计算基于变量的秩次（即变量值的排序位置）而非变量的原始值。'))
|
||||
# content.append(Graphs.draw_text('斯皮尔曼相关系数的取值范围在 -1 到 1 之间。'))
|
||||
# content.append(Graphs.draw_text('当系数为 1 时，表示两个变量之间存在完全正的单调关系；'))
|
||||
# content.append(Graphs.draw_text('''当前特征中正单调关系前十的有：'''))
|
||||
# top10_columns = correlation_df.sort_values(by='Spearman_Correlation',ascending=False).head(10)['Feature'].to_list()
|
||||
# top10 = ','.join(top10_columns)
|
||||
# content.append(Graphs.draw_text(f'''{top10}'''))
|
||||
|
||||
# feature_df = feature_data_df[['ds','y']+top10_columns]
|
||||
# # 遍历X每一列，和yy画散点图 ，
|
||||
# for i, col in enumerate(feature_df.columns):
|
||||
# print(f'正在绘制第{i+1}个特征{col}与价格散点图...')
|
||||
# if col not in ['ds', 'y']:
|
||||
# fig, ax1 = plt.subplots(figsize=(10, 6))
|
||||
# # 在第一个坐标轴上绘制数据
|
||||
# ax1.plot(feature_df['ds'], feature_df['y'], 'b-')
|
||||
# ax1.set_xlabel('日期')
|
||||
# ax1.set_ylabel('y', color='b')
|
||||
# ax1.tick_params('y', colors='b')
|
||||
# # 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
|
||||
# for j in range(1,len(feature_df),2):
|
||||
# value = feature_df['y'].iloc[j]
|
||||
# date = feature_df['ds'].iloc[j]
|
||||
# offset = 1.001
|
||||
# ax1.text(date, value * offset, str(round(value, 2)), ha='center', va='bottom', color='b', fontsize=10)
|
||||
# # 创建第二个坐标轴
|
||||
# ax2 = ax1.twinx()
|
||||
# # 在第二个坐标轴上绘制数据
|
||||
# line2 = ax2.plot(feature_df['ds'], feature_df[col], 'r-')
|
||||
# ax2.set_ylabel(col, color='r')
|
||||
# ax2.tick_params('y', colors='r')
|
||||
# # 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
|
||||
# for j in range(0,len(feature_df),2):
|
||||
# value = feature_df[col].iloc[j]
|
||||
# date = feature_df['ds'].iloc[j]
|
||||
# offset = 1.001
|
||||
# ax2.text(date, value * offset, str(round(value, 2)), ha='center', va='bottom', color='r', fontsize=10)
|
||||
# # 添加标题
|
||||
# plt.title(col)
|
||||
# # 设置横坐标为日期格式并自动调整
|
||||
# locator = mdates.AutoDateLocator()
|
||||
# formatter = mdates.AutoDateFormatter(locator)
|
||||
# ax1.xaxis.set_major_locator(locator)
|
||||
# ax1.xaxis.set_major_formatter(formatter)
|
||||
# # 文件名特殊字符处理
|
||||
# col = col.replace('*', '-')
|
||||
# col = col.replace(':', '-')
|
||||
# plt.savefig(os.path.join(dataset, f'{col}与价格散点图.png'))
|
||||
# content.append(Graphs.draw_img(os.path.join(dataset, f'{col}与价格散点图.png')))
|
||||
# plt.close()
|
||||
|
||||
# content.append(Graphs.draw_text('当系数为 -1 时，表示存在完全负的单调关系；'))
|
||||
# content.append(Graphs.draw_text('''当前特征中负单调关系前十的有：'''))
|
||||
# tail10_columns = correlation_df.sort_values(by='Spearman_Correlation',ascending=True).head(10)['Feature'].to_list()
|
||||
# top10 = ','.join(tail10_columns)
|
||||
# content.append(Graphs.draw_text(f'''{top10}'''))
|
||||
# # 获取特征的近一周值
|
||||
# feature_df = feature_data_df[['ds','y']+tail10_columns]
|
||||
# # 遍历X每一列，和yy画散点图 ，
|
||||
# for i, col in enumerate(feature_df.columns):
|
||||
# print(f'正在绘制第{i+1}个特征{col}与价格散点图...')
|
||||
# if col not in ['ds', 'y']:
|
||||
# fig, ax1 = plt.subplots(figsize=(10, 6))
|
||||
# # 在第一个坐标轴上绘制数据
|
||||
# ax1.plot(feature_df['ds'], feature_df['y'], 'b-')
|
||||
# ax1.set_xlabel('日期')
|
||||
# ax1.set_ylabel('y', color='b')
|
||||
# ax1.tick_params('y', colors='b')
|
||||
# # 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
|
||||
# for j in range(len(feature_df)):
|
||||
# if j%2 == 1:
|
||||
# value = feature_df['y'].iloc[j]
|
||||
# date = feature_df['ds'].iloc[j]
|
||||
# offset = 1.001
|
||||
# ax1.text(date, value * offset, str(round(value, 2)), ha='center', va='bottom', color='b', fontsize=10)
|
||||
# # 创建第二个坐标轴
|
||||
# ax2 = ax1.twinx()
|
