{"description":"实验创建于2017/8/26","graph":{"edges":[{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-15:instruments","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8:data"},{"to_node_id":"-215:instruments","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data1","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-15:data"},{"to_node_id":"-215:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"-222:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"-231:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"-238:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"-669:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"-423:input_data","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data"},{"to_node_id":"-231:instruments","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"to_node_id":"-222:input_data","from_node_id":"-215:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data2","from_node_id":"-222:data"},{"to_node_id":"-238:input_data","from_node_id":"-231:data"},{"to_node_id":"-427:input_data","from_node_id":"-238:data"},{"to_node_id":"-669:training_ds","from_node_id":"-423:data"},{"to_node_id":"-669:predict_ds","from_node_id":"-427:data"},{"to_node_id":"-110:predictions","from_node_id":"-669:predictions"}],"nodes":[{"node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8","module_id":"BigQuantSpace.instruments.instruments-v2","parameters":[{"name":"start_date","value":"2011-01-01","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2020-12-31","type":"Literal","bound_global_parameter":null},{"name":"market","value":"CN_STOCK_A","type":"Literal","bound_global_parameter":null},{"name":"instrument_list","value":"","type":"Literal","bound_global_parameter":null},{"name":"max_count","value":"0","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"rolling_conf","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8"}],"output_ports":[{"name":"data","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8"}],"cacheable":true,"seq_num":1,"comment":"","comment_collapsed":true},{"node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-15","module_id":"BigQuantSpace.advanced_auto_labeler.advanced_auto_labeler-v2","parameters":[{"name":"label_expr","value":"\nshift(close, -5) / shift(open, -1)\n\n\nclip(label, all_quantile(label, 0.01), all_quantile(label, 0.99))\n\n\nall_wbins(label, 20)\n\n\nwhere(shift(high, -1) == shift(low, -1), NaN, label)\n","type":"Literal","bound_global_parameter":null},{"name":"start_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"benchmark","value":"000300.SHA","type":"Literal","bound_global_parameter":null},{"name":"drop_na_label","value":"True","type":"Literal","bound_global_parameter":null},{"name":"cast_label_int","value":"False","type":"Literal","bound_global_parameter":null},{"name":"user_functions","value":"","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-15"}],"output_ports":[{"name":"data","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-15"}],"cacheable":true,"seq_num":2,"comment":"","comment_collapsed":true},{"node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24","module_id":"BigQuantSpace.input_features.input_features-v1","parameters":[{"name":"features","value":"# (close_0-mean(close_0,12))/mean(close_0,12)*100\n# rank(std(amount_0,15))\n# rank_avg_amount_0/rank_avg_amount_8\n# ts_argmin(low_0,20)\n# rank_return_30\n# (low_1-close_0)/close_0\n# ta_bbands_lowerband_14_0\n# mean(mf_net_pct_s_0,4)\n# amount_0/avg_amount_3\n# return_0/return_5\n# return_1/return_5\n# rank_avg_amount_7/rank_avg_amount_10\n# ta_sma_10_0/close_0\n# sqrt(high_0*low_0)-amount_0/volume_0*adjust_factor_0\n# avg_turn_15/(turn_0+1e-5)\n# return_10\n# mf_net_pct_s_0\n# (close_0-open_0)/close_1\nreturn_5\nreturn_10\nreturn_20\navg_amount_0/avg_amount_5\navg_amount_5/avg_amount_20\nrank_avg_amount_0/rank_avg_amount_5\nrank_avg_amount_5/rank_avg_amount_10\nrank_return_0\nrank_return_5\nrank_return_10\nrank_return_0/rank_return_5\nrank_return_5/rank_return_10\npe_ttm_0\nlabel=log(close)","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24"}],"output_ports":[{"name":"data","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24"}],"cacheable":true,"seq_num":3,"comment":"","comment_collapsed":true},{"node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53","module_id":"BigQuantSpace.join.join-v3","parameters":[{"name":"on","value":"date,instrument","type":"Literal","bound_global_parameter":null},{"name":"how","value":"inner","type":"Literal","bound_global_parameter":null},{"name":"sort","value":"False","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"data1","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53"},{"name":"data2","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53"}],"output_ports":[{"name":"data","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53"}],"cacheable":true,"seq_num":7,"comment":"","comment_collapsed":true},{"node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62","module_id":"BigQuantSpace.instruments.instruments-v2","parameters":[{"name":"start_date","value":"2021-01-01","type":"Literal","bound_global_parameter":"交易日期"},{"name":"end_date","value":"2021-12-31","type":"Literal","bound_global_parameter":"交易日期"},{"name":"market","value":"CN_STOCK_A","type":"Literal","bound_global_parameter":null},{"name":"instrument_list","value":"","type":"Literal","bound_global_parameter":null},{"name":"max_count","value":"0","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"rolling_conf","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62"}],"output_ports":[{"name":"data","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62"}],"cacheable":true,"seq_num":9,"comment":"预测数据，用于回测和模拟","comment_collapsed":false},{"node_id":"-215","module_id":"BigQuantSpace.general_feature_extractor.general_feature_extractor-v7","parameters":[{"name":"start_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"before_start_days","value":0,"type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-215"},{"name":"features","node_id":"-215"}],"output_ports":[{"name":"data","node_id":"-215"}],"cacheable":true,"seq_num":15,"comment":"","comment_collapsed":true},{"node_id":"-222","module_id":"BigQuantSpace.derived_feature_extractor.derived_feature_extractor-v3","parameters":[{"name":"date_col","value":"date","type":"Literal","bound_global_parameter":null},{"name":"instrument_col","value":"instrument","type":"Literal","bound_global_parameter":null},{"name":"drop_na","value":"False","type":"Literal","bound_global_parameter":null},{"name":"remove_extra_columns","value":"False","type":"Literal","bound_global_parameter":null},{"name":"user_functions","value":"","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-222"},{"name":"features","node_id":"-222"}],"output_ports":[{"name":"data","node_id":"-222"}],"cacheable":true,"seq_num":16,"comment":"","comment_collapsed":true},{"node_id":"-231","module_id":"BigQuantSpace.general_feature_extractor.general_feature_extractor-v7","parameters":[{"name":"start_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"before_start_days","value":0,"type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-231"},{"name":"features","node_id":"-231"}],"output_ports":[{"name":"data","node_id":"-231"}],"cacheable":true,"seq_num":17,"comment":"","comment_collapsed":true},{"node_id":"-238","module_id":"BigQuantSpace.derived_feature_extractor.derived_feature_extractor-v3","parameters":[{"name":"date_col","value":"date","type":"Literal","bound_global_parameter":null},{"name":"instrument_col","value":"instrument","type":"Literal","bound_global_parameter":null},{"name":"drop_na","value":"False","type":"Literal","bound_global_parameter":null},{"name":"remove_extra_columns","value":"False","type":"Literal","bound_global_parameter":null},{"name":"user_functions","value":"","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-238"},{"name":"features","node_id":"-238"}],"output_ports":[{"name":"data","node_id":"-238"}],"cacheable":true,"seq_num":18,"comment":"","comment_collapsed":true},{"node_id":"-423","module_id":"BigQuantSpace.dropnan.dropnan-v2","parameters":[],"input_ports":[{"name":"input_data","node_id":"-423"},{"name":"features","node_id":"-423"}],"output_ports":[{"name":"data","node_id":"-423"}],"cacheable":true,"seq_num":5,"comment":"","comment_collapsed":true},{"node_id":"-427","module_id":"BigQuantSpace.dropnan.dropnan-v2","parameters":[],"input_ports":[{"name":"input_data","node_id":"-427"},{"name":"features","node_id":"-427"}],"output_ports":[{"name":"data","node_id":"-427"}],"cacheable":true,"seq_num":6,"comment":"","comment_collapsed":true},{"node_id":"-669","module_id":"BigQuantSpace.extra_trees_regressor.extra_trees_regressor-v1","parameters":[{"name":"criterion","value":"mse","type":"Literal","bound_global_parameter":null},{"name":"iterations","value":10,"type":"Literal","bound_global_parameter":null},{"name":"feature_fraction","value":1,"type":"Literal","bound_global_parameter":null},{"name":"max_depth","value":30,"type":"Literal","bound_global_parameter":null},{"name":"min_samples_per_leaf","value":200,"type":"Literal","bound_global_parameter":null},{"name":"key_cols","value":"date,instrument","type":"Literal","bound_global_parameter":null},{"name":"workers","value":1,"type":"Literal","bound_global_parameter":null},{"name":"random_state","value":0,"type":"Literal","bound_global_parameter":null},{"name":"other_train_parameters","value":"{}","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"training_ds","node_id":"-669"},{"name":"features","node_id":"-669"},{"name":"model","node_id":"-669"},{"name":"predict_ds","node_id":"-669"}],"output_ports":[{"name":"output_model","node_id":"-669"},{"name":"predictions","node_id":"-669"}],"cacheable":true,"seq_num":8,"comment":"","comment_collapsed":true},{"node_id":"-110","module_id":"BigQuantSpace.metrics_regression.metrics_regression-v1","parameters":[{"name":"explained_variance_score","value":"True","type":"Literal","bound_global_parameter":null},{"name":"mean_absolute_error","value":"True","type":"Literal","bound_global_parameter":null},{"name":"mean_squared_error","value":"True","type":"Literal","bound_global_parameter":null},{"name":"mean_squared_log_error","value":"True","type":"Literal","bound_global_parameter":null},{"name":"median_absolute_error","value":"True","type":"Literal","bound_global_parameter":null},{"name":"r2_score","value":"True","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"predictions","node_id":"-110"}],"output_ports":[{"name":"report","node_id":"-110"}],"cacheable":false,"seq_num":4,"comment":"","comment_collapsed":true}],"node_layout":"<node_postions><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-8' Position='211,64,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-15' Position='81,227,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-24' Position='765,19,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-53' Position='249,375,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-62' Position='1088,20,200,200'/><node_position Node='-215' Position='381,188,200,200'/><node_position Node='-222' Position='385,280,200,200'/><node_position Node='-231' Position='1002,202,200,200'/><node_position Node='-238' Position='1004,299,200,200'/><node_position Node='-423' Position='387,439,200,200'/><node_position Node='-427' Position='1034,404,200,200'/><node_position Node='-669' Position='556,619,200,200'/><node_position Node='-110' Position='650,765,200,200'/></node_postions>"},"nodes_readonly":false,"studio_version":"v2"}

