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--git a/juxitingdataset/特征频度统计.csv b/juxitingdataset/特征频度统计.csv index 5e2e93d..0f48ace 100644 --- a/juxitingdataset/特征频度统计.csv +++ b/juxitingdataset/特征频度统计.csv @@ -1,4 +1,4 @@ -日度(220),周度(93),84天(1) +日度(219),周度(94),119天(1) PP主力收盘价拟合残差/丙烷 CP M1,PE注塑开工率/周,中国：华东地区：市场平均价：BOPP厚光膜 华南聚丙烯基差（折盘面收盘价）,PP：看跌比例：中国（周）, 华北聚丙烯基差（折盘面收盘价）,PP：看平比例：中国（周）, @@ -60,39 +60,39 @@ PP：高熔共聚：2240S：自提价：常州：国家能源宁煤（日）,PP PP：高熔共聚：EP548R：市场价：广州：中科炼化（日）,PP粉料库存, PP：高熔共聚：2240S：出厂价：华南地区：国家能源宁煤（日）,中国BOPP原料库存量, PP：高熔共聚：EP548R：出厂价：华南地区：中海壳牌（日）,BOPP订单天数-产成品库存天数（隆众）, -PP：薄壁注塑：BZ-70：出厂价：华北地区：寿光鲁清（日）,PP下游综合开工率/2WMA, -PP：薄壁注塑：PPH-MM60：出厂价：华北地区：石家庄炼化（日）,PP下游综合开工率同差, -PP：薄壁注塑：K1870-B：市场价：临沂：榆能化（日）,PP周度产量/4WMA, -PP：薄壁注塑：M60ET：出厂价：华东地区：镇海炼化（日）,PP粉检修减损量（周）, -PP：薄壁注塑：1040TE：出厂价：华东地区：宁夏神华宁煤（日）,PP周度产量同差, -PP：薄壁注塑：1040TE：出厂价：华南地区：宁夏神华宁煤（日）,PP周度产量, -PP：薄壁注塑：PPH-MN60：出厂价：华南地区：中石化北海（日）,PP周度开工率, -PP：薄壁注塑：TM6000H：出厂价：华南地区：福建联合石化（日）,PP下游开工/PE下游开工, -PP：透明：PPR-B10：出厂价：华中地区：中原中石化（日）,PP管材开工率同差, -PP：透明：PPR-MT25：出厂价：华中地区：中原中石化（日）,PP无纺布开工率同差, -PP：透明：PPR-MT75N：出厂价：华中地区：中原中石化（日）,PP下游综合开工率/3年超季节性, -PP：无规共聚：HC-M700B：出厂价：华北地区：山东东明（日）,中国再生PP周度开工负荷率, -热水管：PA14D：市场价：青州：大庆炼化（日）,中国再生PP周度开工负荷率同差, -CPP：二元共聚膜：DY-W0723F：市场主流价：天津：独山子石化（日）,PP粉料开工率（4WMA）, -PP：透明：R3080T：出厂价：华东地区：浙江鸿基（日）,PP检修减损量, -PP：透明：M08ETN：出厂价：华东地区：镇海炼化（日）,PP下游综合开工率（少注塑&CPP）, -PP：透明：R3260T：出厂价：华东地区：浙江鸿基（日）,BOPP开工率同差, -热水管：YPR-503：出厂价：华东地区：扬子石化（日）,BOPP开工率（4WMA）, -热水管：PA14D：出厂价：华东地区：大庆炼化（日）,PP粉料开工率, -CPP：二元共聚膜：F800EDF：出厂价：华东地区：上海石化（日）,PP周度检修率, -CPP：二元共聚膜：F08EC：出厂价：华东地区：镇海炼化（日）,PP周度产量周环差, -PP：透明：HT9025NX：出厂价：华南地区：茂名石化（日）,PP周度产量同比, -PP：透明：HT9025ZK：出厂价：华南地区：中科炼化（日）,PP下游成品库存天数, -PP：透明：HT9025NX：市场主流价：广州：中石化茂名（日）,PP部分下游订单天数, -热水管：T4401：出厂价：华南地区：茂名石化（日）,中国BOPP订单天数同差, -热水管：PPR-4220：出厂价：华南地区：广州石化（日）,中国BOPP订单天数/4WMA, -PP：BOPP：1103K：出厂价：华北地区：国家能源宁煤（日）,BOPP完工订单工作量（周）, -PP：BOPP：PPH-FL03-S：出厂价：华北地区：青岛炼化（日）,中国再生PP周度样本成交量, -PP：BOPP：1103K：出厂价：华东地区：国家能源宁煤（日）,BOPP开工率超季节性/3年, -PP：BOPP：F03BT：出厂价：华东地区：镇海炼化（日）,中国BOPP成品库存量同差, -PP：BOPP：1103K：出厂价：华南地区：宁夏神华宁煤（日）,中国CPP订单天数, -PP：BOPP：L5D98：出厂价：华南地区：广东石化（日）,BOPP 订单-成品天数, -PP：BOPP：PPH-F03D：出厂价：华南地区：海南炼化装置一（日）,, +PP：薄壁注塑：BZ-70：出厂价：华北地区：寿光鲁清（日）,BOPP开工率(隆众)同差, +PP：薄壁注塑：PPH-MM60：出厂价：华北地区：石家庄炼化（日）,PP下游综合开工率/2WMA, +PP：薄壁注塑：K1870-B：市场价：临沂：榆能化（日）,PP下游综合开工率同差, +PP：薄壁注塑：M60ET：出厂价：华东地区：镇海炼化（日）,PP周度产量/4WMA, +PP：薄壁注塑：1040TE：出厂价：华东地区：宁夏神华宁煤（日）,PP粉检修减损量（周）, +PP：薄壁注塑：1040TE：出厂价：华南地区：宁夏神华宁煤（日）,PP周度产量同差, +PP：薄壁注塑：PPH-MN60：出厂价：华南地区：中石化北海（日）,PP周度产量, +PP：薄壁注塑：TM6000H：出厂价：华南地区：福建联合石化（日）,PP周度开工率, +PP：透明：PPR-B10：出厂价：华中地区：中原中石化（日）,PP下游开工/PE下游开工, +PP：透明：PPR-MT25：出厂价：华中地区：中原中石化（日）,PP管材开工率同差, +PP：透明：PPR-MT75N：出厂价：华中地区：中原中石化（日）,PP无纺布开工率同差, +PP：无规共聚：HC-M700B：出厂价：华北地区：山东东明（日）,PP下游综合开工率/3年超季节性, +热水管：PA14D：市场价：青州：大庆炼化（日）,中国再生PP周度开工负荷率, +CPP：二元共聚膜：DY-W0723F：市场主流价：天津：独山子石化（日）,中国再生PP周度开工负荷率同差, +PP：透明：R3080T：出厂价：华东地区：浙江鸿基（日）,PP粉料开工率（4WMA）, +PP：透明：M08ETN：出厂价：华东地区：镇海炼化（日）,PP检修减损量, +PP：透明：R3260T：出厂价：华东地区：浙江鸿基（日）,PP下游综合开工率（少注塑&CPP）, +热水管：YPR-503：出厂价：华东地区：扬子石化（日）,BOPP开工率同差, +热水管：PA14D：出厂价：华东地区：大庆炼化（日）,BOPP开工率（4WMA）, +CPP：二元共聚膜：F800EDF：出厂价：华东地区：上海石化（日）,PP粉料开工率, +CPP：二元共聚膜：F08EC：出厂价：华东地区：镇海炼化（日）,PP周度检修率, +PP：透明：HT9025NX：出厂价：华南地区：茂名石化（日）,PP周度产量周环差, +PP：透明：HT9025ZK：出厂价：华南地区：中科炼化（日）,PP周度产量同比, +PP：透明：HT9025NX：市场主流价：广州：中石化茂名（日）,PP下游成品库存天数, +热水管：T4401：出厂价：华南地区：茂名石化（日）,PP部分下游订单天数, +热水管：PPR-4220：出厂价：华南地区：广州石化（日）,中国BOPP订单天数同差, +PP：BOPP：1103K：出厂价：华北地区：国家能源宁煤（日）,中国BOPP订单天数/4WMA, +PP：BOPP：PPH-FL03-S：出厂价：华北地区：青岛炼化（日）,BOPP完工订单工作量（周）, +PP：BOPP：1103K：出厂价：华东地区：国家能源宁煤（日）,中国再生PP周度样本成交量, +PP：BOPP：F03BT：出厂价：华东地区：镇海炼化（日）,BOPP开工率超季节性/3年, +PP：BOPP：1103K：出厂价：华南地区：宁夏神华宁煤（日）,中国BOPP成品库存量同差, +PP：BOPP：L5D98：出厂价：华南地区：广东石化（日）,中国CPP订单天数, +PP：BOPP：PPH-F03D：出厂价：华南地区：海南炼化装置一（日）,BOPP 订单-成品天数, CPP：二元共聚膜：PPR-F08M-S：出厂价：华南地区：茂名石化（日）,, CPP：二元共聚膜：PPR-F08-S：出厂价：华南地区：茂名石化（日）,, BOPP：12μ光膜：出厂价：华北地区：凯达包装（日）,, @@ -156,7 +156,6 @@ PP现货-丙烯价差（山东）,, 聚丙烯出口利润,, PP检修损失量（万吨/年）,, PP日度产量1000天百分位,, -BOPP开工率(隆众)同差,, PP开工率/2WMA,, PP开工率同差,, PP日度开工率,, diff --git a/juxitingdataset/特征频度统计.txt b/juxitingdataset/特征频度统计.txt index 84dc40e..8bc342d 100644 --- a/juxitingdataset/特征频度统计.txt +++ b/juxitingdataset/特征频度统计.txt @@ -1,4 +1,4 @@ -特征信息：总共有312个,日度(220),周度(93),84天(1), 详看 附1、特征列表 +特征信息：总共有312个,日度(219),周度(94),119天(1), 详看 附1、特征列表 数据特征工程： 1. 数据日期排序，新日期在最后 2. 删除空列，特征数据列没有值，就删除 diff --git a/juxitingdataset/美元指数与价格散点图.png b/juxitingdataset/美元指数与价格散点图.png deleted file mode 100644 index 5e05d53..0000000 Binary files a/juxitingdataset/美元指数与价格散点图.png and /dev/null differ diff --git a/juxitingdataset/聚丙烯出口利润与价格散点图.png b/juxitingdataset/聚丙烯出口利润与价格散点图.png deleted file mode 100644 index 35ae932..0000000 Binary files a/juxitingdataset/聚丙烯出口利润与价格散点图.png and /dev/null differ diff --git a/juxitingdataset/聚丙烯进口利润与价格散点图.png b/juxitingdataset/聚丙烯进口利润与价格散点图.png deleted file mode 100644 index 49e356b..0000000 Binary files a/juxitingdataset/聚丙烯进口利润与价格散点图.png and /dev/null differ diff --git a/juxitingdataset/薄壁-拉丝价差（华北）与价格散点图.png b/juxitingdataset/薄壁-拉丝价差（华北）与价格散点图.png deleted file mode 100644 index 32fcee4..0000000 Binary files a/juxitingdataset/薄壁-拉丝价差（华北）与价格散点图.png