{"description":"实验创建于2018/6/27","graph":{"edges":[{"to_node_id":"-927:input_1","from_node_id":"-915:data"},{"to_node_id":"-949:input_1","from_node_id":"-927:data_1"},{"to_node_id":"-961:input_1","from_node_id":"-949:data_1"}],"nodes":[{"node_id":"-961","module_id":"BigQuantSpace.cached.cached-v3","parameters":[{"name":"run","value":"# Python 代码入口函数，input_1/2/3 对应三个输入端，data_1/2/3 对应三个输出端\ndef bigquant_run(input_1, input_2, input_3):\n # 示例代码如下。在这里编写您的代码\n \n tmp01=input_1.read_pickle()\n \n ad = {\n \"instruments\":tmp01,\n \"start_date\":\"2021-11-16\",\n \"end_date\":T.live_run_param('trading_date', \"2021-11-16\")\n #\"end_date\":\"2021-08-04\"\n }\n \n print(444444444444)\n # 这一段代码，结合超参搜索就会爆出错误\n # ERROR: moduleinvoker: module name: cached, module version: v3, trackeback: AssertionError: daemonic processes are not allowed to have children_\n # 在勾选 不显示日志之后，才会出现\n DataSource('bar5m_CN_STOCK_A').read(tmp01 ,start_date='2021-11-05',end_date='2021-11-16',fields=['close','volume'])\n print(333333333333)\n \n data_1 = DataSource.write_pickle(ad)\n return Outputs(data_1=data_1, data_2=None, data_3=None)\n","type":"Literal","bound_global_parameter":null},{"name":"post_run","value":"# 后处理函数，可选。输入是主函数的输出，可以在这里对数据做处理，或者返回更友好的outputs数据格式。此函数输出不会被缓存。\ndef bigquant_run(outputs):\n return 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# 本代码由可视化策略环境自动生成 2021年11月19日 16:55
# 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。


# Python 代码入口函数，input_1/2/3 对应三个输入端，data_1/2/3 对应三个输出端
def m3_run_bigquant_run(input_1, input_2, input_3):
    # 示例代码如下。在这里编写您的代码
    tmp01=input_1.read_pickle()
    
    df = pd.DataFrame()
    
    # print(tmp01['instruments'])
    
    df['instrument'] = tmp01['instruments']
    df['date'] = datetime.datetime.strptime(tmp01['start_date'], '%Y-%m-%d')
    
    data_1 = DataSource.write_df(df)
    return Outputs(data_1=data_1, data_2=None, data_3=None)

# 后处理函数，可选。输入是主函数的输出，可以在这里对数据做处理，或者返回更友好的outputs数据格式。此函数输出不会被缓存。
def m3_post_run_bigquant_run(outputs):
    return outputs

# Python 代码入口函数，input_1/2/3 对应三个输入端，data_1/2/3 对应三个输出端
def m5_run_bigquant_run(input_1, input_2, input_3):
    # 示例代码如下。在这里编写您的代码
#     df = pd.DataFrame({'data': [1, 2, 3]})
    
    tmp02=input_1.read_df()
    
    ins = tmp02['instrument'].values.tolist()
    
    data_1 = DataSource.write_pickle(ins)
    
    return Outputs(data_1=data_1, data_2=None, data_3=None)

# 后处理函数，可选。输入是主函数的输出，可以在这里对数据做处理，或者返回更友好的outputs数据格式。此函数输出不会被缓存。
def m5_post_run_bigquant_run(outputs):
    return outputs

# Python 代码入口函数，input_1/2/3 对应三个输入端，data_1/2/3 对应三个输出端
def m6_run_bigquant_run(input_1, input_2, input_3):
    # 示例代码如下。在这里编写您的代码
    
    tmp01=input_1.read_pickle()
    
    ad = {
    "instruments":tmp01,
    "start_date":"2021-11-16",
    "end_date":T.live_run_param('trading_date', "2021-11-16")
        #"end_date":"2021-08-04"
    }
    
    print(444444444444)
    # 这一段代码，结合超参搜索就会爆出错误
    # ERROR: moduleinvoker: module name: cached, module version: v3, trackeback: AssertionError: daemonic processes are not allowed to have children_
    # 在勾选 不显示日志之后，才会出现
    DataSource('bar5m_CN_STOCK_A').read(tmp01 ,start_date='2021-11-05',end_date='2021-11-16',fields=['close','volume'])
    print(333333333333)
    
    data_1 = DataSource.write_pickle(ad)
    return Outputs(data_1=data_1, data_2=None, data_3=None)