# 本代码由可视化策略环境自动生成 2023年4月24日 10:15
# 本代码单元只能在可视化模式下编辑。您也可以拷贝代码，粘贴到新建的代码单元或者策略，然后修改。


m1 = M.instruments.v2(
    start_date='2011-01-01',
    end_date='2020-12-31',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m2 = M.advanced_auto_labeler.v2(
    instruments=m1.data,
    label_expr="""
shift(close, -5) / shift(open, -1)


clip(label, all_quantile(label, 0.01), all_quantile(label, 0.99))


all_wbins(label, 20)


where(shift(high, -1) == shift(low, -1), NaN, label)
""",
    start_date='',
    end_date='',
    benchmark='000300.SHA',
    drop_na_label=True,
    cast_label_int=False
)

m3 = M.input_features.v1(
    features="""# (close_0-mean(close_0,12))/mean(close_0,12)*100
# rank(std(amount_0,15))
# rank_avg_amount_0/rank_avg_amount_8
# ts_argmin(low_0,20)
# rank_return_30
# (low_1-close_0)/close_0
# ta_bbands_lowerband_14_0
# mean(mf_net_pct_s_0,4)
# amount_0/avg_amount_3
# return_0/return_5
# return_1/return_5
# rank_avg_amount_7/rank_avg_amount_10
# ta_sma_10_0/close_0
# sqrt(high_0*low_0)-amount_0/volume_0*adjust_factor_0
# avg_turn_15/(turn_0+1e-5)
# return_10
# mf_net_pct_s_0
# (close_0-open_0)/close_1
return_5
return_10
return_20
avg_amount_0/avg_amount_5
avg_amount_5/avg_amount_20
rank_avg_amount_0/rank_avg_amount_5
rank_avg_amount_5/rank_avg_amount_10
rank_return_0
rank_return_5
rank_return_10
rank_return_0/rank_return_5
rank_return_5/rank_return_10
pe_ttm_0
label=log(close)"""
)

m15 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=0
)

m16 = M.derived_feature_extractor.v3(
    input_data=m15.data,
    features=m3.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False
)

m7 = M.join.v3(
    data1=m2.data,
    data2=m16.data,
    on='date,instrument',
    how='inner',
    sort=False
)

m5 = M.dropnan.v2(
    input_data=m7.data
)

m9 = M.instruments.v2(
    start_date=T.live_run_param('trading_date', '2021-01-01'),
    end_date=T.live_run_param('trading_date', '2021-12-31'),
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m17 = M.general_feature_extractor.v7(
    instruments=m9.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=0
)

m18 = M.derived_feature_extractor.v3(
    input_data=m17.data,
    features=m3.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False
)

m6 = M.dropnan.v2(
    input_data=m18.data
)

m8 = M.extra_trees_regressor.v1(
    training_ds=m5.data,
    features=m3.data,
    predict_ds=m6.data,
    criterion='mse',
    iterations=10,
    feature_fraction=1,
    max_depth=30,
    min_samples_per_leaf=200,
    key_cols='date,instrument',
    workers=1,
    random_state=0,
    other_train_parameters={}
)

m4 = M.metrics_regression.v1(
    predictions=m8.predictions,
    explained_variance_score=True,
    mean_absolute_error=True,
    mean_squared_error=True,
    mean_squared_log_error=True,
    median_absolute_error=True,
    r2_score=True
)

[2023-04-24 09:40:40.996239] INFO: moduleinvoker: instruments.v2 开始运行..