and /dev/null differ diff --git a/juxitingdataset/黄金连1合约与价格散点图.png b/juxitingdataset/黄金连1合约与价格散点图.png deleted file mode 100644 index 58c9371..0000000 Binary files a/juxitingdataset/黄金连1合约与价格散点图.png and /dev/null differ diff --git a/main_juxiting.py b/main_juxiting.py index af93444..1c2b34b 100644 --- a/main_juxiting.py +++ b/main_juxiting.py @@ -108,25 +108,25 @@ def predict_main(): row,col = df.shape now = datetime.datetime.now().strftime('%Y%m%d%H%M%S') - ex_Model(df, - horizon=horizon, - input_size=input_size, - train_steps=train_steps, - val_check_steps=val_check_steps, - early_stop_patience_steps=early_stop_patience_steps, - is_debug=is_debug, - dataset=dataset, - is_train=is_train, - is_fivemodels=is_fivemodels, - val_size=val_size, - test_size=test_size, - settings=settings, - now=now, - etadata = etadata, - modelsindex = modelsindex, - data = data, - is_eta=is_eta, - ) + # ex_Model(df, + # horizon=horizon, + # input_size=input_size, + # train_steps=train_steps, + # val_check_steps=val_check_steps, + # early_stop_patience_steps=early_stop_patience_steps, + # is_debug=is_debug, + # dataset=dataset, + # is_train=is_train, + # is_fivemodels=is_fivemodels, + # val_size=val_size, + # test_size=test_size, + # settings=settings, + # now=now, + # etadata = etadata, + # modelsindex = modelsindex, + # data = data, + # is_eta=is_eta, + # ) logger.info('模型训练完成') diff --git a/models/nerulforcastmodels.py b/models/nerulforcastmodels.py index fe6330b..d05c786 100644 --- a/models/nerulforcastmodels.py +++ b/models/nerulforcastmodels.py @@ -534,58 +534,79 @@ def model_losss_juxiting(sqlitedb): with open(os.path.join(dataset,"best_modelnames.txt"), 'w') as f: f.write(','.join(modelnames) + '\n') - - # 根据真实值y确定最大最小值,去掉最高最低的预测值 - import heapq # 使用堆来找到最大和最小的值 + + # 根据最接近真实值的预测模型计算波动率，得到在波动率范围内的预测值确定通道边界 + + best_models = pd.read_csv(os.path.join(dataset,'best_modelnames.txt'),header=None).values.flatten().tolist() def find_min_max_within_quantile(row): - true_value = row['y'] - row.drop(['ds','y'], inplace=True) - row = row.astype(float).round(2) - - max_heap = [] - min_heap = [] - for col in row.index: - # 对比真实值进行分类 - if row[col] < true_value: - heapq.heappush(min_heap, row[col]) - elif row[col] > true_value: - heapq.heappush(max_heap, -row[col]) # 使用负号来实现最大堆 - - if len(max_heap) == 1: - max_y = max_heap[0] - elif len(max_heap) == 0: - max_y = -min_heap[-1] - else: - max_y = heapq.nsmallest(2, max_heap)[1] - - if len(min_heap) < 2 : - min_y = -max_heap[-1] - else: - min_y = heapq.nsmallest(2, min_heap)[-1] - - - # 获取最大和最小的值 - q10 = min_y - q90 = -max_y - - # 获取最大和最小的模型名称 + row = row[best_models] + q10 = row.min() + q90 = row.max() + # 获取 row行最大最小值模型名称 min_model = row[row == q10].idxmin() - max_model = row[row == q90].idxmax() + max_model = row[row == q90].idxmin() + + # # 判断flot值是否为空值 + # if pd.isna(q10) or pd.isna(q90): + return pd.Series([q10, q90,min_model,max_model], index=['min_within_quantile','max_within_quantile','min_model','max_model']) - # 设置上下界比例 - rote = 1 - - q10 = q10 * rote - q90 = q90 * rote - - logger.info(min_model,q10,max_model,q90) - - return pd.Series([q10, q90, min_model, max_model], index=['min_within_quantile', 'max_within_quantile', 'min_model', 'max_model']) - # # 遍历行 + # 遍历行 df_combined3[['min_within_quantile', 'max_within_quantile','min_model','max_model']] = df_combined3.apply(find_min_max_within_quantile, axis=1) df_combined = df_combined.round(4) print(df_combined3) + + # # 根据真实值y确定最大最小值,去掉最高最低的预测值 + # import heapq # 使用堆来找到最大和最小的值 + # def find_min_max_within_quantile(row): + # true_value = row['y'] + # row.drop(['ds','y'], inplace=True) + # row = row.astype(float).round(2) + + # max_heap = [] + # min_heap = [] + # for col in row.index: + # # 对比真实值进行分类 + # if row[col] < true_value: + # heapq.heappush(min_heap, row[col]) + # elif row[col] > true_value: + # heapq.heappush(max_heap, -row[col]) # 使用负号来实现最大堆 + + # if len(max_heap) == 1: + # max_y = max_heap[0] + # elif len(max_heap) == 0: + # max_y = -min_heap[-1] + # else: + # max_y = heapq.nsmallest(2, max_heap)[1] + + # if len(min_heap) < 2 : + # min_y = -max_heap[-1] + # else: + # min_y = heapq.nsmallest(2, min_heap)[-1] + + + # # 获取最大和最小的值 + # q10 = min_y + # q90 = -max_y + + # # 获取最大和最小的模型名称 + # min_model = row[row == q10].idxmin() + # max_model = row[row == q90].idxmax() + + # # 设置上下界比例 + # rote = 1 + + # q10 = q10 * rote + # q90 = q90 * rote + + # logger.info(min_model,q10,max_model,q90) + + # return pd.Series([q10, q90, min_model, max_model], index=['min_within_quantile', 'max_within_quantile', 'min_model', 'max_model']) + # # # 遍历行 + # df_combined3[['min_within_quantile', 'max_within_quantile','min_model','max_model']] = df_combined3.apply(find_min_max_within_quantile, axis=1) + # df_combined = df_combined.round(4) + # print(df_combined3) + # 使用最佳五个模型进行绘图 # best_models = pd.read_csv(os.path.join(dataset,'best_modelnames.txt'),header=None).values.flatten().tolist() # def find_min_max_within_quantile(row):