# 后处理函数，可选。输入是主函数的输出，可以在这里对数据做处理，或者返回更友好的outputs数据格式。此函数输出不会被缓存。
def m6_post_run_bigquant_run(outputs):
    return outputs


g = T.Graph({

    'm55': 'M.input_features.v1',
    'm55.features': 'b',

    'm30': 'M.input_features.v1',
    'm30.features': 'a',

    'm2': 'M.instruments.v2',
    'm2.start_date': '2017-01-01',
    'm2.end_date': '2021-08-17',
    'm2.market': 'CN_STOCK_A',
    'm2.instrument_list': '',
    'm2.max_count': 0,

    'm3': 'M.cached.v3',
    'm3.input_1': T.Graph.OutputPort('m2.data'),
    'm3.run': m3_run_bigquant_run,
    'm3.post_run': m3_post_run_bigquant_run,
    'm3.input_ports': '',
    'm3.params': '{}',
    'm3.output_ports': '',

    'm5': 'M.cached.v3',
    'm5.input_1': T.Graph.OutputPort('m3.data_1'),
    'm5.run': m5_run_bigquant_run,
    'm5.post_run': m5_post_run_bigquant_run,
    'm5.input_ports': '',
    'm5.params': '{}',
    'm5.output_ports': '',

    'm6': 'M.cached.v3',
    'm6.input_1': T.Graph.OutputPort('m5.data_1'),
    'm6.run': m6_run_bigquant_run,
    'm6.post_run': m6_post_run_bigquant_run,
    'm6.input_ports': '',
    'm6.params': '{}',
    'm6.output_ports': '',
})

# g.run({})


def m18_param_grid_builder_bigquant_run():
    param_grid = {}

    param_grid['m30.features'] =  [['a']]
    param_grid['m55.features'] =  [['a'],['b']]
    
    
    return param_grid
def m18_scoring_bigquant_run(result):
    score = result.get('m54').read_raw_perf()['algorithm_period_return'].dropna().tail(1)[0]
    return {'score':score}


m18 = M.hyper_parameter_search.v1(
    param_grid_builder=m18_param_grid_builder_bigquant_run,
    scoring=m18_scoring_bigquant_run,
    search_algorithm='网格搜索',
    search_iterations=1,
    workers=2,
    worker_distributed_run=False,
    worker_silent=True,
    run_now=True,
    bq_graph=g
)

[2021-11-19 16:55:44.422094] ERROR: moduleinvoker: module name: cached, module version: v3, trackeback: AssertionError: daemonic processes are not allowed to have children

[2021-11-19 16:55:44.423011] ERROR: moduleinvoker: module name: cached, module version: v3, trackeback: AssertionError: daemonic processes are not allowed to have children

[2021-11-19 16:55:44.449240] ERROR: moduleinvoker: module name: hyper_parameter_search, module version: v1, trackeback: RuntimeError: Cannot clone object , as the constructor either does not set or modifies parameter m30.features

[CV 1/1; 1/2] START m30.features=['a'], m55.features=['a']......................
[CV 1/1; 2/2] START m30.features=['a'], m55.features=['b']......................
444444444444444444444444

Fitting 1 folds for each of 2 candidates, totalling 2 fits
[Parallel(n_jobs=2)]: Using backend MultiprocessingBackend with 2 concurrent workers.
[CV 1/1; 2/2] END ....m30.features=['a'], m55.features=['b']; total time=   0.2s[CV 1/1; 1/2] END ....m30.features=['a'], m55.features=['a']; total time=   0.2s

[Parallel(n_jobs=2)]: Done   1 tasks      | elapsed:    0.2s
[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.2s remaining:    0.0s
[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.2s finished

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-194-4b43e2542103> in <module>
    123 
    124 
--> 125 m18 = M.hyper_parameter_search.v1(
    126     param_grid_builder=m18_param_grid_builder_bigquant_run,
    127     scoring=m18_scoring_bigquant_run,

RuntimeError: Cannot clone object <biglearning.module2.modules.hyper_parameter_search.v1.graphestimator.GraphEstimator object at 0x7f884900ab20>, as the constructor either does not set or modifies parameter m30.features