[2023-04-24 09:40:41.011246] INFO: moduleinvoker: 命中缓存

[2023-04-24 09:40:41.012599] INFO: moduleinvoker: instruments.v2 运行完成[0.016376s].

[2023-04-24 09:40:41.022982] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..

[2023-04-24 09:40:47.355515] INFO: 自动标注(股票): 加载历史数据: 6813532 行

[2023-04-24 09:40:47.356951] INFO: 自动标注(股票): 开始标注 ..

[2023-04-24 09:40:55.060865] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[14.037876s].

[2023-04-24 09:40:55.069172] INFO: moduleinvoker: input_features.v1 开始运行..

[2023-04-24 09:40:55.087169] INFO: moduleinvoker: 命中缓存

[2023-04-24 09:40:55.088760] INFO: moduleinvoker: input_features.v1 运行完成[0.019587s].

[2023-04-24 09:40:55.106680] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2023-04-24 09:40:56.277193] INFO: 基础特征抽取: 年份 2011, 特征行数=511455

[2023-04-24 09:40:57.435339] INFO: 基础特征抽取: 年份 2012, 特征行数=565675

[2023-04-24 09:40:58.637294] INFO: 基础特征抽取: 年份 2013, 特征行数=564168

[2023-04-24 09:40:59.820964] INFO: 基础特征抽取: 年份 2014, 特征行数=569948

[2023-04-24 09:41:01.222790] INFO: 基础特征抽取: 年份 2015, 特征行数=569698

[2023-04-24 09:41:02.749511] INFO: 基础特征抽取: 年份 2016, 特征行数=641546

[2023-04-24 09:41:04.560060] INFO: 基础特征抽取: 年份 2017, 特征行数=743233

[2023-04-24 09:41:06.454576] INFO: 基础特征抽取: 年份 2018, 特征行数=816987

[2023-04-24 09:41:08.604389] INFO: 基础特征抽取: 年份 2019, 特征行数=884867

[2023-04-24 09:41:10.881602] INFO: 基础特征抽取: 年份 2020, 特征行数=945961

[2023-04-24 09:41:11.016330] INFO: 基础特征抽取: 总行数: 6813538

[2023-04-24 09:41:11.032025] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[15.925344s].

[2023-04-24 09:41:11.044116] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2023-04-24 09:41:21.552492] INFO: derived_feature_extractor: 提取完成 avg_amount_0/avg_amount_5, 0.018s

[2023-04-24 09:41:21.573775] INFO: derived_feature_extractor: 提取完成 avg_amount_5/avg_amount_20, 0.019s

[2023-04-24 09:41:21.584312] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_0/rank_avg_amount_5, 0.009s

[2023-04-24 09:41:21.594846] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_5/rank_avg_amount_10, 0.009s

[2023-04-24 09:41:21.605603] INFO: derived_feature_extractor: 提取完成 rank_return_0/rank_return_5, 0.009s

[2023-04-24 09:41:21.635124] INFO: derived_feature_extractor: 提取完成 rank_return_5/rank_return_10, 0.028s

[2023-04-24 09:41:24.230583] INFO: derived_feature_extractor: /y_2011, 511455

[2023-04-24 09:41:25.153110] INFO: derived_feature_extractor: /y_2012, 565675

[2023-04-24 09:41:26.108328] INFO: derived_feature_extractor: /y_2013, 564168

[2023-04-24 09:41:27.071559] INFO: derived_feature_extractor: /y_2014, 569948

[2023-04-24 09:41:28.054961] INFO: derived_feature_extractor: /y_2015, 569698

[2023-04-24 09:41:29.186223] INFO: derived_feature_extractor: /y_2016, 641546

[2023-04-24 09:41:30.544183] INFO: derived_feature_extractor: /y_2017, 743233

[2023-04-24 09:41:32.006659] INFO: derived_feature_extractor: /y_2018, 816987

[2023-04-24 09:41:33.498111] INFO: derived_feature_extractor: /y_2019, 884867

[2023-04-24 09:41:35.300912] INFO: derived_feature_extractor: /y_2020, 945961

[2023-04-24 09:41:36.177204] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[25.133079s].

[2023-04-24 09:41:36.188997] INFO: moduleinvoker: join.v3 开始运行..

