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 } } } }