1.如何同时替换json多个指定key的value
import json
from jsonpath_ng import parse
def join_paths(regx_path,new_value,dict_replace):
"""
eg: join_paths(regx_path='$..host..namespace', new_value="9999999999", dict_replace=pydict)
:param regx_path: the path of replaced key
:param new_value: the new value of key to be replaced
:param dict_replace: the initial_dict that to be replaced
:return: dict
"""
data = dict_replace
jsonpath_expr = parse(regx_path)
str_path_list=[str(match.full_path) for match in jsonpath_expr.find(dict_replace)]
def cast_dict_path(path_list):
cast_list = []
for str_path in path_list:
path_split_list=str_path.split('.')
path = ''
for i in path_split_list:
if i.count('[')==1 and i.count(']')==1:
path=path+'[%s]'%eval(i)[0]
else:
path=path+"['%s']"%i
cast_list.append(path)
#[ "['role_parameters']['guest']['args']['data']['train_data'][0]['namespace']" ]
return cast_list
cast_paths=cast_dict_path(str_path_list)
for i in cast_paths:
if isinstance(new_value,str):
fullpath="data"+i+"='%s'"%new_value
abs_path=fullpath
exec(abs_path)
if isinstance(new_value,(int,list,float)):
fullpath = "data" + i + "={}".format(new_value)
abs_path=fullpath
exec(abs_path)
return data
def muti_replace(rep_list,initial_dict:dict):
"""
format of rep_list:
[
(regx_path1 ,new_value1) ],
(regx_path2 ,new_value2 )
]
for example:
>> final_dict=muti_replace([('$..hetero_lr_0..eps',0.7777),('$..host..namespace',8888888)],initial_dict=pydict)
initial_dict :the key need to replaced dict ,type dict
"""
dict_list=[]
for i in rep_list:
regx_path ,new_value=i[0],i[1]
dict_next=join_paths(regx_path,new_value,dict_replace=initial_dict)
dict_list.append(dict_next)
for k in dict_list:
initial_dict.update(k)
print(json.dumps(initial_dict,indent=5))
return initial_dict
if __name__ == '__main__':
final_dict=muti_replace([('$..hetero_lr_0..eps',0.7777),('$..host..namespace',8888888)],initial_dict=pydict)测试数据：
{
"initiator": {
"role": "guest",
"party_id":9997
},
"job_parameters": {
"work_mode": 1
},
"role": {
"guest": [
9997
],
"host": [
9997
],
"arbiter": [
9997
]
},
"role_parameters": {
"guest": {
"args": {
"data": {
"train_data": [
{
"name": "breast_guest",
"namespace": "breast_guest"
}
]
}
},
"dataio_0": {
"with_label": [true],
"label_name": ["y"],
"label_type": ["int"],
"output_format": ["dense"],
"missing_fill": [true],
"outlier_replace": [true]
},
"feature_scale_0": {
"method": ["min_max_scale"]
},
"hetero_feature_binning_0": {
"method": ["quantile"],
"compress_thres": [10000],
"head_size": [10000],
"error": [0.001],
"bin_num": [10],
"cols": [-1],
"adjustment_factor": [0.5],
"local_only": [false],
"transform_param": {
"transform_cols": [-1],
"transform_type": ["bin_num"]
}
},
"hetero_feature_selection_0": {
"select_cols": [-1],
"filter_methods": [[
"unique_value",
"iv_value_thres",
"coefficient_of_variation_value_thres",
"iv_percentile",
"outlier_cols"
]],
"local_only": [false],
"unique_param": {
"eps": [1e-6]
},
"iv_value_param": {
"value_threshold": [1.0]
},
"iv_percentile_param": {
"percentile_threshold": [0.9]
},
"variance_coe_param": {
"value_threshold": [0.3]
},
"outlier_param": {
"percentile": [0.95],
"upper_threshold": [10]
}
},
"evaluation_0": {
"eval_type": ["binary"],
"pos_label": [1]
}
},
"host": {
"args": {
"data": {
"train_data": [
{
"name": "breast_host",
"namespace": "breast_host"
}
]
}
},
"dataio_0": {
"with_label": [false],
"output_format": ["dense"],
"outlier_replace": [true]
},
"feature_scale_0": {
"method": ["standard_scale"],
"need_run": [false]
},
"hetero_feature_binning_0": {
"method": ["quantile"],
"compress_thres": [10000],
"head_size": [10000],
"error": [0.001],
"bin_num": [10],
"cols": [-1],
"adjustment_factor": [0.5],
"local_only": [false],
"transform_param": {
"transform_cols": [-1],
"transform_type": ["bin_num"]
}
},
"hetero_feature_selection_0": {
"select_cols": [-1],
"filter_methods": [[
"unique_value",
"iv_value_thres",
"coefficient_of_variation_value_thres",
"iv_percentile",
"outlier_cols"
]],
"local_only": [false],
"unique_param": {
"eps": [1e-6]
},
"iv_value_param": {
"value_threshold": [1.0]
},
"iv_percentile_param": {
"percentile_threshold": [0.9]
},
"variance_coe_param": {
"value_threshold": [0.3]
},
"outlier_param": {
"percentile": [0.95],
"upper_threshold": [10]
}
},
"evaluation_0": {
"need_run": [true]
}
}
},
"algorithm_parameters": {
"feature_scale_0": {
"need_run": true
},
"hetero_feature_binning_0": {
"need_run": true
},
"hetero_feature_selection_0": {
"need_run": true
},
"hetero_lr_0": {
"penalty": "L2",
"optimizer": "rmsprop",
"eps": 1e-5,
"alpha": 0.01,
"max_iter": 10,
"converge_func": "diff",
"batch_size": -1,
"learning_rate": 0.15,
"init_param": {
"init_method": "random_uniform"
},
"cv_param": {
"n_splits": 5,
"shuffle": false,
"random_seed": 103,
"need_cv": false
}
}
}
}View Code
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