原油报告内容更改
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workpc
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@ -234,6 +234,15 @@
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# 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。'))
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# 附1，特征列表
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# content.append(Graphs.draw_little_title('附1、特征列表：'))
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# df_fuyi = pd.read_csv(os.path.join(dataset,'特征频度统计.csv'),encoding='utf-8')
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# for col in df_fuyi.columns:
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# fuyi = df_fuyi[col]
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# fuyi = fuyi.dropna()
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# content.append(Graphs.draw_text(f'{col}：'))
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# for i in range(len(fuyi)):
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# content.append(Graphs.draw_text(f'{i+1}、{fuyi[i]}'))
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@ -192,14 +192,14 @@ warning_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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is_edbnamelist = False # 自定义特征，对应上面的edbnamelist
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is_update_eta = False # 预测结果上传到eta
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is_update_report = False # 是否上传报告
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is_update_warning_data = True # 是否上传预警数据
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is_update_warning_data = False # 是否上传预警数据
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# 数据截取日期
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end_time = '' # 数据截取日期
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@ -1,5 +1,5 @@
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# from config_jingbo import *
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from config_juxiting import *
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from config_jingbo import *
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# from config_juxiting import *
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# 导入模块
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@ -116,7 +116,7 @@ def predict_main():
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logger.info('今天是周一，更新预测模型')
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# 计算最近20天预测残差最低的模型名称
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model_results = sqlitedb.select_data('trueandpredict', order_by="ds DESC", limit="20")
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model_results = sqlitedb.select_data('trueandpredict', order_by="ds DESC", limit="60")
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# 删除空值率为40%以上的列,删除空行
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model_results = model_results.dropna(thresh=len(model_results)*0.6,axis=1)
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model_results = model_results.dropna()
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@ -135,7 +135,7 @@ def predict_main():
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min_abs_error_rate_column_name = min_abs_error_rate_column_name.map(lambda x: x.split('_')[0])
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# 取出现次数最多的模型名称
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most_common_model = min_abs_error_rate_column_name.value_counts().idxmax()
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logger.info(f"最近20天预测残差最低的模型名称：{most_common_model}")
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logger.info(f"最近60天预测残差最低的模型名称：{most_common_model}")
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# 保存结果到数据库
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@ -151,31 +151,31 @@ 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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logger.info('训练数据绘图ing')
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model_results3 = model_losss_juxiting(sqlitedb)
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model_results3 = model_losss(sqlitedb)
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logger.info('训练数据绘图end')
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# 模型报告
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@ -207,7 +207,7 @@ def predict_main():
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file=max(glob.glob(os.path.join(dataset,'*.pdf')), key=os.path.getctime),
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ssl=ssl,
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)
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m.send_mail()
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# m.send_mail()
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if __name__ == '__main__':
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@ -369,8 +369,8 @@ def model_losss(sqlitedb):
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# 最多频率的模型名称
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min_model_max_frequency_model = df_combined3['min_model'].value_counts().idxmax()
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max_model_max_frequency_model = df_combined3['max_model'].value_counts().idxmax()
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min_model_max_frequency_model = df_combined3['min_model'][-50:].value_counts().idxmax()
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max_model_max_frequency_model = df_combined3['max_model'][-50:].value_counts().idxmax()
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df_predict['min_model'] = min_model_max_frequency_model
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df_predict['max_model'] = max_model_max_frequency_model
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df_predict['min_within_quantile'] = df_predict[min_model_max_frequency_model]
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@ -756,7 +756,7 @@ def brent_export_pdf(num_indicators=475,num_models=21, num_dayindicator=202,inpu
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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(dataset,'指标数据添加时间特征.csv'), parse_dates=['ds']).tail(20)
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feature_data_df = pd.read_csv(os.path.join(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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# 画特征近一周的趋势图
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feature_df = feature_data_df[['ds','y']+features]
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@ -976,222 +976,6 @@ def brent_export_pdf(num_indicators=475,num_models=21, num_dayindicator=202,inpu
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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。'))
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logger.info(f'绘制相关性总和的气泡图结束')
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# # 计算特征相关性
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# data.rename(columns={y: 'y'}, inplace=True)
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# data['ds'] = pd.to_datetime(data['ds'])
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# data.drop(columns=['ds'], inplace=True)
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# # 创建一个空的 DataFrame 来保存相关系数
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# correlation_df = pd.DataFrame(columns=['Feature', 'Correlation'])
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# # 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
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# for col in data.columns:
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# if col!= 'y':
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# pearson_correlation = np.corrcoef(data[col], data['y'])[0, 1]
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# spearman_correlation, _ = spearmanr(data[col], data['y'])
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# new_row = {'Feature': col, 'Pearson_Correlation': round(pearson_correlation,3), 'Spearman_Correlation': round(spearman_correlation,2)}
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# correlation_df = correlation_df._append(new_row, ignore_index=True)
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# correlation_df.drop('Correlation', axis=1, inplace=True)
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# correlation_df.dropna(inplace=True)
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# correlation_df.to_csv(os.path.join(dataset,'指标相关性分析.csv'), index=False)
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# data = correlation_df['Pearson_Correlation'].values.tolist()
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# # 生成 -1 到 1 的 20 个区间
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# bins = np.linspace(-1, 1, 21)
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# # 计算每个区间的统计数（这里是区间内数据的数量）
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# hist_values = [np.sum((data >= bins[i]) & (data < bins[i + 1])) for i in range(len(bins) - 1)]
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# #设置画布大小
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# plt.figure(figsize=(10, 6))
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# # 绘制直方图
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# plt.bar(bins[:-1], hist_values, width=(bins[1] - bins[0]))
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# # 添加标题和坐标轴标签
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# plt.title('皮尔逊相关系数分布图')
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# plt.xlabel('区间')
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# plt.ylabel('统计数')
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# plt.savefig(os.path.join(dataset, '皮尔逊相关性系数.png'))
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# plt.close()
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# #设置画布大小
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# plt.figure(figsize=(10, 6))
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# data = correlation_df['Spearman_Correlation'].values.tolist()
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# # 计算每个区间的统计数（这里是区间内数据的数量）
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# hist_values = [np.sum((data >= bins[i]) & (data < bins[i + 1])) for i in range(len(bins) - 1)]
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# # 绘制直方图
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# plt.bar(bins[:-1], hist_values, width=(bins[1] - bins[0]))
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# # 添加标题和坐标轴标签
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# plt.title('斯皮尔曼相关系数分布图')
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# plt.xlabel('区间')
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# plt.ylabel('统计数')
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# plt.savefig(os.path.join(dataset, '斯皮尔曼相关性系数.png'))
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# plt.close()
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# content.append(Graphs.draw_text(f'指标相关性分析--皮尔逊相关系数：'))
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# # 皮尔逊正相关 不相关 负相关 的表格
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# content.append(Graphs.draw_img(os.path.join(dataset,'皮尔逊相关性系数.png')))
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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('''当前特征中正相关前十的有：'''))
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# top10_columns = correlation_df.sort_values(by='Pearson_Correlation',ascending=False).head(10)['Feature'].to_list()
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# top10 = ','.join(top10_columns)
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# content.append(Graphs.draw_text(f'''{top10}'''))
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# feature_df = feature_data_df[['ds','y']+top10_columns]
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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(feature_df.columns):
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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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# ax1.plot(feature_df['ds'], feature_df['y'], '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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# line2 = ax2.plot(feature_df['ds'], feature_df[col], '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 = 1.001
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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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# 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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# content.append(Graphs.draw_text(f'指标相关性分析--斯皮尔曼相关系数：'))
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# # 皮尔逊正相关 不相关 负相关 的表格
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# content.append(Graphs.draw_img(os.path.join(dataset,'斯皮尔曼相关性系数.png')))
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# 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。'))
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# 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。'))
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# content.append(Graphs.draw_text('斯皮尔曼相关系数的取值范围在 -1 到 1 之间。'))
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# 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；'))
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# content.append(Graphs.draw_text('''当前特征中正单调关系前十的有：'''))
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# top10_columns = correlation_df.sort_values(by='Spearman_Correlation',ascending=False).head(10)['Feature'].to_list()
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# top10 = ','.join(top10_columns)
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# content.append(Graphs.draw_text(f'''{top10}'''))
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# feature_df = feature_data_df[['ds','y']+top10_columns]
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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(feature_df.columns):
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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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# ax1.plot(feature_df['ds'], feature_df['y'], '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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# line2 = ax2.plot(feature_df['ds'], feature_df[col], '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 = 1.001
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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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# 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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# content.append(Graphs.draw_text('当系数为 -1 时，表示存在完全负的单调关系；'))
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# content.append(Graphs.draw_text('''当前特征中负单调关系前十的有：'''))
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# tail10_columns = correlation_df.sort_values(by='Spearman_Correlation',ascending=True).head(10)['Feature'].to_list()
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# top10 = ','.join(tail10_columns)
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# content.append(Graphs.draw_text(f'''{top10}'''))
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# # 获取特征的近一周值
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# feature_df = feature_data_df[['ds','y']+tail10_columns]
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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(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：'))
|
||||
|
||||
@ -1230,15 +1014,6 @@ def brent_export_pdf(num_indicators=475,num_models=21, num_dayindicator=202,inpu
|
||||
# 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]}'))
|
||||
|
||||
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user