ngrammes_fit_index.append(clique_index)
print " + liste des index des concepts creee"
for aut,clique in ngrammes_auteurs_fit.iteritems():
for terme in set(clique):
dictionnaire_frequence_exact_auteur[terme]=dictionnaire_frequence_exact_auteur[terme]+1
print " + liste des index des concepts creee"
file_freq_exact = path_req +'years/'+ requete +str(year) + '_' + 'frequences_exactes.csv'
fichier_out =file(file_freq_exact,'w')
def format(value):
return "%.9f" % value
for x in dictionnaire_frequence_exact:
# print str(x) + '\t' + str(correspondance_lemme_forme[x]) +'\t' + (str(format(float(dictionnaire_frequence_exact[x])/N))).replace('.',',') +'\t' + (str(format(float(dictionnaire_frequence_exact_auteur[x])/float(Nb_auteurs)))).replace('.',',')+ '\n'
fichier_out.write(str(x) + '\t' + str(correspondance_lemme_forme[x]) +'\t' + (str(format(float(dictionnaire_frequence_exact[x])/N))).replace('.',',') +'\t' + (str(format(float(dictionnaire_frequence_exact_auteur[x])/float(Nb_auteurs)))).replace('.',',')+ '\n')
print " + frequences exactes calculees "+ file_freq_exact
try:
os.mkdir(path_req +'years/')
except:
pass
pool_size = int(multiprocessing.cpu_count())
pool = multiprocessing.Pool(processes=pool_size)
pool.map(freq_comp, years)
fusion_years.fusion('freq')
dictionnaire_gramme_year[y]=x
fonctions_lib.dumpingin(dictionnaire_gramme_year,name_export_pkl,requete)
#decoupage par periode:
print dictionnaire_gramme_year.keys()
#puis on itere annee par annee
try:
os.mkdir(path_req +'years/')
except:
pass
for y,year in enumerate(years):
#on trie par fréquence et on exporte le lexique final avec les occurrences
print '\n'
print year
dico_final = misc.freq_tri(dictionnaire_gramme_year[y],freqmin,int(math.floor(top*1.1)),language,ng_filter)#on effectue le tri de notre dictionnaire
filename = path_req +'years/'+ requete + '_' + str(freqmin) + '_' +str(year) + '_'+ 'liste_n-grammes_freq_divers.csv'
filename_redond = path_req +'years/'+ requete + '_' + str(freqmin) +str(year) + '_'+ 'liste_n-grammes_freq_divers_noredond.csv'
filename_redond_leven = path_req +'years/'+ requete + '_' + str(freqmin)+str(year) + '_' 'liste_n-grammes_freq_divers_leven_noredond.csv'
misc.ecrire_liste_lemmes_freq(dico_final,Nb_rows,filename,lemme_maj,freqmin,ng_filter)#on ecrit la liste precedente dans un fichier filename
print "\n+++"+str(len(dico_final))+" n-lemmes crees."
#leven.pack_rendondance(filename,filename_redond,maxTermLength,freqmin,language,redondance_manuelle,ng_filter,user_interface)
leven.pack_rendondance_exact(filename,filename_redond,maxTermLength,freqmin,language,ng_filter,user_interface)
print "\n"
Nb_rows = fonctions_bdd.count_rows_where(name_bdd,'billets'," where jours IN ('" + "','".join(list(map(str,year))) + "') ")
print Nb_rows
leven.pack_leven(filename_redond,filename_redond_leven,language,user_interface,freqmin,Nb_rows)
fusion_years.fusion('redond')
#
# l = distribution_distance.items()
# l.sort(key=itemgetter(1),reverse=True)
# dico_final_top={}
#
# synonymes_potentiels = open(path_req + 'synonymes.txt','w')
#
# for x in l[:10000]:
# couple=x[0]
# #if p_cooccurrences[(couple[0],couple[0],0)]*NN>freqmin and p_cooccurrences[(couple[1],couple[1],0)]*NN>freqmin:
# #print dico_termes[couple[0]] + '\t'+dico_termes[couple[1]] + '\t' + str(float(distribution_distance[couple]))
# synonymes_potentiels.write(dico_termes[couple[0]] + '\t'+dico_termes[couple[1]] + '\t' + str(float(distribution_distance[couple])) + '\n')
#
# timeapres = timeavt
# timeavt = time()
# print 'duree de la derniere etape : ' + str(timeavt-timeapres) + '\n'
# print "matrice de cooccurrence construite"
muti = build_mutual_information(p_cooccurrences,p_cooccurrences_ordre1,nb_billets,occurrences,top_concepts_dict)
xhi2val = xhi2(muti)
export_concepts_xhi2(xhi2val,p_cooccurrences,p_cooccurrences_ordre1,dico_termes,dico_lemmes,year,occurrences)
#
# pool_size = int(multiprocessing.cpu_count())
# pool = multiprocessing.Pool(processes=pool_size)
# print years
# pool.map(xhi2_comp, years)
for year in years:
xhi2_comp(year)
fusion_years.fusion('conceptsxhi2')