diff --git a/config_jingbo_yuedu.py b/config_jingbo_yuedu.py index bb0c6ee..d41ec10 100644 --- a/config_jingbo_yuedu.py +++ b/config_jingbo_yuedu.py @@ -39,35 +39,35 @@ edbnamelist = [ ] -# eta自有数据指标编码 +# eta自有数据指标编码 次月 modelsindex = { - 'NHITS': 'SELF0000001', - 'Informer': 'SELF0000057', - 'LSTM': 'SELF0000058', - 'iTransformer': 'SELF0000059', - 'TSMixer': 'SELF0000060', - 'TSMixerx': 'SELF0000061', - 'PatchTST': 'SELF0000062', - 'RNN': 'SELF0000063', - 'GRU': 'SELF0000064', - 'TCN': 'SELF0000065', - 'BiTCN': 'SELF0000066', - 'DilatedRNN': 'SELF0000067', - 'MLP': 'SELF0000068', - 'DLinear': 'SELF0000069', - 'NLinear': 'SELF0000070', - 'TFT': 'SELF0000071', - 'FEDformer': 'SELF0000072', - 'StemGNN': 'SELF0000073', - 'MLPMultivariate': 'SELF0000074', - 'TiDE': 'SELF0000075', - 'DeepNPTS': 'SELF0000076' + 'NHITS': 'SELF0000143', + 'Informer': 'SELF0000144', + 'LSTM': 'SELF0000145', + 'iTransformer': 'SELF0000146', + 'TSMixer': 'SELF0000147', + 'TSMixerx': 'SELF0000148', + 'PatchTST': 'SELF0000149', + 'RNN': 'SELF0000150', + 'GRU': 'SELF0000151', + 'TCN': 'SELF0000152', + 'BiTCN': 'SELF0000153', + 'DilatedRNN': 'SELF0000154', + 'MLP': 'SELF0000155', + 'DLinear': 'SELF0000156', + 'NLinear': 'SELF0000157', + 'TFT': 'SELF0000158', + 'FEDformer': 'SELF0000159', + 'StemGNN': 'SELF0000160', + 'MLPMultivariate': 'SELF0000161', + 'TiDE': 'SELF0000162', + 'DeepNPTS': 'SELF0000163', } # eta 上传预测结果的请求体，后面发起请求的时候更改 model datalist 数据 data = { "IndexCode": "", - "IndexName": "价格预测模型", + "IndexName": "价格预测xx模型-次月", "Unit": "无", "Frequency": "日度", "SourceName": f"价格预测", @@ -305,7 +305,7 @@ is_timefurture = True # 是否使用时间特征 is_fivemodels = False # 是否使用之前保存的最佳的5个模型 is_edbcode = False # 特征使用edbcoding列表中的 is_edbnamelist = False # 自定义特征，对应上面的edbnamelist -is_update_eta = False # 预测结果上传到eta +is_update_eta = True # 预测结果上传到eta is_update_report = True # 是否上传报告 is_update_warning_data = False # 是否上传预警数据 is_update_predict_value = True # 是否上传预测值到市场信息平台 diff --git a/config_jingbo_zhoudu.py b/config_jingbo_zhoudu.py index 5eea36d..d69faf4 100644 --- a/config_jingbo_zhoudu.py +++ b/config_jingbo_zhoudu.py @@ -39,29 +39,29 @@ edbnamelist = [ ] -# eta自有数据指标编码 +# eta自有数据指标编码 次周 modelsindex = { - 'NHITS': 'SELF0000001', - 'Informer': 'SELF0000057', - 'LSTM': 'SELF0000058', - 'iTransformer': 'SELF0000059', - 'TSMixer': 'SELF0000060', - 'TSMixerx': 'SELF0000061', - 'PatchTST': 'SELF0000062', - 'RNN': 'SELF0000063', - 'GRU': 'SELF0000064', - 'TCN': 'SELF0000065', - 'BiTCN': 'SELF0000066', - 'DilatedRNN': 'SELF0000067', - 'MLP': 'SELF0000068', - 'DLinear': 'SELF0000069', - 'NLinear': 'SELF0000070', - 'TFT': 'SELF0000071', - 'FEDformer': 'SELF0000072', - 'StemGNN': 'SELF0000073', - 'MLPMultivariate': 'SELF0000074', - 'TiDE': 'SELF0000075', - 'DeepNPTS': 'SELF0000076' + 'NHITS': 'SELF0000099', + 'Informer': 'SELF0000100', + 'LSTM': 'SELF0000101', + 'iTransformer': 'SELF0000102', + 'TSMixer': 'SELF0000103', + 'TSMixerx': 'SELF0000104', + 'PatchTST': 'SELF0000105', + 'RNN': 'SELF0000106', + 'GRU': 'SELF0000107', + 'TCN': 'SELF0000108', + 'BiTCN': 'SELF0000109', + 'DilatedRNN': 'SELF0000110', + 'MLP': 'SELF0000111', + 'DLinear': 'SELF0000112', + 'NLinear': 'SELF0000113', + 'TFT': 'SELF0000114', + 'FEDformer': 'SELF0000115', + 'StemGNN': 'SELF0000116', + 'MLPMultivariate': 'SELF0000117', + 'TiDE': 'SELF0000118', + 'DeepNPTS': 'SELF0000119', } # eta 上传预测结果的请求体，后面发起请求的时候更改 model datalist 数据 diff --git a/config_juxiting.py b/config_juxiting.py index e667c33..febc9fb 100644 --- a/config_juxiting.py +++ b/config_juxiting.py @@ -219,7 +219,6 @@ push_data_value_list_data = { } - # 八大维度数据项编码 bdwd_items = { 'ciri': 'jxtppbdwdcr', @@ -243,7 +242,7 @@ table_name = 'v_tbl_crude_oil_warning' # 开关 is_train = True # 是否训练 is_debug = False # 是否调试 -is_eta = True # 是否使用eta接口 +is_eta = False # 是否使用eta接口 is_market = False # 是否通过市场信息平台获取特征 ,在is_eta 为true 的情况下生效 is_timefurture = True # 是否使用时间特征 is_fivemodels = False # 是否使用之前保存的最佳的5个模型 diff --git a/main_juxiting.py b/main_juxiting.py index 3f8e0a8..129bdac 100644 --- a/main_juxiting.py +++ b/main_juxiting.py @@ -302,12 +302,13 @@ def predict_main(): # 计算最近60天预测残差最低的模型名称 model_results = sqlitedb.select_data( 'trueandpredict', order_by="ds DESC", limit="60") + + # 删除created_dt,y列空行 + model_results = model_results.dropna( + subset=['created_dt', 'y']) # 删除空值率为90%以上的列 - if len(model_results) > 10: - model_results = model_results.dropna( - thresh=len(model_results)*0.1, axis=1) - # 删除空行 - model_results = model_results.dropna() + model_results = model_results.dropna( + thresh=len(model_results)*0.1, axis=1) modelnames = model_results.columns.to_list()[2:-1] for col in model_results[modelnames].select_dtypes(include=['object']).columns: model_results[col] = model_results[col].astype(np.float32) diff --git a/models/nerulforcastmodels.py b/models/nerulforcastmodels.py index 87280a8..b2ac995 100644 --- a/models/nerulforcastmodels.py +++ b/models/nerulforcastmodels.py @@ -250,7 +250,8 @@ def ex_Model(df, horizon, input_size, train_steps, val_check_steps, early_stop_p list.append({'Date': date, 'Value': value}) data['DataList'] = list data['IndexCode'] = modelsindex[m] - data['IndexName'] = f'价格预测{m}模型' + # data['IndexName'] = f'价格预测{m}模型' + data['IndexName'] = data['IndexName'].replece('xx', m) data['Remark'] = m etadata.push_data(data) @@ -463,7 +464,7 @@ def ex_Model_Juxiting(df, horizon, input_size, train_steps, val_check_steps, ear if config.is_update_eta: df_predict['ds'] = pd.to_datetime(df_predict['ds']) dates = df_predict['ds'].dt.strftime('%Y-%m-%d') - + for m in modelsindex.keys(): try: list = [] @@ -1247,7 +1248,6 @@ def model_losss(sqlitedb, end_time): _get_accuracy_rate(df, create_dates, ds_dates) except Exception as e: config.logger.info(f'准确率计算错误{e}') - def _add_abs_error_rate(): # 计算每个预测值与真实值之间的偏差率 @@ -1822,7 +1822,8 @@ def model_losss_juxitingbak(sqlitedb, end_time): plt.text(i, j, str(j), ha='center', va='bottom') # 当前日期画竖虚线 - plt.axvline(x=df['ds'].iloc[-config.horizon], color='r', linestyle='--') + plt.axvline(x=df['ds'].iloc[-config.horizon], + color='r', linestyle='--') plt.legend() plt.xlabel('日期') # 自动设置横轴日期显示 @@ -2270,7 +2271,7 @@ def brent_export_pdf(num_indicators=475, num_models=21, num_dayindicator=202, in config.dataset, 'model_evaluation.csv'), encoding='utf-8') # 按评估指标排序，取前五 fivemodels_list = eval_df['模型(Model)'].values # 列表形式，后面当作列名索引使用 - eval_models_list = fivemodels_list.tolist() + eval_models_list = fivemodels_list.tolist() # 去掉FEDformer if 'FEDformer' in eval_models_list: eval_models_list.remove('FEDformer') @@ -2356,7 +2357,8 @@ def brent_export_pdf(num_indicators=475, num_models=21, num_dayindicator=202, in content.append(Graphs.draw_little_title('模型选择：')) 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：')) - content.append(Graphs.draw_text(f'使用{config.input_size}个数据预测未来{inputsize}个数据。')) + content.append(Graphs.draw_text( + f'使用{config.input_size}个数据预测未来{inputsize}个数据。')) content.append(Graphs.draw_little_title('指标情况：')) with open(os.path.join(config.dataset, '特征频度统计.txt'), encoding='utf-8') as f: for line in f.readlines():