regx json fullpath
作者：互联网
from collections import OrderedDict
import jsonpath_rw
import json
from future.moves import collections
A={"c":"ddd","app":999,"phone":18676743129,"user":"张胜男","tr":{"last":{"need":False}}}
order=OrderedDict()
# print(sorted(A.items(),key=lambda x:x[0],reverse=True))
def order_define(o):
if isinstance(o,dict):
orders=dict(sorted(o.items(),key=lambda x:x[0],reverse=True))
print(orders)
# data = {
# "glossary": {
# "title": "example glossary",
# "GlossDiv": {
# "title": "S",
# "GlossList": {
# "GlossEntry": {
# "ID": "SGML",
# "SortAs": "SGML",
# "GlossTerm": "Standard",
# "Acronym": "SGML",
# "Abbrev": "ISO ",
# "GlossDef": {
# "para": "DocBook.",
# "GlossSeeAlso": ["GML", "XML"]
# },
# "GlossSee": "markup"
# }
# }
# }
# }
# }
data={
"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
}
}
}
}
import collections
import warnings
warnings.filterwarnings("ignore")
def get_paths(source):
paths = []
if isinstance(source, collections.MutableMapping): # found a dict-like structure...
for k, v in source.items(): # iterate over it; Python 2.x: source.iteritems()
paths.append([k]) # add the current child path
paths += [[k] + x for x in get_paths(v)] # get sub-paths, extend with the current
# else, check if a list-like structure, remove if you don't want list paths included
elif isinstance(source, collections.Sequence) and not isinstance(source, str):
# Python 2.x: use basestring instead of str ^
for i, v in enumerate(source):
paths.append([i])
paths += [[i] + x for x in get_paths(v)] # get sub-paths, extend with the current
return paths
def cast_full_path(path_list,dictvariable_name:str):
path=""
for i in path_list:
if isinstance(i,int):
path=path+'[{}]'.format(i)
else:
path =path+"['%s']"%i
full_path= path
return full_path
def str_path(data):
path_str=[]
paths=get_paths(data)
for i in paths:
res='.'.join([str(j) for j in i])
path_str.append(res)
return path_str
# for i in path_str:
# print(i)
def full_path_list(quantile_list,dictvar_name):
paths_list=[]
for i in quantile_list:
v=cast_full_path(i,"%s"%dictvar_name)
paths_list.append(v)
return paths_list
def judge_path(key:str,regx_index=-1):
res=[]
it=full_path_list(get_paths(data),"data")
for i in it:
var=i.replace('[','.').replace(']','.').strip('.').replace('..','.').replace("'",'').split('.')
if key==var[-1]:
print("matchs join strpath is:\n %s" % '.'.join(var))
res.append(i)
if regx_index !=-1:
print("last return:\n%s "%res[regx_index])
return res[regx_index]
elif regx_index==-1:
print("last return:\n%s " % res)
return res
if __name__ == '__main__':
# k=full_path_list(get_paths(data),"data")
# for i in k:
# print(i)
judge_path("name")
# st="['role_parameters']['guest']['hetero_feature_selection_0']['iv_value_param']['value_threshold'][0]"
# aa=st.replace('[','.').replace(']','.').strip('.').replace('..','.').replace("'",'').split('.')
.
标签：paths,regx,list,param,json,value,path,fullpath,data 来源： https://www.cnblogs.com/SunshineKimi/p/11808167.html