for newsid in l: rank = 100 - int(newsid[8::]) if rank < 0: print newsid newistimedic[newsid] = (newsid[4:8] + newsid[0:4] + str(rank)) l = [] for newsid,_ in sorted(newistimedic.items(), key=lambda x:x[1]): l.append(newsid) """ from IIalgorithm_model import create_thread, create_toptarget_dic_newl_dic, maketext, maketextdoc,makedocvec,PredictAndAnalyze2,PredictAndAnalyze3 #for folda in ['topnewstextswithtag/']: thread2014 = create_thread('../getruiternewsfromweb/topnewstextswithtag/') thread2015 = create_thread('../getruiternewsfromweb/topnewstextswithtag2015/') thread2013 = create_thread('../getruiternewsfromweb/topnewstextswithtag2013/') thread = thread2013 thread.update(thread2014) thread.update(thread2015) import pandas as pd import datetime toptarget_dic, newl_dic = create_toptarget_dic_newl_dic(l,stockvaluedict, thread, thred_value = 0.01) DimentionN = 500 #DimentionN = 50 word2vecdic = pickle.load(open("../getruiternewsfromweb/word2vecdic_10.dump","r"))
#toptarget.append(stockvaluedict[newsid]['tag']) try: #print len(stockvaluedict[newsid]['value'][stockvaluedict[newsid]['ID'][0]]) ID = stockvaluedict[newsid]['ID'][0] if ((".T" in ID) & (newsid not in nikkeiheikinlist) & (len(stockvaluedict[newsid]['value'][stockvaluedict[newsid]['ID'][0]]) == 3)): #if (len(stockvaluedict[newsid]['value'][stockvaluedict[newsid]['ID'][0]]) == 3): #print newsid toptarget.append(stockvaluedict[newsid]['tag'][stockvaluedict[newsid]['ID'][0]]) newl.append(newsid) except: excpetl.append(ID) continue """ from IIalgorithm_model import create_thread, create_toptarget_dic_newl_dic, maketext, maketextdoc,makedocvec thread2013 = create_thread('topnewstextswithtag/') thread2014 = create_thread('topnewstextswithtag2015/') thread2015 = create_thread('topnewstextswithtag2013/') thread = thread2013 thread.update(thread2014) thread.update(thread2015) toptarget_dic, newl_dic = create_toptarget_dic_newl_dic(l,stockvaluedict, thread, thred_value = 0.01) dic_key = 'close_previousday_to_close_nextday' newl = np.array(newl_dic[dic_key]) toptarget = np.array(toptarget_dic[dic_key]) #toptarget = np.array(toptarget) topvectorMat2 = [] #newl = pickle.load(open("l_balanced_2014.dump","r")) #for newsid in l: