Evaluate.py

import MachineLearning.ML_Tools.log
import MachineLearning.ML_Tools.sigmond
import DataManager.GetRepoInfo
import Get_Model
import config
import json

def get_repo_info(repo_name):
     repo_dic={}
     repo_dic=DataManager.GetRepoInfo.GetRepoInfo(repo_name)
     repo_dic=MachineLearning.ML_Tools.log.loge(repo_dic)
     return repo_dic

def read_model():
   fr=open(config.dataset_root+'/model.txt')
   dic={}
   for i in fr.readlines():
	  	dic[i.split('\t')[0].strip()]=float(i.split('\t')[1].strip())
    
   return dic


def evaluate(repo_name):
	dic_model=read_model()
	dic_target=get_repo_info(repo_name)
	factor=dic_model['constant'] 
	for i in config.feat:
	   if i =='name':
	    continue
	   else:  	
		factor=factor+dic_model[i]*dic_target[i]
	return factor

def get_score(repo_name):
     ful_name='https://api.github.com/repos/'+repo_name
     return (MachineLearning.ML_Tools.sigmond.sig((evaluate(ful_name))))*100

def call_back_json(repo_name):
     dic={
       'name': '%s'%(repo_name),
       'score':'%f'%(get_score(repo_name))

     }
     return json.dumps(dic)


if __name__ == '__main__':
	print call_back_json('jimenbian/DataMining')