||||
# # 在第二个坐标轴上绘制数据
|
||||
# line2 = ax2.plot(feature_df['ds'], feature_df[col], 'r-')
|
||||
# ax2.set_ylabel(col, color='r')
|
||||
# ax2.tick_params('y', colors='r')
|
||||
# # 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
|
||||
# for j in range(1,len(feature_df),2):
|
||||
# value = feature_df[col].iloc[j]
|
||||
# date = feature_df['ds'].iloc[j]
|
||||
# offset = 1.001
|
||||
# ax2.text(date, value * offset, str(round(value, 2)), ha='center', va='bottom', color='r', fontsize=10)
|
||||
# # 添加标题
|
||||
# plt.title(col)
|
||||
# # 设置横坐标为日期格式并自动调整
|
||||
# locator = mdates.AutoDateLocator()
|
||||
# formatter = mdates.AutoDateFormatter(locator)
|
||||
# ax1.xaxis.set_major_locator(locator)
|
||||
# ax1.xaxis.set_major_formatter(formatter)
|
||||
# # 文件名特殊字符处理
|
||||
# col = col.replace('*', '-')
|
||||
# col = col.replace(':', '-')
|
||||
# plt.savefig(os.path.join(dataset, f'{col}与价格散点图.png'))
|
||||
# content.append(Graphs.draw_img(os.path.join(dataset, f'{col}与价格散点图.png')))
|
||||
# plt.close()
|
||||
# content.append(Graphs.draw_text('当系数为 0 时，表示两个变量之间不存在单调关系。'))
|
||||
# content.append(Graphs.draw_text('与皮尔逊相关系数相比，斯皮尔曼相关系数对于数据中的异常值不敏感，更适用于处理非线性关系或存在极端值的数据。'))
|
||||
content.append(Graphs.draw_little_title('模型选择：'))
|
||||
content.append(Graphs.draw_text(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：'))
|
||||
|
||||
### 读取模型简介
|
||||
with open(os.path.join(dataset,'model_introduction.txt'), 'r', encoding='utf-8') as f:
|
||||
for line in f:
|
||||
line_split = line.strip().split('--')
|
||||
if line_split[0] in fivemodels_list:
|
||||
for introduction in line_split:
|
||||
content.append(Graphs.draw_text(introduction))
|
||||
|
||||
content.append(Graphs.draw_little_title('模型评估：'))
|
||||
|
||||
df = pd.read_csv(os.path.join(dataset,'model_evaluation.csv'),encoding='utf-8')
|
||||
# 判断 df 的数值列转为float
|
||||
for col in eval_df.columns:
|
||||
if col not in ['模型(Model)']:
|
||||
eval_df[col] = eval_df[col].astype(float)
|
||||
eval_df[col] = eval_df[col].round(3)
|
||||
# 筛选 fivemodels_list.tolist() 的行
|
||||
eval_df = eval_df[eval_df['模型(Model)'].isin(fivemodels_list)]
|
||||
# df转置
|
||||
eval_df = eval_df.T
|
||||
# df重置索引
|
||||
eval_df = eval_df.reset_index()
|
||||
eval_df = eval_df.T
|
||||
# # 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(dataset,'预测值与真实值对比图.png')))
|
||||
|
||||
# 附1，特征列表
|
||||
content.append(Graphs.draw_little_title('附1、特征列表：'))
|
||||
df_fuyi = pd.read_csv(os.path.join(dataset,'特征频度统计.csv'),encoding='utf-8')
|
||||
for col in df_fuyi.columns:
|
||||
fuyi = df_fuyi[col]
|
||||
fuyi = fuyi.dropna()
|
||||
content.append(Graphs.draw_text(f'{col}：'))
|
||||
for i in range(len(fuyi)):
|
||||
content.append(Graphs.draw_text(f'{i+1}、{fuyi[i]}'))
|
||||
|
||||
|
||||
|
||||
### 生成pdf文件
|
||||
doc = SimpleDocTemplate(os.path.join(dataset,reportname), pagesize=letter)
|
||||
# doc = SimpleDocTemplate(os.path.join(dataset,'reportname.pdf'), pagesize=letter)
|
||||
doc.build(content)
|
||||
# pdf 上传到数字化信息平台
|
||||
# 读取pdf并转为base64
|
||||
try:
|
||||
if is_update_report:
|
||||
with open(os.path.join(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}")
|
||||
|
||||
|
||||
|
||||
def pp_export_pdf(num_indicators=475,num_models=21, num_dayindicator=202,inputsize=5,dataset='dataset',time = '2024-07-30',reportname='report.pdf'):
|
||||
def pp_export_pdf_v1(num_indicators=475,num_models=21, num_dayindicator=202,inputsize=5,dataset='dataset',time = '2024-07-30',reportname='report.pdf'):
|
||||
global y
|
||||
# 创建内容对应的空列表
|
||||
content = list()
|
||||
|
||||
Loading…
Reference in New Issue
Block a user