[2023-04-24 09:41:51.197449] INFO: join: /y_2011, 行数=510922/511455, 耗时=3.211264s

[2023-04-24 09:41:57.242278] INFO: join: /y_2012, 行数=564582/565675, 耗时=6.038849s

[2023-04-24 09:42:02.126654] INFO: join: /y_2013, 行数=563137/564168, 耗时=4.879496s

[2023-04-24 09:42:06.332785] INFO: join: /y_2014, 行数=567874/569948, 耗时=4.201652s

[2023-04-24 09:42:10.366900] INFO: join: /y_2015, 行数=560428/569698, 耗时=4.029082s

[2023-04-24 09:42:14.831759] INFO: join: /y_2016, 行数=637478/641546, 耗时=4.460579s

[2023-04-24 09:42:19.411133] INFO: join: /y_2017, 行数=738259/743233, 耗时=4.573779s

[2023-04-24 09:42:24.622064] INFO: join: /y_2018, 行数=813508/816987, 耗时=5.204787s

[2023-04-24 09:42:29.676081] INFO: join: /y_2019, 行数=881288/884867, 耗时=5.048191s

[2023-04-24 09:42:38.082754] INFO: join: /y_2020, 行数=919362/945961, 耗时=8.40054s

[2023-04-24 09:42:38.218124] INFO: join: 最终行数: 6756838

[2023-04-24 09:42:38.293025] INFO: moduleinvoker: join.v3 运行完成[62.104006s].

[2023-04-24 09:42:38.317643] INFO: moduleinvoker: dropnan.v2 开始运行..

[2023-04-24 09:42:41.043701] INFO: dropnan: /y_2011, 504726/510922

[2023-04-24 09:42:42.306541] INFO: dropnan: /y_2012, 561109/564582

[2023-04-24 09:42:43.739080] INFO: dropnan: /y_2013, 563107/563137

[2023-04-24 09:42:44.995382] INFO: dropnan: /y_2014, 566035/567874

[2023-04-24 09:42:46.614245] INFO: dropnan: /y_2015, 558152/560428

[2023-04-24 09:42:48.190065] INFO: dropnan: /y_2016, 635618/637478

[2023-04-24 09:42:50.861463] INFO: dropnan: /y_2017, 732602/738259

[2023-04-24 09:42:54.026851] INFO: dropnan: /y_2018, 811828/813508

[2023-04-24 09:42:56.639809] INFO: dropnan: /y_2019, 877946/881288

[2023-04-24 09:42:59.581676] INFO: dropnan: /y_2020, 911045/919362

[2023-04-24 09:42:59.790206] INFO: dropnan: 行数: 6722168/6756838

[2023-04-24 09:42:59.799688] INFO: moduleinvoker: dropnan.v2 运行完成[21.482046s].

[2023-04-24 09:42:59.807859] INFO: moduleinvoker: instruments.v2 开始运行..

[2023-04-24 09:42:59.826119] INFO: moduleinvoker: 命中缓存

[2023-04-24 09:42:59.827798] INFO: moduleinvoker: instruments.v2 运行完成[0.019941s].

[2023-04-24 09:42:59.842866] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2023-04-24 09:42:59.854143] INFO: moduleinvoker: 命中缓存

[2023-04-24 09:42:59.855700] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.012843s].

[2023-04-24 09:42:59.862908] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2023-04-24 09:42:59.871665] INFO: moduleinvoker: 命中缓存

[2023-04-24 09:42:59.873483] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.010569s].

[2023-04-24 09:42:59.882599] INFO: moduleinvoker: dropnan.v2 开始运行..

[2023-04-24 09:43:02.240609] INFO: dropnan: /y_2021, 1049627/1061527

[2023-04-24 09:43:02.304380] INFO: dropnan: 行数: 1049627/1061527

[2023-04-24 09:43:02.314603] INFO: moduleinvoker: dropnan.v2 运行完成[2.432006s].

[2023-04-24 09:43:02.422269] INFO: moduleinvoker: extra_trees_regressor.v1 开始运行..

[2023-04-24 09:43:28.007160] INFO: moduleinvoker: extra_trees_regressor.v1 运行完成[25.584884s].

[2023-04-24 09:43:29.384440] ERROR: moduleinvoker: module name: metrics_regression, module version: v1, trackeback: ValueError: 训练数据中无label，无法评估模型

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-1-72a96ea7ca57> in <module>
    139 )
    140 
--> 141 m4 = M.metrics_regression.v1(
    142     predictions=m8.predictions,
    143     explained_variance_score=True,

ValueError: 训练数据中无label，无法评估模型